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 |
|---|---|---|---|---|
import math
import os
import sys
def UpperCamelCase ( snake_case__ : str ) -> str:
UpperCamelCase : Tuple = ''
try:
with open(snake_case__ , 'rb' ) as binary_file:
UpperCamelCase : Dict = binary_file.read()
for... | 40 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 126 | 0 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configur... | 41 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase = 1_00_00_00 ) -> int:
lowerCamelCase_ = 1
lowerCamelCase_ = 1
lowerCamelCase_ = {1: 1}
for inputa in range(2 ,__UpperCamelCase ):
lowerCamelCase_ = 0
lower... | 42 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
for param in module.parameters():
lowercase__ = False
def _a ( ):
"""simple docstring"""
lowercase__ = '''cuda''... | 43 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProces... | 44 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAme... | 126 | 0 |
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 ( lowercase__ : Optional[Any] ) -> Any:
Up... | 45 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase = " " )-> list:
__UpperCAmelCase = []
__UpperCAmelCase = 0
for index, char in enumerate(_lowerCAmelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__Uppe... | 126 | 0 |
"""simple docstring"""
import os
from distutils.util import strtobool
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> List[str]:
'''simple docstring'''
for e in env_keys:
_lowerCamelCase : List[Any] = int(os.environ.get(_lowerCamelCase ... | 46 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, ... | 126 | 0 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPrior... | 47 |
'''simple docstring'''
from __future__ import annotations
_A: Tuple = list[list[int]]
# assigning initial values to the grid
_A: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 126 | 0 |
'''simple docstring'''
import re
def A ( UpperCamelCase_ : str ) -> str:
'''simple docstring'''
if len(re.findall("[ATCG]" , UpperCamelCase_ ) ) != len(UpperCamelCase_ ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("A... | 48 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_A: Any = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A ):
... | 126 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
P... | 49 |
'''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_vision... | 126 | 0 |
'''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_effe... | 50 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__UpperCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lo... | 126 | 0 |
'''simple docstring'''
import string
def __snake_case ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
UpperCAmelCase = ''''''
for i in sequence:
UpperCAmelCase = ord(SCREAMING_SNAKE_CASE_ )
if 65 <= extract <= 90:
... | 51 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A: List[str] = logging.get_... | 126 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
A = '''\
'''
A = '''
Perplexity (PPL) is one of the most common ... | 52 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A: Optional[Any] = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R... | 126 | 0 |
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : str ):
def get_matched_characters(lowerCAmelCase_ : str, lowerCAmelCase_ : str ) -> str:
__lowerCAmelCase = []
__lowerCAmelCase = min(len(_stra ), len(_stra ... | 53 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Optional[int] = CustomTokenizer
pass
| 126 | 0 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
def __init__( s... | 54 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A: Optional[int] = True
except (ImportError, ModuleNotFoundError):
_A: Dict = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt"... | 126 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension... | 55 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase = 2**power
__UpperCAmelCase = 0
while n:
__UpperCAmelCase , __UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).... | 126 | 0 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_a : List[Any] = logging.get_logger(__name__)
def _a (lowe... | 56 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Any:
__UpperCAmel... | 126 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:... | 57 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 126 | 0 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__lowerCAmelCase : Tuple ... | 58 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTec... | 59 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_A: List[Any] = datasets.load_iris()
_A: Union[str, Any] = np.array(data["""data"""])
_A: Union[str, Any] ... | 126 | 0 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : ... | 60 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCAmelCase ( _lowerCAmelCase="ro" , _lowerCAmelCase="en" , _lowerCAmelCase="wmt16" , _lowerCAmelCase=None )-> None:
try:
import datasets
except (ModuleNotFoundError, Imp... | 126 | 0 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase = version.parse(version.pars... | 61 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 126 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
snake_case = False
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstrin... | 62 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 126 | 0 |
class a :
"""simple docstring"""
def __init__( self : List[str] ) -> None:
__UpperCAmelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCAmelCase : List[str] = False
def UpperCAmel... | 63 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowercase_ : Tuple = TypeVar('T')
class _lowerCamelCase ( Generic[T] ):
def __init__( self , lowerCAmelCase , lowerCAmelCase ) -> None:
SCREAMING_SNAKE... | 64 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__UpperCAmelCase = {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConf... | 65 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 0 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowerCAmel... | 66 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAme... | 126 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependency... | 67 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase = " " )-> list:
__UpperCAmelCase = []
__UpperCAmelCase = 0
for index, char in enumerate(_lowerCAmelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__Uppe... | 126 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/realm-cc-n... | 68 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, ... | 126 | 0 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __Upper... | 69 |
'''simple docstring'''
from __future__ import annotations
_A: Tuple = list[list[int]]
# assigning initial values to the grid
_A: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 126 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : Dict = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaXLConfig",
... | 70 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_A: Any = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A ):
... | 126 | 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... | 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_vision... | 126 | 0 |
'''simple docstring'''
from maths.prime_check import is_prime
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
lowercase =f'Input value of [number={number}] must be an integer'
raise TypeError(l... | 72 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__UpperCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lo... | 126 | 0 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
a_ : Optional[Any] = get_logger(__name__)
a_ : Dict = R'\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_si... | 73 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A: List[str] = logging.get_... | 126 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a__ ( snake_case = True , *snake_case , **snake_case ):
"""simple docstring"""
if not is_tqdm_available():
raise ImportErro... | 74 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A: Optional[Any] = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R... | 126 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerC... | 75 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Optional[int] = CustomTokenizer
pass
| 126 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def __UpperCAmelCase ( ):
from torch.utils.cpp_extension import load
__lowercase : List[Any] = Path(__UpperCamelCase ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
... | 76 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A: Optional[int] = True
except (ImportError, ModuleNotFoundError):
_A: Dict = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt"... | 126 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : List[Any] = len(UpperCamelCase )
for _ in range(UpperCamelCase ):
for i in range(_ % 2 , arr_size - 1 , ... | 77 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase = 2**power
__UpperCAmelCase = 0
while n:
__UpperCAmelCase , __UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).... | 126 | 0 |
'''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 (
AudioLDMPipeline,
... | 78 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Any:
__UpperCAmel... | 126 | 0 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class UpperCAmelCase_ ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
UpperCAmelCase__ : Any = get_activation("""swi... | 79 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 126 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
__UpperCamelCase : Union[str, Any] = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": ... | 80 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase = 1_0_0_0_0_0_0 ):
__snake_case : Dict = set(range(3 , __lowerCamelCase , 2 ) )
primes.add(2 )
for p in range(3 , __lowerCamelCase , 2 ):
if p not in primes:
... | 81 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_A: List[Any] = datasets.load_iris()
_A: Union[str, Any] = np.array(data["""data"""])
_A: Union[str, Any] ... | 126 | 0 |
"""simple docstring"""
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_ava... | 82 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCAmelCase ( _lowerCAmelCase="ro" , _lowerCAmelCase="en" , _lowerCAmelCase="wmt16" , _lowerCAmelCase=None )-> None:
try:
import datasets
except (ModuleNotFoundError, Imp... | 126 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from acceler... | 83 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 126 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''facebook/data2vec... | 84 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 126 | 0 |
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 snake_case ( UpperCamelCase_ ):
lowercase_ = (DPMSolverSDESched... | 85 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 | 0 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase ... | 86 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowerCamelCase : str = pd.read_csv("""sample_data.csv""", header=None)
_lowerCamelCa... | 87 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def _snake_case ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
_lowerCamelCase : Optional[Any] = Path(__snake_case ).resolve().parent.parent.parent / """kernels""" / """de... | 88 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAme... | 126 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SC... | 89 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase = " " )-> list:
__UpperCAmelCase = []
__UpperCAmelCase = 0
for index, char in enumerate(_lowerCAmelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__Uppe... | 126 | 0 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase = []
def _snake_case ( A , A , A ) -> bool:
for i in range(len(A ) ):
if board[row][i] == 1:
return False
for i ... | 90 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, ... | 126 | 0 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_lowercase = re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')
