code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class SCREAMING_SNAKE_CASE_ ( __SCREAMING_SNAKE_CASE )... | 155 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Any =["speech"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["speech"] )
class _s... | 71 | 0 |
'''simple docstring'''
from maths.prime_check import is_prime
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
snake_case: Any = f"""Input value of [number={number}]... | 329 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ) -> tuple[float, float]:
"""simple docstring"""
if not len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) == 3:
raise ValueError("Please... | 71 | 0 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentPars... | 637 |
'''simple docstring'''
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 3 ) -> list:
"""simple docstring"""
UpperCAmelCase_ : Dict = min(_SCREAMING_SNAKE_CASE )
UpperCAmelCase... | 71 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class __lowercase :
def __init__(self ):
lowerCamelCase_ : Union[str, Any] = {}
def UpperCAmelCase__ (self , A ):
lowerCamelCase_ :... | 422 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, s... | 71 | 0 |
'''simple docstring'''
def __lowercase ( __lowercase , __lowercase ) -> str:
'''simple docstring'''
_A = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
_A = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative... | 330 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = 2
UpperCAmelCase_ : Optional[int] = []
while i * i <= n:
... | 71 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
... | 5 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowerCamelCase = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Transl... | 71 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __a ( SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
def is_in_circle(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:... | 303 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
Auto... | 71 | 0 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar("""T""")
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase : Any ) ... | 651 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_... | 71 | 0 |
from __future__ import annotations
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : int ) -> list[int]:
_lowercase = 0
_lowercase = len(_SCREAMING_SNAKE_CASE ) - 1
while i < j:
if nums[i] + nums... | 287 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def a__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) ... | 71 | 0 |
'''simple docstring'''
def _snake_case ( A_ : int ):
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError("""Input value must be an 'int' type""" )
a_ : Union[str, Any] = 0
while number:
po... | 577 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _snake_case (nn.Module):
def __init__( self ,_snake_case = 16 ,_snake_case = 88 ,_snake_case = None ,_snake_case = 1 ,_snake... | 71 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_se... | 377 |
'''simple docstring'''
import json
import sys
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int ) -> Tuple:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8" ) as f:
UpperCAmelCase_ : ... | 71 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE_ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@staticmethod
@abstractmethod
def snake_case_ ( lowerCAmelCase__):
raise NotImplementedError()
@... | 155 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase = g... | 71 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils impor... | 329 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError("Input value must be an 'int' type" )
UpperCAmelCase_ : Union[str,... | 71 | 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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)... | 637 |
'''simple docstring'''
from math import factorial
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k" )... | 71 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowercase : Union[str, Any] = logging.get_logger(__name__)
__lowercase ... | 422 |
'''simple docstring'''
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... | 71 | 0 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
lowerCamelCase_ = logging.getLogger(__name__)
if is_torch_tpu... | 330 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_lowerCamelCase = pytest.mark.integration
@pytest.mark... | 71 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test... | 5 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_... | 71 | 0 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Di... | 303 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
Audio... | 71 | 0 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
lowerCamelCase : torch.Tensor # [batch_size x 3]
lowerCamelCase : torch.Tensor # [batch_size x 3]
... | 651 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
"""configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT... | 71 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 287 |
'''simple docstring'''
import heapq
def a__ ( _SCREAMING_SNAKE_CASE : dict ) -> set[int]:
"""simple docstring"""
UpperCAmelCase_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq... | 71 | 0 |
'''simple docstring'''
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 (
TFBaseModelOutputWithNoAt... | 577 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms imp... | 71 | 0 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) ->List[Any]:
UpperCAmel... | 377 |
'''simple docstring'''
