code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Tuple ... | 21 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
... | 21 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 21 |
import random
from typing import Any
def UpperCamelCase_( lowerCamelCase_ ) -> list[Any]:
for _ in range(len(lowerCamelCase_ ) ):
_lowercase : Optional[int] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
_lowercase : str = random... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ = 100_0000 ) -> int:
_lowercase : Optional[int] = limit + 1
_lowercase : str = [0] * limit
for first_term in range(1 , lowerCamelCase_ ):
for n in range(lowerCamelCase_ , lowerCamelCase_ ... | 21 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impor... | 21 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def UpperCamelCase_( lowerCamelCase_ ) -... | 21 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers im... | 21 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@requir... | 21 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE : Any = logging.get_logg... | 21 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE : Dict = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["Bi... | 21 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not numbers:
return 0
if not isinstance(lowerCamelCase_ , (list, tuple) ) or not all(
isinstance(lowerCamelCase_ , lowerCamelCase_ ) for number in numbers ):
raise ValueError('numbers must be an iterable o... | 21 | 1 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance < 0:
rais... | 21 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
SCREAMING_SNAKE_CASE : Tuple = (3, 9, -11, 0, 7, 5, 1, -1)
SCREAMING_SNAKE_CASE : Union[str, Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowe... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ ) -> float:
_lowercase : Dict = 0
while len(lowerCamelCase_ ) > 1:
_lowercase : Dict = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
_lowercase :... | 21 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 21 | 1 |
import math
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 21 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 21 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 21 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> float:
_lowercase : Tuple = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCamelCase_( ) ... | 21 | 1 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedT... | 21 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 21 | 1 |
import torch
def UpperCamelCase_( ) -> List[str]:
if torch.cuda.is_available():
_lowercase : Optional[int] = torch.cuda.device_count()
else:
_lowercase : int = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ ==... | 21 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowerCamelCase( unittest.TestCase ):
lowercase_ : Dict = JukeboxTokenizer
lowercase_ : Dict = {
"""artist""": """Zac Brown Band""",
"""ge... | 21 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE : Any = {
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
"configuratio... | 21 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 21 | 1 |
import functools
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> int:
# Validation
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or not all(isinstance(lowerCamelCase_ , lowerCamelCase_ ) for day in days ):
raise ValueError('The parameter days... | 21 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 21 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _lowerCamelCase( _a ... | 21 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"SenseTime/deformable-detr": "https://huggi... | 21 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
f... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : List[str] = {
"configuration_speech_to_text": ["SPEE... | 21 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any ... | 21 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 21 | 1 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=1337 , num_examples=42 , dataset_name='my_dataset' )... | 21 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import en... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
_lowercase : str = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def UpperCamelCase_( lowerCamelCase_ = 5000 ) -> int:
_lowercase : Optional[Any] = [(i * (3 * i - 1)) // 2 f... | 21 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
_lowercase : int = int(number**0.5 )
return number == sq * sq
def UpperCamelCase_( lowerCamelCase_ , lowerCam... | 21 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_uti... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 21 | 1 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
_lowercase : int = int(number**0.5 )
return number == sq * sq
def UpperCamelCase_( lowerCamelCase_ , lowerCam... | 21 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater tha... | 21 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 21 |
from __future__ import annotations
from math import ceil, floor, sqrt
def UpperCamelCase_( lowerCamelCase_ = 200_0000 ) -> int:
_lowercase : list[int] = [0]
_lowercase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle... | 21 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
SCREAMING_SNAKE_CASE : Tuple = (3, 9, -11, 0, 7, 5, 1, -1)
SCREAMING_SNAKE_CASE : Union[str, Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowe... | 21 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
... | 21 | 1 |
from manim import *
class _lowerCamelCase( _a ):
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Union[str, Any] = Rectangle(height=0.5, width=0.5)
_lowercase : List[Any] ... | 21 |
import random
from typing import Any
def UpperCamelCase_( lowerCamelCase_ ) -> list[Any]:
for _ in range(len(lowerCamelCase_ ) ):
_lowercase : Optional[int] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
_lowercase : str = random... | 21 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"microsoft/xprophetnet-large-wiki100-c... | 21 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impor... | 21 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _l... | 21 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers im... | 21 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_re... | 21 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE : Any = logging.get_logg... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise TypeError('only integers accepted as input' )
