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 |
|---|---|---|---|---|
'''simple docstring'''
import math
import sys
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> int:
if number != int(UpperCamelCase ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0... | 41 |
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 | 0 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrat... | 42 |
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 | 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 = logging.getLogger(__name__)
__lowercase = 50 # max width of layer nam... | 43 |
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 | 0 |
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokeni... | 44 |
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 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
__a = set()
# Replace all the whitespace in our sentence
__a = input_str.replace(''' ''' , '''''' )
for a... | 45 |
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 | 0 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class lowercase ( _UpperCAmelCase ):
def __init__( self , *lowercase , **lowercase ) -> Optional[int]:
super().__init__(*lowercase , **lowerc... | 46 |
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 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase : List[Any] = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["T... | 47 |
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 | 0 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def __init__( ... | 48 |
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 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogits... | 49 |
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 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acce... | 50 |
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 | 0 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_... | 51 |
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 | 0 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...... | 52 |
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 | 0 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a__ : Optional[Any] =logging.get_logger(__name__)
a__ : str =... | 53 |
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 | 0 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
a__ : Dict = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they s... | 54 |
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 | 0 |
'''simple docstring'''
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 BaseTransformersCLIComma... | 55 |
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 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
a : Any = logging.get_logger(__name__... | 56 |
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 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _UpperCamelCase :
'''simple docstring'''
__UpperCAmelCase : int
__UpperCAmel... | 57 |
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 | 0 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
... | 58 |
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 | 0 |
from __future__ import annotations
from typing import Any
class UpperCAmelCase :
def __init__(self : str , snake_case__ : int ) -> None:
'''simple docstring'''
snake_case : str = num_of_nodes
snake_case :... | 59 |
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 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : list ):
_validate_point(_snake_case )
_validate_point(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
... | 60 |
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 | 0 |
"""simple docstring"""
from __future__ import annotations
_a = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class A_ :
'''simple docstring'''
def __init__( self , lo... | 61 |
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 | 0 |
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_va,
... | 62 |
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 | 0 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCAmelCase_ : int = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerro... | 63 |
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 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control th... | 64 |
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 | 0 |
import warnings
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
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ ... | 65 |
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 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@sl... | 66 |
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 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def __lowerCAmelCase ( UpperCamelCase__ ) -> int:
__lowerCamelCase = SwinConfig(image_size=1_92 )
if "base" i... | 67 |
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 | 0 |
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dime... | 68 |
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 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 69 |
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 | 0 |
'''simple docstring'''
import os
import string
import sys
A__ : str =1 << 8
A__ : Optional[int] ={
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'... | 70 |
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 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 71 |
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 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSe... | 72 |
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 | 0 |
from math import ceil
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ = 1_0_0_1 ) -> int:
__lowerCamelCase : Tuple = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__lowerCamelCase : Any = 2 * i + 1
__lowerCamelCase : Tuple ... | 73 |
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 | 0 |
"""simple docstring"""
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name_... | 74 |
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 | 0 |
'''simple docstring'''
import os
def a_ ( ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =os.path.join(os.path.dirname(__snake_case ) , '''num.txt''' )
with open(__snake_case ) as file_hand:
return str(sum(i... | 75 |
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 | 0 |
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_... | 76 |
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 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_... | 77 |
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 | 0 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
UpperCAmelCase = [0] * no_of_processes
UpperCAmelCase = [0] * no_of_processes
# Copy the burst time i... | 78 |
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 | 0 |
'''simple docstring'''
def __lowercase ( __lowercase , __lowercase = False ) -> str:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_A = F'''Expected string as input, found {type(__lowercase )}'''
... | 79 |
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 | 0 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CH... | 80 |
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 | 0 |
"""simple docstring"""
import argparse
lowerCamelCase_ : int = """docs/source/_static/js/custom.js"""
def _A ( lowercase ):
"""simple docstring"""
with open(lowercase , encoding='''utf-8''' , newline='''\n''' ) as f:
... | 81 |
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 | 0 |
from __future__ import annotations
import math
A__ = """2020.9.26"""
A__ = """xcodz-dot, cclaus, dhruvmanila"""
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if not all(isinst... | 82 |
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 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAU... | 83 |
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 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :list[list[str]] = [[] for _ in range(lowercase__ )]
lowerCAmelCase_ :Optional[Any] = key - 1
... | 84 |
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 | 0 |
'''simple docstring'''
def UpperCamelCase_( snake_case : int ):
'''simple docstring'''
snake_case_ = len(snake_case )
while cur > 1:
# Find the maximum number in arr
snake_case_ = arr.index(max(arr[0:cur] ... | 85 |
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 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
__lowerCAmelCase , __lowerCAmelCase ... | 86 |
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 | 0 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
UpperCamelCase = namedtuple(
'''_TestCommandArgs''',
[
... | 87 |
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 | 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_barth... | 88 |
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 | 0 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
_a : Optional[int] = str(bin(lowerCAmelCase_ ) )
binary_number += ... | 89 |
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 | 0 |
