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 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 |
'''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 | 1 |
'''simple docstring'''
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base impor... | 71 |
'''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 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
... | 71 |
'''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 | 1 |
'''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... | 71 |
'''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 | 1 |
'''simple docstring'''
import re
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> list:
"""simple docstring"""
return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]" , str_ )]
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> s... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : List[Any] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = 0
UpperCAmelCase_ : str = len(_SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in rang... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def a__ ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Optional[Any] , _S... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallb... | 71 |
'''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 | 1 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase ... | 71 |
'''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 | 1 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def a__ ( _SCREAMING_SNAKE_CASE : bytes ) -> bytes:
"""simple docstring"""
if len(_SCREAMING_SNAKE_CASE ) != 32:
raise ValueError("Input must be of length 32" )
UpperC... | 71 |
'''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 | 1 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
_lowerCamelCase = """src/transformers"""
# Matches is_xxx_available()
_lowerCamelCase = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
_lowerC... | 71 |
'''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 | 1 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
_lowerCamelCase =... | 71 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def a__ ( _SCREAMING_... | 71 |
'''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 | 1 |
'''simple docstring'''
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 a__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : D... | 71 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def a__ ( _SCREAMING_SNAKE_CASE : Any ) -> Union[str, Any]:
"""simple docstring"""
return choice(_SCREAMING_SNAKE_CASE )
def a__ ( _SCREAMING_SNAKE_CASE : ... | 71 |
'''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 | 1 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_tes... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is... | 71 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a__ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREA... | 71 |
'''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 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import Pretraine... | 71 |
'''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 | 1 |
'''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 _snake_case (__SCREA... | 71 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class _snake_case :
def __init__( self ,_snake_case ):
UpperCAmelCase_ : list[dict] = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "... | 71 |
'''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 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowerCamelCase = re.compile(R"""\b(a|an|the)\b""", re.UNICODE)
_lowerCamelCase = None
def a__ ( ) -> Tuple:
... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
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
fro... | 71 |
'''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 | 1 |
'''simple docstring'''
from collections import namedtuple
import requests
from lxml import html # type: ignore
_lowerCamelCase = namedtuple("""covid_data""", """cases deaths recovered""")
def a__ ( _SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus... | 71 |
'''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 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""google/pix2struct-textcaps-base""": (
... | 71 |
'''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 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class _snake_case (__SCREAMING_SNAKE_CASE):
def __init__( self ,*_snake_case ,**_snake_case ... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[Any] ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ : Dict ... | 71 |
'''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 | 1 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, req... | 71 |
'''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 | 1 |
'''simple docstring'''
from typing import Any
import numpy as np
def a__ ( _SCREAMING_SNAKE_CASE : np.ndarray ) -> bool:
"""simple docstring"""
return np.array_equal(_SCREAMING_SNAKE_CASE , matrix.conjugate().T )
def a__ ( _SCREAMING_SNAKE... | 71 |
'''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 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : str =["keras_nlp"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["keras_nlp"] )
| 71 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
class _snake_case :
def __init__( self ,_snake_case ,_snake_case ):
UpperCAmelCase_ , UpperCAmelCase_ : Dict = text, pattern
UpperCAmelCase_ , UpperCAmelCase_ : Dict = l... | 71 |
'''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 | 1 |
'''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,
Segf... | 71 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
class _snake_case :
def __init__( self ,_snake_case ):
UpperCAmelCase_ : Tuple = size
# approximate the overall size of segment tree with given value
UpperCAmelCase_ : str ... | 71 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int | None = None , _SCREAMING_SNAKE_CASE : int | None = None ) -> None:
"""simple docstring"""
if start is None:
U... | 71 |
'''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 | 1 |
'''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,
)
... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def a__ ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE ... | 71 |
'''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 | 1 |
'''simple docstring'''
from itertools import permutations
def a__ ( _SCREAMING_SNAKE_CASE : tuple ) -> bool:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return F... | 71 |
'''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 | 1 |
'''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 _snake_case (__SCREAMING_SNAKE_CASE , unittest.TestCase):
__A : Dict ... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return abs(_SCREAMING_SNAKE_CASE ) if a == 0 else greatest_common_divisor(b % a , _SCREAMING_SNAKE_CASE )
def a__ ... | 71 |
'''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 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""t5-small""": """https:... | 71 |
'''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 | 1 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_lowerCamelCase = HUGGINGFACE_HUB_CACHE
_lowerCamelCase = """config.json"""