else:
_lowercase : Any = str(abs(lowerCamelCase_ ) )
_lowercase : Dict ... | 21 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not numbers:
return 0
if not isinstance(lowerCamelCase_ , (list, tuple) ) or not all(
isinstance(lowerCamelCase_ , lowerCamelCase_ ) for number in numbers ):
raise ValueError('numbers must be an iterable o... | 21 | 1 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
... | 21 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
SCREAMING_SNAKE_CASE : Tuple = (3, 9, -11, 0, 7, 5, 1, -1)
SCREAMING_SNAKE_CASE : Union[str, Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowe... | 21 | 1 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
def UpperCamelCase_( lowerCamelCase_ ) -> List[int]:
if isinstance(lowerCamelCase_ ... | 21 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 21 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def UpperCamelCase_( ) -> Any:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with ... | 21 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 21 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
SCREAMING_SNAKE_CASE : Optional[int] = "\nimport os\n"
SCREAMING_SNAKE_CASE : str = "\ndef foo():\n import os\n return False\n"
SCREAMING_SNAKE_CASE : str = ... | 21 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> float:
_lowercase : Tuple = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCamelCase_( ) ... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> List[str]:
_enforce_args(lowerCamelCase_ , lowerCamelCase_ )
if n == 0:
return 0
_lowercase : Union[str, Any] = float('-inf' )
for i in range(1 , n + 1 ):
_lowercase ... | 21 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 21 | 1 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
SCREAMING_SNAKE_CASE : Optional[int] = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n a... | 21 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowerCamelCase( unittest.TestCase ):
lowercase_ : Dict = JukeboxTokenizer
lowercase_ : Dict = {
"""artist""": """Zac Brown Band""",
"""ge... | 21 | 1 |
# flake8: noqa
# Lint as: python3
SCREAMING_SNAKE_CASE : int = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar... | 21 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 21 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:... | 21 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 21 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 21 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"SenseTime/deformable-detr": "https://huggi... | 21 | 1 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
SCREAMING_SNAKE_CASE : Tuple ... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : List[str] = {
"configuration_speech_to_text": ["SPEE... | 21 | 1 |
from sklearn.metrics import recall_score
import datasets
SCREAMING_SNAKE_CASE : Optional[int] = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positi... | 21 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 21 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[Any] = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingfac... | 21 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import en... | 21 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 21 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
_lowercase : int = int(number**0.5 )
return number == sq * sq
def UpperCamelCase_( lowerCamelCase_ , lowerCam... | 21 | 1 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase_( lowerCamelCase_ ) -> int:
return x + 2
class _lowerCamelCase( unittest.TestCase ):
def UpperCamelCase ( self) ... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 21 | 1 |
from __future__ import annotations
import time
import numpy as np
SCREAMING_SNAKE_CASE : Optional[Any] = [8, 5, 9, 7]
SCREAMING_SNAKE_CASE : int = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
SCREAMING_SNAKE_CASE : ... | 21 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater tha... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ ) -> list[int]:
if num <= 0:
raise ValueError('Input must be a positive integer' )
_lowercase : str = [True] * (num + 1)
_lowercase : str = 2
while p * p <= num:
if primes[p]:
... | 21 |
from __future__ import annotations
from math import ceil, floor, sqrt
def UpperCamelCase_( lowerCamelCase_ = 200_0000 ) -> int:
_lowercase : list[int] = [0]
_lowercase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle... | 21 | 1 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, ra... | 21 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> int:
while second != 0:
_lowercase : Tuple = first & second
first ^= second
_lowercase : Tuple = c << 1
return first
if __name__ == "__main__":
import doctest
... | 21 |
import random
from typing import Any
def UpperCamelCase_( lowerCamelCase_ ) -> list[Any]:
for _ in range(len(lowerCamelCase_ ) ):
_lowercase : Optional[int] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
_lowercase : str = random... | 21 | 1 |
import os
import numpy
import onnx
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Any:
_lowercase : Optional[Any] = a.name
_lowercase : Dict = b.name
_lowercase : List[str] = ''
_lowercase : ... | 21 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impor... | 21 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def UpperCamelCase_( lowerCamelCase_ ) -> str:
return "".join(sorted(lowerCamelCase_ ) )
def UpperCamelCase_( lowerCamelCase_ ) -> list[str]:
return word_by_signature[signatur... | 21 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers im... | 21 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, tor... | 21 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE : Any = logging.get_logg... | 21 | 1 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
... | 21 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not numbers:
return 0
if not isinstance(lowerCamelCase_ , (list, tuple) ) or not all(
isinstance(lowerCamelCase_ , lowerCamelCase_ ) for number in numbers ):