from math import pi, sqrt
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError('math domain error' )
if num > 1_71.5:
raise OverflowError('math range e... | 90 |
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 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LIC... | 91 |
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 | 0 |
from __future__ import annotations
UpperCamelCase__ = 1.6021E-19 # units = C
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ):
if (conductivity, electron_conc, mobilit... | 92 |
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 | 0 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowercase : Union[str, Any] = "Usage of script: script_name <size_of_canvas:int>"
_lowercase : Union[str, Any] = ... | 93 |
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 | 0 |
def __lowerCamelCase ( UpperCAmelCase_ : int = 1000 ):
"""simple docstring"""
a , a :int = 1, 1
a :Any = 2
while True:
a :Optional[int] = 0
a :str = fa + fa
... | 94 |
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 | 0 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCamelCase__):
def __init__( self , *... | 95 |
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 | 0 |
"""simple docstring"""
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = [
'decoder.version',
... | 96 |
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 | 0 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( A__ ):
""... | 97 |
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 | 0 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import ... | 98 |
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 | 0 |
import gc
import unittest
from transformers import CTRLConfig, 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 ModelTes... | 99 |
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 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | 100 |
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 | 0 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import... | 101 |
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 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, r... | 102 |
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 | 0 |
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 toke... | 103 |
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 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase_ ... | 104 |
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 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
a : str = logging.get... | 105 |
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 | 0 |
"""simple docstring"""
import cva
import numpy as np
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : str ,lowercase_ : float ,lowercase_ : int ):
if k in (0.04, 0.06):
lowerCAmelCase__ : Optional[Any] = k... | 106 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : List[str] = {'processing_layoutxlm': ['LayoutXLMProcessor'... | 107 |
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 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowerCAmelCase__ = (3, 9, -11, 0, 7, 5, 1, -1)
lowerCAmelCase__ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class SCREAMING_SNAKE_CASE__ :
... | 108 |
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 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _snake_case ( UpperCamelCase : int = 1000000 , UpperCamelCase : int = 10 ):
UpperCAmelCase : defaultdict = defaultdict(UpperCamelCase )
for outer_width in range(3 , (t_limit... | 109 |
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 | 0 |
import os
from math import logaa
def _a ( SCREAMING_SNAKE_CASE = "base_exp.txt" ):
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE ) , SCREAMING_... | 110 |
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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Optiona... | 126 |
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 | 0 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewT... | 343 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase : Dict = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokeni... | 280 |
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 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
SCREAMING_SNAKE_CASE_: int =str(bin(lowerCamelCase_ ) )[2:] # remove the leading "0b"
SC... | 173 |
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 | 0 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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 ImageProcessin... | 209 |
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 | 0 |
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... | 29 |
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 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
_enforce_args(lowerCamelCase_ , lowerCamelCase_ )
if n == 0:
return 0
lowerCAmelCase__ : Union[str, Any] = float('-inf' )
for i in range(1 , ... | 212 |
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 | 0 |
# flake8: noqa
# Lint as: python3
lowerCAmelCase__ :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, enable_progre... | 329 |
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 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from t... | 187 |
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 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, ... | 97 |
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 | 0 |
"""simple docstring"""
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... | 293 |
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 | 0 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mode... | 126 |
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 | 0 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_SCREAMING_SNAKE_CASE = "src/transformers"
_SCREAMING_SNA... | 343 |
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 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _SCREAMING_SNAKE_CASE ( ) -> Any:
__A : Dict = ArgumentParser(
description=(
'PyTorch TPU... | 280 |
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 | 0 |
"""simple docstring"""
from math import factorial
_UpperCAmelCase = {str(d): factorial(d) for d in range(1_0)}
def __magic_name__ ( lowercase ):
return sum(DIGIT_FACTORIAL[d] for d in str(lowerCamelCase_ ) )
def __magic_name__ ( ):
SCREAMIN... | 173 |
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 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 209 |
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 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCamelCase :
'''simple docstring'''
def __init__( self , _UpperCamelCase ) -> Tuple:
UpperCAmelCase_ : Optional[Any] = li... | 29 |
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 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassifica... | 212 |
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 | 0 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase__ ( a__: Tuple ) -> float:
'''simple docstring'''
return np.dot(lowerCamelCase_ , lowerCamelCase_ )
class __a :
def __init__( self... | 329 |
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 | 0 |
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 .... | 187 |
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 | 0 |
'''simple docstring'''
class lowercase :
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = {}
def lowerCAmelCase__ ( self ):
'''simple docstring'''
print(self.vertex )
... | 97 |
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 | 0 |
"""simple docstring"""
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
__A ... | 293 |
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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase = {
"configuration_llama": ["LLAMA_PRETRAINED_CO... | 126 |
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 | 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_tensor, ra... | 343 |
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 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _SCREAMING_SNAKE_CASE ( a , a , a , a , ) -> list[float]:
__A : Union[str, Any] = coefficient_matrix.shape
__A : ... | 280 |
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 | 0 |
"""simple docstring"""
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 impor... | 173 |
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 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case = None ,__snake_case = None ,_... | 209 |
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 | 0 |
def lowercase__ ( __snake_case : List[Any] = 100 ):
'''simple docstring'''
UpperCAmelCase_ : int = set()
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : int = n + 1 # maximum li... | 29 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 212 |
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 | 0 |
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... | 329 |
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 | 0 |
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