_lowerCamelCase = """diffusion_pytorch_model.bin"""
_lowerCamelCase ... | 71 |
'''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 | 1 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..contro... | 71 |
'''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 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
... | 71 |
'''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 | 1 |
'''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 diffus... | 71 |
'''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 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
Aut... | 71 |
'''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 | 1 |
'''simple docstring'''
import math
def a__ ( _SCREAMING_SNAKE_CASE : int = 1_00 ) -> int:
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = sum(i * i for i in range(1 , n + 1 ) )
UpperCAmelCase_ : Optional[Any] = in... | 71 |
'''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 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_m... | 71 |
'''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 | 1 |
'''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 sepi... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = len(_SCREAMING_SNAKE_CASE )
for i in range(length - 1 ):
UpperCAmelCase_ : Any = i
for k in rang... | 71 |
'''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 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils... | 71 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_d... | 71 |
'''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 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_fla... | 71 |
'''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 | 1 |
'''simple docstring'''
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_CHECK... | 71 |
'''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 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""... | 71 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
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... | 71 |
'''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 | 1 |
'''simple docstring'''
_lowerCamelCase = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 100_0000,
"gigajoule": 10_0000_0000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 360_0000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalorie_nutr"... | 71 |
'''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 | 1 |
'''simple docstring'''
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,
... | 71 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
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 ... | 71 |
'''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 | 1 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaP... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ : Any = st... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = [], []
while len(_SCREAMING_SNAKE_CASE ) > 1:
UpperCAmelCase_ , UpperCAmelCase_ : ... | 71 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
_lowerCamelCase = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel... | 71 |
'''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 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Dict ,... | 71 |
'''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 | 1 |
'''simple docstring'''
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,... | 71 |
'''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 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCamelCase = 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_copie... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int = 10 ) -> str:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n < 0:
raise ValueError("Invalid input" )
UpperCAmelCase_ : Dict = 10**... | 71 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[list[str]] , _SCREAMING_SNAKE_CASE : int , ... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
def merge(_SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : list ) -> list:
def _merge():
while left and right:
yield (left if left... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_lowerCamelCase = {
"""sample_size""": 32,
"""in_channels""": 3,
"""out_channels""": 3,
"""lay... | 71 |
'''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 | 1 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def a__ ( _SC... | 71 |
'''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 | 1 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class _snake_case (tf.keras.optimizers.schedules.Learnin... | 71 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple... | 71 |
'''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 | 1 |
'''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... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
UpperCAmelCase_ : List[str] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def a__ ( _SCREAMING_SNAKE_CASE : ... | 71 |
'''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 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_lowerCamelCase = """docs/source/en/_toctree.yml"""
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ ... | 71 |
'''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 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import... | 71 |
'''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 | 1 |
'''simple docstring'''
from typing import Any
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : dict , _SCREAMING_SNAKE_CASE : dict , _SCREAMING_SNAKE_CASE : dict , ) -> list:
"""simple... | 71 |
'''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 | 1 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _snake_case :
@property
def UpperCam... | 71 |
'''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 | 1 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_lowerCam... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple ) ... | 71 |
'''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 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for... | 71 |
'''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 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_lowerCamelCase = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed ... | 71 |
'''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 | 1 |
'''simple docstring'''
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
@dat... | 71 |
'''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 | 1 |
'''simple docstring'''
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 transformer... | 71 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''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
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCa... | 71 |
'''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 | 1 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def a__ ( ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ : List[str] = 9
UpperCAmelCase_ : List[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8]... | 71 |
'''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 | 1 |
'''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
... | 71 |
'''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 | 1 |
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