raise ValueError('numbers must be an iterable o... | 21 | 1 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 21 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
SCREAMING_SNAKE_CASE : Tuple = (3, 9, -11, 0, 7, 5, 1, -1)
SCREAMING_SNAKE_CASE : Union[str, Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowe... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Any:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCamelCase_ , int(b / 2 ) ) * actual_power(lowerCamelCase_ , int(b / 2 ) )
else:
return a * actual_power(lowerCamelCase_... | 21 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 21 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : List[str] = {
"configuration_speech_to_text": ["SPEE... | 21 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 21 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCond... | 21 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> float:
_lowercase : Tuple = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCamelCase_( ) ... | 21 | 1 |
from ...processing_utils import ProcessorMixin
class _lowerCamelCase( _a ):
lowercase_ : List[Any] = """WhisperFeatureExtractor"""
lowercase_ : List[str] = """WhisperTokenizer"""
def __init__( self, lowerCamelCase, lowerCamelCase) -> Dict:
... | 21 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 21 | 1 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ... | 21 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowerCamelCase( unittest.TestCase ):
lowercase_ : Dict = JukeboxTokenizer
lowercase_ : Dict = {
"""artist""": """Zac Brown Band""",
"""ge... | 21 | 1 |
import colorsys
from PIL import Image # type: ignore
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> float:
_lowercase : str = x
_lowercase : int = y
for step in range(lowerCamelCase_ ): # noqa: B007
... | 21 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 21 | 1 |
from __future__ import annotations
import requests
def UpperCamelCase_( lowerCamelCase_ ) -> dict:
_lowercase : List[Any] = F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(lowerCamelCase_ ).json()
def UpperCame... | 21 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 21 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"SenseTime/deformable-detr": "https://huggi... | 21 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"SenseTime/deformable-detr": "https://huggi... | 21 | 1 |
import re
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
_lowercase : str = re.compile(
R'^(?:0|94|\+94|0{2}94)' R'7(0|1|2|4|5|6|7|8)' R'(-| |)' R'\d{7}$' )
return bool(re.search(lowerCamelCase_ , lowerCamelCase_ ) )
if __name__ == "__main__":
SCREAM... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : List[str] = {
"configuration_speech_to_text": ["SPEE... | 21 | 1 |
import numpy as np
import qiskit
def UpperCamelCase_( lowerCamelCase_ = 8 , lowerCamelCase_ = None ) -> str:
_lowercase : int = np.random.default_rng(seed=lowerCamelCase_ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we nee... | 21 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 21 | 1 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class _lowerCamelCase( nn.Module ):
lowercase_ : int
lowercase_ : int
lowercase_ : float = 0.0
... | 21 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import en... | 21 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inter... | 21 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
_lowercase : int = int(number**0.5 )
return number == sq * sq
def UpperCamelCase_( lowerCamelCase_ , lowerCam... | 21 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 21 | 1 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_tor... | 21 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater tha... | 21 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"configuration_electra": ["ELECTRA_PR... | 21 |
from __future__ import annotations
from math import ceil, floor, sqrt
def UpperCamelCase_( lowerCamelCase_ = 200_0000 ) -> int:
_lowercase : list[int] = [0]
_lowercase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle... | 21 | 1 |
SCREAMING_SNAKE_CASE : dict[tuple[int, int, int], int] = {}
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 ... | 21 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
... | 21 | 1 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_E... | 21 |
import random
from typing import Any
def UpperCamelCase_( lowerCamelCase_ ) -> list[Any]:
for _ in range(len(lowerCamelCase_ ) ):
_lowercase : Optional[int] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
_lowercase : str = random... | 21 | 1 |
import mpmath # for roots of unity
import numpy as np
class _lowerCamelCase:
def __init__( self, lowerCamelCase=None, lowerCamelCase=None) -> Optional[int]:
"""simple docstring"""
_lowercase : Optional[int] = list(poly_a or [0])[:]
... | 21 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impor... | 21 | 1 |
import os
from collections.abc import Iterator
def UpperCamelCase_( lowerCamelCase_ = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(lowerCamelCase_ ):
_lowercase : Union[str, Any] = [d for d in dir_names if d != 'scripts' and d[0] not in '._... | 21 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers im... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ = 1000 ) -> int:
_lowercase : Tuple = -1
_lowercase : List[str] = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowercase : ... | 21 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE : Any = logging.get_logg... | 21 | 1 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def UpperCamelCase_( lowerCamelCase_ ) -> float:
return np.dot(lowerCamelCase_ , lowerCamelCase_ )
class _lowerCamelCase:
def __init__( self, *,
lo... | 21 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not numbers:
return 0
if not isinstance(lowerCamelCase_ , (list, tuple) ) or not all(
isinstance(lowerCamelCase_ , lowerCamelCase_ ) for number in numbers ):
raise ValueError('numbers must be an iterable o... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ ) -> list:
def merge(lowerCamelCase_ , lowerCamelCase_ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
... | 21 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
SCREAMING_SNAKE_CASE : Tuple = (3, 9, -11, 0, 7, 5, 1, -1)
