code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCAmelCase ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ArgumentPars... | 26 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 1 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowercase :Optional[int] = logging.get_logge... | 26 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 1 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, i... | 26 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def UpperCAmelCase ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 1 |
class _a :
"""simple docstring"""
def __init__( self : List[str] ) ->Optional[int]:
SCREAMING_SNAKE_CASE__ : Tuple = 0
SCREAMING_SNAKE_CASE__ : Tuple = 0
SCREAMING_SNAKE_CASE__ : Any = {}
... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def UpperCAmelCase ( _lo... | 26 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase :List[str] ... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 1 |
from __future__ import annotations
class _a :
"""simple docstring"""
def __init__( self : Dict , a : str , a : str ) ->Optional[Any]:
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ : Tuple = text, pattern
... | 26 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square(_lowerCamelCase : int , _lowerCamelCase : int ) -> int:
# ... | 26 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 1 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion impo... | 26 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 26 | 1 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposit... | 26 | 1 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 1 |
from __future__ import annotations
import math
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : bool , _lowerCamelCase : list[int] , _lowerCamelCase : float ):
'''simple docstring'''
... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 | 1 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( _lowerCamelCase : Tuple , _lowerCamelCase : Union[str, Any] , _lower... | 26 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 26 | 1 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 26 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
... | 26 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 26 |
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,
FEATUR... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase :Optional[int] = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
... | 26 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, Path... | 26 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 26 | 1 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCAmelCase ... | 26 |
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 ...tokeniz... | 26 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__lowercase :Any = ""
__lowercase :Optional[Any] = ""
__lowercase :Tuple = ""
__lowercase :List[Any] = 1 # (0 is vertical, 1 is horizontal)
def UpperCAmelCas... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowercase :Union[str, Any] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generati... | 26 |
import unittest
from typing import Dict, List, Optional, Union
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... | 26 | 1 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
fr... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 | 1 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__lowercase :Any = logging.get_logger(__name__)
class _a ( lowercase__ ):
"""simple docst... | 26 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : str , _lowerCamelCase : int ):
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(_lowerCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
... | 26 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 1 |
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... | 26 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() an... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 1 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_v... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__lowercase :Optional[int] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_P... | 26 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from .... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 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,
FEATUR... | 26 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _a :
"""simple docstring"""
snake_case_ = None
def A_ ( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : i... | 26 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 1 |
from collections.abc import Iterable
from typing import Any
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , a : int | None = None ) ->str:
SCREAMING_SNAKE_CASE__ : Dict = value
SCREAMING_SNAK... | 26 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 26 | 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 ...tokeniz... | 26 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposit... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int = 10 , _lowerCamelCase : int = 1_000 , _lowerCamelCase : bool = True ):
'''simple docstring'''
assert (
isinstance(_lowerCamelCase , _lowerCamelCase )
and isinstance(_lowerCamelC... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase :List[str] = logging.get_logger(__name__)
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = "encoder-decoder"
snake_case_... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCAmelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Dict , _lowerCamelCase : Any=1_024... | 26 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : list ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while len(_lowerCamelCase ) > 1:
SCREAMING_SNAKE_CASE__ : str = 0
# Consider two files with minimum ... | 26 |
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,
FEATUR... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1
... | 26 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 1 |
from __future__ import annotations
import numpy as np
def UpperCAmelCase ( _lowerCamelCase : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ : int = np.shape(_lowerCamelCase )
if rows != columns:
... | 26 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 |
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 ...tokeniz... | 26 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import T... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = HfArgumentParser(_lowerCamelCase )
SCREAMING_SNAKE_CASE__ : Any = ... | 26 |
import unittest
from typing import Dict, List, Optional, Union
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... | 26 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
__lowercase :Dict = datasets.logging.get_logger(__name__)
__lowercase :Union[str, Any] = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def UpperCAmelCase ( _lowerCamelCase : int = 5_000 ):
... | 26 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( lowercase__ , unittest.TestCase ):
"""simple docstring"""
snake_... | 26 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 1 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__lowercase :Optional[int] = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["export... | 26 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowercase :Tuple = {
"configuration_clip": [
"CLIP_... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__l... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCAmelCase ( _lowerCamelCase : str , _lowerCamelCase : complex , _lowerCamelCase : str = "x" , _lowerCamelCase : float = 10**-10 , _lowerCamelCase : in... | 26 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self : List[Any] ) ->int:
debug_launche... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokeniz... | 26 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 1 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.mod... | 26 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposit... | 26 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncode... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import loggi... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 | 1 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import requir... | 26 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 26 | 1 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _a ( lowercase__ , unittest.TestCase ):
... | 26 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
... | 26 | 1 |
import numpy as np
def UpperCAmelCase ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : str = 1E-12 , _lowerCamelCase : Any = 100 , ):
'''simple docstring'''
assert np.shape(SCREAMING_SNAKE_CASE_ )... | 700 |
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,
FEATUR... | 26 | 0 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
__lowercase :Any = logging.getLogger(__name__)
__lowercase :List[str] = 50 ... | 701 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 0 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 702 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 26 | 0 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tens... | 703 |
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 ...tokeniz... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return str(_lowercase ) == str(_lowercase )[::-1]
def UpperCAmelCase ( _lowerCamelCase : List[Any] ):
'''simple docstring'''
retur... | 704 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_availab... | 705 |
import unittest
from typing import Dict, List, Optional, Union
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... | 26 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird imp... | 706 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase :int = logging.get_logger(__name__)
__lowercase :int = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class _... | 707 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 0 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetY... | 708 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : int ):
'''simple docstring'''
if length <= 0 or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(__l... | 709 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowercase :str = ... | 710 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Co... | 711 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__lowercase :Any = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": 1_000,
... | 712 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 0 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__lowercase = ... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 0 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__lowercase :Optional[Any] = numpy.array([0, 0])
__lowercase :Optional[Any] = numpy.array([0.5, 0.8_6_6_0_2_5_4])
__lowercase :Any = numpy.a... | 714 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutpu... | 715 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 0 |
'''simple docstring'''
import argparse
import struct
import unittest
class _a :
"""simple docstring"""
def __init__( self : List[str] , a : int ) ->Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] = data
# Initiali... | 716 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
... | 717 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposit... | 26 | 0 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def UpperCAmelCase ( _lowerCamelCase : str , _lowerCamelCase : float | Decimal , _lowerCamelCase : float = 10**-10 ):
'''simple d... | 718 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_for... | 719 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase :List[Any] = logging.get_logger(__name__)
__lowercase :List[str] = {
"""google/pix2struct-textcaps-base""": (
"""https://huggingface... | 720 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 26 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase :Optional[Any] = logging.get_logger(__name__)
__lowercase :Union[str, Any] = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
'''https://huggingface.c... | 721 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
... | 26 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__lowercase :str =... | 700 |
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,
FEATUR... | 26 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase :Optional[Any] = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}
tr... | 701 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 0 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["d... | 702 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 26 | 0 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _a ( UpperCamelCase_ ):
""... | 703 |
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 ...tokeniz... | 26 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__lowercase :List[str] = logging.get_logger(__name__)
__l... | 704 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
... | 705 |
import unittest
from typing import Dict, List, Optional, Union
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... | 26 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaD... | 706 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 | 0 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase :Tuple = [
"word_embeddings_layernorm.we... | 707 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 0 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__lowercase :Optional[Any] = False
__lowercase :List[Any] = True
__lowercase :List[Any] = False
if __name__ == "__mai... | 708 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 0 |
import baseaa
def UpperCAmelCase ( _lowerCamelCase : str ):
'''simple docstring'''
return baseaa.baaencode(string.encode("utf-8" ) )
def UpperCAmelCase ( _lowerCamelCase : bytes ):
'''simple docstring'''
... | 709 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposi... | 710 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 0 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowercase :Union[str, Any] = ... | 711 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert i... | 712 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 0 |
import requests
from bsa import BeautifulSoup
def UpperCAmelCase ( _lowerCamelCase : List[Any] = "AAPL" ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
SCREAMING_SNAK... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : Tuple ):
'''simple docstring'''
while b:
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ : int = b, a % b
return a
def UpperCAmelCase (... | 714 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__lowercase :Tuple = logging.get_logger(__name__)
__lowercase ... | 715 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transforme... | 716 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 26 | 0 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...uti... | 717 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposit... | 26 | 0 |
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... | 718 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 0 |
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