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 impo... | 71 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusi... | 155 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Any =["speech"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["speech"] )
class _s... | 71 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class SCREAMING_SNAKE_CASE ( unittest.TestCas... | 329 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ) -> tuple[float, float]:
"""simple docstring"""
if not len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) == 3:
raise ValueError("Please... | 71 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__a : Union[str, Any] = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": 100... | 637 |
'''simple docstring'''
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 3 ) -> list:
"""simple docstring"""
UpperCAmelCase_ : Dict = min(_SCREAMING_SNAKE_CASE )
UpperCAmelCase... | 71 | 0 |
'''simple docstring'''
import math
def lowercase_ ( _lowercase = 100 ) -> int:
'''simple docstring'''
lowerCamelCase_ : Optional[int] = sum(i * i for i in range(1 , n + 1 ) )
lowerCamelCase_ : Optional[Any] = int(math.pow(sum(range(1 , ... | 422 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, s... | 71 | 0 |
'''simple docstring'''
def __lowercase ( __lowercase ) -> int:
'''simple docstring'''
_A = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __lowercase ( __lowercase = 100 ) -> int:
'''simple ... | 330 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = 2
UpperCAmelCase_ : Optional[int] = []
while i * i <= n:
... | 71 | 0 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def A (__lowerCamelCase :bytes ):
if len(_SCREAMING_SNAKE_CASE ) != 32:
raise ValueError("""Input must be of length 32""" )
_lowerCAmelCase = B""
for i in [3, 2, 1, 0]:
... | 5 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowerCamelCase = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Transl... | 71 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqli... | 303 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
Auto... | 71 | 0 |
from typing import Any
def snake_case_ (__A : list , __A : list , __A : dict , __A : dict , __A : dict , ) -> list:
_validation(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE... | 651 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Optional[int] = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not is_torch_available():
r... | 287 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def a__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) ... | 71 | 0 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__snake_case: Dict = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Tra... | 577 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _snake_case (nn.Module):
def __init__( self ,_snake_case = 16 ,_snake_case = 88 ,_snake_case = None ,_snake_case = 1 ,_snake... | 71 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torc... | 377 |
'''simple docstring'''
import json
import sys
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int ) -> Tuple:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8" ) as f:
UpperCAmelCase_ : ... | 71 | 0 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert... | 155 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase = g... | 71 | 0 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Dict , __A : ... | 329 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError("Input value must be an 'int' type" )
UpperCAmelCase_ : Union[str,... | 71 | 0 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __lowercase : list[int] , __lowercase : list[int] , __lowercase : list[int] , __lowercase : list[list[str]] , __lowercase : int , ) -> None:
"""simple d... | 637 |
'''simple docstring'''
from math import factorial
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k" )... | 71 | 0 |
'''simple docstring'''
def lowercase_ ( _lowercase ) -> list:
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return [tuple(_SCREAMING_SNAKE_CASE )]
lowerCamelCase_ : List[str] = []
def generate(_lowercase , _lowercase ):
... | 422 |
'''simple docstring'''
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... | 71 | 0 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCamelCase_ = pytest.mark.integration
@pytest.mark.parametrize("path... | 330 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_lowerCamelCase = pytest.mark.integration
@pytest.mark... | 71 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemant... | 5 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_... | 71 | 0 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class A_ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
... | 303 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
Audio... | 71 | 0 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__UpperCAmelCase ... | 651 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
"""configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT... | 71 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.sch... | 287 |
'''simple docstring'''
import heapq
def a__ ( _SCREAMING_SNAKE_CASE : dict ) -> set[int]:
"""simple docstring"""
UpperCAmelCase_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq... | 71 | 0 |
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__snake_case: Tuple = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Sa... | 577 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms imp... | 71 | 0 |
from math import sqrt
def _UpperCamelCase ( lowerCAmelCase_ ) ->bool:
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
UpperCAmelCase = True
# 0 and 1 are none primes.
if number... | 377 |
'''simple docstring'''
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 impo... | 71 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 155 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Any =["speech"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["speech"] )
class _s... | 71 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", ... | 329 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ) -> tuple[float, float]:
"""simple docstring"""
if not len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) == 3:
raise ValueError("Please... | 71 | 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 t... | 637 |
'''simple docstring'''
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 3 ) -> list:
"""simple docstring"""
UpperCAmelCase_ : Dict = min(_SCREAMING_SNAKE_CASE )
UpperCAmelCase... | 71 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_a... | 422 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, s... | 71 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcesso... | 330 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = 2
UpperCAmelCase_ : Optional[int] = []
while i * i <= n:
... | 71 | 0 |
'''simple docstring'''
from math import factorial
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(_SCREAMING_SNAKE_CASE ) // (factorial(... | 5 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowerCamelCase = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Transl... | 71 | 0 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a__ = ["keras_nlp"]
def __init__(self , *lowercase__ , **lowercase__ ) -> List[Any]:
requires_backends(self , ['''k... | 303 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
Auto... | 71 | 0 |
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase : int ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Union[str, ... | 651 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_... | 71 | 0 |
from __future__ import annotations
from random import choice
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : Any ) -> Union[str, Any]:
return choice(_SCREAMING_SNAKE_CASE )
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : int ) -> ... | 287 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def a__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) ... | 71 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_ava... | 577 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _snake_case (nn.Module):
def __init__( self ,_snake_case = 16 ,_snake_case = 88 ,_snake_case = None ,_snake_case = 1 ,_snake... | 71 | 0 |
import re
def _UpperCamelCase ( lowerCAmelCase_ ) ->str:
if len(re.findall("""[ATCG]""" , _SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" , """TAGC""" ) )
if __name__ =... | 377 |
'''simple docstring'''
import json
import sys
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int ) -> Tuple:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8" ) as f:
UpperCAmelCase_ : ... | 71 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 155 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase = g... | 71 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.mo... | 329 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError("Input value must be an 'int' type" )
UpperCAmelCase_ : Union[str,... | 71 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
fr... | 637 |
'''simple docstring'''
from math import factorial
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k" )... | 71 | 0 |
'''simple docstring'''
class __lowercase :
def __init__(self , A = "" , A = False ):
# Mapping from the first character of the prefix of the node
lowerCamelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowe... | 422 |
'''simple docstring'''
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... | 71 | 0 |
'''simple docstring'''
def __lowercase ( __lowercase , __lowercase ) -> int:
'''simple docstring'''
return abs(_SCREAMING_SNAKE_CASE ) if a == 0 else greatest_common_divisor(b % a , _SCREAMING_SNAKE_CASE )
def __lowercase ( __lowercase , __lowercase ) ... | 330 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_lowerCamelCase = pytest.mark.integration
@pytest.mark... | 71 | 0 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowercase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
#... | 5 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_... | 71 | 0 |
import re
def __a ( SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
__UpperCAmelCase = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return match.s... | 303 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
Audio... | 71 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase : Optional[int] ="timm_backbon... | 651 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
"""configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT... | 71 | 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 ImageProcessingSavi... | 287 |
'''simple docstring'''
import heapq
def a__ ( _SCREAMING_SNAKE_CASE : dict ) -> set[int]:
"""simple docstring"""
UpperCAmelCase_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq... | 71 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTest... | 577 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms imp... | 71 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
fr... | 377 |
'''simple docstring'''
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 impo... | 71 | 0 |
"""simple docstring"""
__magic_name__ = 8.314_4598
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
raise Exception("""Molar mass cannot be les... | 155 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Any =["speech"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["speech"] )
class _s... | 71 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"google/pix2struct-textcaps-base": (
"https://huggingface.co... | 329 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ) -> tuple[float, float]:
"""simple docstring"""
if not len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) == 3:
raise ValueError("Please... | 71 | 0 |
from collections.abc import Callable
class __lowercase :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCamelCase_ : Optional[Any] = None ):
"""simple docstring"""
__A = []
# Stores index... | 637 |
'''simple docstring'''
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 3 ) -> list:
"""simple docstring"""
UpperCAmelCase_ : Dict = min(_SCREAMING_SNAKE_CASE )
UpperCAmelCase... | 71 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
f... | 422 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, s... | 71 | 0 |
'''simple docstring'''
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> List[str]:
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(_SCREAMING_SNAKE_CASE , n - 1 , _SCREAMING_SNAK... | 330 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = 2
UpperCAmelCase_ : Optional[int] = []
while i * i <= n:
... | 71 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSchedu... | 5 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowerCamelCase = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Transl... | 71 | 0 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