SCREAMING_SNAKE_CASE : Union[str, Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowe... | 21 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowerCamelCase( _a ):
lowercase_ : Tuple = (UniPCMult... | 21 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 21 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union[str, Any] ... | 21 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 21 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set... | 21 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> float:
_lowercase : Tuple = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCamelCase_( ) ... | 21 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 21 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 21 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAK... | 21 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowerCamelCase( unittest.TestCase ):
lowercase_ : Dict = JukeboxTokenizer
lowercase_ : Dict = {
"""artist""": """Zac Brown Band""",
"""ge... | 21 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataC... | 21 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 21 | 1 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchma... | 21 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 21 | 1 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
requir... | 21 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"SenseTime/deformable-detr": "https://huggi... | 21 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acc... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : List[str] = {
"configuration_speech_to_text": ["SPEE... | 21 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : ... | 21 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 21 | 1 |
class _lowerCamelCase:
def __init__( self) -> Dict:
"""simple docstring"""
_lowercase : Union[str, Any] = {}
def UpperCamelCase ( self) -> None:
"""simple docstring"""
print(self.vertex)
... | 21 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import en... | 21 | 1 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class _lowerCamelCase:
lowercase_ : Optional[Union[str, Path]] = None
lowercase_ : bool = False
lowercase_ : bool = False
lowercase_ : bool ... | 21 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
_lowercase : int = int(number**0.5 )
return number == sq * sq
def UpperCamelCase_( lowerCamelCase_ , lowerCam... | 21 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
re... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 21 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import T... | 21 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater tha... | 21 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 21 |
from __future__ import annotations
from math import ceil, floor, sqrt
def UpperCamelCase_( lowerCamelCase_ = 200_0000 ) -> int:
_lowercase : list[int] = [0]
_lowercase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle... | 21 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
... | 21 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
... | 21 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.ut... | 21 |
import random
from typing import Any
def UpperCamelCase_( lowerCamelCase_ ) -> list[Any]:
for _ in range(len(lowerCamelCase_ ) ):
_lowercase : Optional[int] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
_lowercase : str = random... | 21 | 1 |
import random
from typing import Any
def UpperCamelCase_( lowerCamelCase_ ) -> list[Any]:
for _ in range(len(lowerCamelCase_ ) ):
_lowercase : Optional[int] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
_lowercase : str = random... | 21 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impor... | 21 | 1 |
from torch import nn
class _lowerCamelCase( nn.Module ):
def __init__( self, lowerCamelCase, lowerCamelCase) -> Dict:
"""simple docstring"""
super().__init__()
_lowercase : List[str] = class_size
_lowercase... | 21 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers im... | 21 | 1 |
from math import loga
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise TypeError('Input value must be a \'int\' type' )
return 0 if (a =... | 21 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE : Any = logging.get_logg... | 21 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
... | 21 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not numbers:
return 0
if not isinstance(lowerCamelCase_ , (list, tuple) ) or not all(
isinstance(lowerCamelCase_ , lowerCamelCase_ ) for number in numbers ):
raise ValueError('numbers must be an iterable o... | 21 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@requ... | 21 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
SCREAMING_SNAKE_CASE : Tuple = (3, 9, -11, 0, 7, 5, 1, -1)
SCREAMING_SNAKE_CASE : Union[str, Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowe... | 21 | 1 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not numbers:
return 0
if not isinstance(lowerCamelCase_ , (list, tuple) ) or not all(
isinstance(lowerCamelCase_ , lowerCamelCase_ ) for number in numbers ):
raise ValueError('numbers must be an iterable o... | 21 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 21 | 1 |
from ..utils import DummyObject, requires_backends
class _lowerCamelCase( metaclass=_a ):
lowercase_ : Optional[int] = ["""torch"""]
def __init__( self, *lowerCamelCase, **lowerCamelCase) -> Any:
"""simple docstring"""
requires_backend... | 21 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 21 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[Any] = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingf... | 21 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> float:
_lowercase : Tuple = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCamelCase_( ) ... | 21 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def UpperCamelCase_( lower... | 21 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 21 | 1 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 21 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowerCamelCase( unittest.TestCase ):
lowercase_ : Dict = JukeboxTokenizer
lowercase_ : Dict = {
"""artist""": """Zac Brown Band""",
"""ge... | 21 | 1 |
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