if index == r:
for j in range(_SCREAMING_S... | 303 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
Auto... | 71 | 0 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Autoen... | 651 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_... | 71 | 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, rand... | 287 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def a__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) ... | 71 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case: List[Any] ... | 577 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _snake_case (nn.Module):
def __init__( self ,_snake_case = 16 ,_snake_case = 88 ,_snake_case = None ,_snake_case = 1 ,_snake... | 71 | 0 |
def _UpperCamelCase ( lowerCAmelCase_ ) ->tuple[int, int]:
try:
UpperCAmelCase = float(_SCREAMING_SNAKE_CASE )
except ValueError:
raise ValueError("""Please enter a valid number""" )
UpperCAmelCase = decimal - int(_SCREAMING_SNAKE_CASE )
if ... | 377 |
'''simple docstring'''
import json
import sys
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int ) -> Tuple:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8" ) as f:
UpperCAmelCase_ : ... | 71 | 0 |
"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
__magic... | 155 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase = g... | 71 | 0 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
__UpperCAmelCase = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowerCAmelCase_ ( __A : Any , __A : List[str] ):
'''simple docstri... | 329 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError("Input value must be an 'int' type" )
UpperCAmelCase_ : Union[str,... | 71 | 0 |
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
__a : Optional[Any] = [
"EAGER",
"AOT_EAGER",
"INDUCTOR",
"NVFUSER",
"AOT_NVFUSER... | 637 |
'''simple docstring'''
from math import factorial
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k" )... | 71 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_... | 72 |
'''simple docstring'''
from math import pi, sqrt, tan
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def UpperCamelCase ... | 72 | 1 |
'''simple docstring'''
_UpperCAmelCase : Dict = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
... | 72 |
'''simple docstring'''
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_sente... | 72 | 1 |
'''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, Si... | 72 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface... | 72 | 1 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCa... | 72 |
'''simple docstring'''
def UpperCamelCase ( ) -> int:
'''simple docstring'''
return 1
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCamelCase ( lowercase_ : ... | 72 | 1 |
'''simple docstring'''
_UpperCAmelCase : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCamelCase ( lowercase_ : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
lowercase =f'a bytes-... | 72 |
'''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, TruncationStrategy
fr... | 72 | 1 |
'''simple docstring'''
import copy
import re
class __magic_name__ :
UpperCamelCase__ = 'hp'
UpperCamelCase__ = {}
UpperCamelCase__ = None
@classmethod
def _A( cls , snake_case_ , snake_case_ ):
lowercase =prefix
lowercase =defaul... | 72 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401... | 72 | 1 |
'''simple docstring'''
from ....utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , snake_case_ , snake_case_=None , snake_case_=20_48 ):
lowercase =config.__dict__
l... | 72 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : str = '''▁'''
_UpperCAmelCase : Union[str, Any] = {'''vocab_fil... | 72 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : List[Any] = {
'''nielsr/canine-s''': 20_48,
}
# Unicode def... | 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplicati... | 72 | 1 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_UpperCAmelCase : Dict = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_to... | 72 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase... | 72 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase : str = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Res... | 72 |
'''simple docstring'''
_UpperCAmelCase : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCamelCase ( lowercase_ : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
lowercase =f'a bytes-... | 72 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_UpperCAmelCase : str = logging.getLogger(__name__)
@dataclass
class __magic_name__ ( __SCREAMING_SN... | 72 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_UpperCAmelCase : Union[str, Any] = datasets.logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = '''\
@InP... | 72 | 1 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.... | 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(lowercase_ , lowercase_ ):
return 0
elif n == 2:
return 1
else:
lowercase =[0, 1]
for i in range(2 , n + 1 ):
sequenc... | 72 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIM... | 72 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_d... | 72 | 1 |
'''simple docstring'''
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'''... | 72 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_UpperCAmelCase : Dict = parse(importlib.metadata.version('''torch'''))
def UpperCamelCase ( lowercase_ : Union[str, Version]... | 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : int ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Except... | 72 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
_UpperCAmelCase : int = [8, 5, 9, 7]
_UpperCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_UpperCAmelCase : Union[str, Any] = [
[3, 2, 1, 4... | 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> int:
'''simple docstring'''
return abs(lowercase_ ) if a == 0 else greatest_common_divisor(b % a , lowercase_ )
def UpperCamelCase ( lowercase_ : int , ... | 72 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_UpperCAmelCase : Dict = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_to... | 72 | 1 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __magic_name__ ( __SCREAMING_SN... | 72 |
'''simple docstring'''
import sys
_UpperCAmelCase : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''6... | 72 | 1 |
'''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/LICENSE-2.0
#
... | 72 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_UpperCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , *snake_case_ , **snake_case_ ):... | 72 | 1 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : float = 0.0 , lowercase_ : float = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / ... | 72 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
'''facebook/encodec_24khz''': '''https://huggingf... | 72 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( lowercase_ : list[float] ) -> float:
'''simple docstring'''
lowercase =0.0_0
lowercase =0
for resistor in resistors:
if resistor <= 0:
lowercase =f'Resistor at index {i... | 72 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : int = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',... | 72 | 1 |
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