@total_ordering
@dataclass... | 91 |
'''simple docstring'''
from __future__ import annotations
_A: Tuple = list[list[int]]
# assigning initial values to the grid
_A: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 126 | 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,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,... | 92 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_A: Any = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A ):
... | 126 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
"""configuration_layoutlmv3""": [
"""L... | 93 |
'''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_vision... | 126 | 0 |
'''simple docstring'''
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCH... | 94 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__UpperCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lo... | 126 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wava... | 95 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A: List[str] = logging.get_... | 126 | 0 |
"""simple docstring"""
from __future__ import annotations
import requests
__lowerCamelCase = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post cate... | 96 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A: Optional[Any] = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R... | 126 | 0 |
def a ( ):
'''simple docstring'''
return 1
def a ( snake_case__: int ):
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def a ( snake_case__: int ):
'''simple docstring'''
return 0 if x... | 97 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Optional[int] = CustomTokenizer
pass
| 126 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
_snake_case : Dict = ['transformers', 'torch', 'note_seq']
def __init__( self : Tuple ... | 98 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A: Optional[int] = True
except (ImportError, ModuleNotFoundError):
_A: Dict = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt"... | 126 | 0 |
import argparse
import datetime
def a (lowerCAmelCase__ ):
__a = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""",
"""5""": """Friday""",
"""6""": """Saturday"""... | 99 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase = 2**power
__UpperCAmelCase = 0
while n:
__UpperCAmelCase , __UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).... | 126 | 0 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requi... | 100 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Any:
__UpperCAmel... | 126 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
impo... | 101 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 126 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pr... | 102 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from... | 103 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_A: List[Any] = datasets.load_iris()
_A: Union[str, Any] = np.array(data["""data"""])
_A: Union[str, Any] ... | 126 | 0 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
UpperCamelCase = get_logger(__name__)
class UpperCamelCase__ ( enum.Enum ):
"""simple d... | 104 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCAmelCase ( _lowerCAmelCase="ro" , _lowerCAmelCase="en" , _lowerCAmelCase="wmt16" , _lowerCAmelCase=None )-> None:
try:
import datasets
except (ModuleNotFoundError, Imp... | 126 | 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__ : str =... | 105 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 126 | 0 |
import os
from datetime import datetime as dt
from github import Github
__snake_case :int =[
'good first issue',
'feature request',
'wip',
]
def lowerCamelCase_ ( ) -> Any:
'''simple docstring'''
A = Github(os.environ['GITHUB_TOK... | 106 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 126 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : Any = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConf... | 107 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 | 0 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
_lowerCamelCase = 42
_lowerCamelCase = 4... | 108 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGenerati... | 109 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 0 |
def __a ( A__ : Optional[Any] , A__ : Any ):
SCREAMING_SNAKE_CASE = word.split()
def justify(A__ : str , A__ : Tuple , A__ : Tuple ) -> str:
SCREAMING_SNAKE_CASE = max_width - width
SCREAMING_SNAKE_CASE = le... | 16 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAme... | 126 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_t... | 439 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase = " " )-> list:
__UpperCAmelCase = []
__UpperCAmelCase = 0
for index, char in enumerate(_lowerCAmelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__Uppe... | 126 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__snake_case :Optional[Any] ={"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not ... | 106 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, ... | 126 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( a_ : Tuple , a_ : Dict , a_ : Tuple )-> Optional[... | 698 |
'''simple docstring'''
from __future__ import annotations
_A: Tuple = list[list[int]]
# assigning initial values to the grid
_A: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 126 | 0 |
'''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_vision
fr... | 120 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_A: Any = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A ):
... | 126 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizat... | 206 |
'''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_vision... | 126 | 0 |
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def __UpperCamelCase (lowe... | 699 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__UpperCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lo... | 126 | 0 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
__SCREAMING_SNAKE_CASE : Dict = [
"""kernels/rwkv/wkv_cuda.cu""",
"""kernels/rwkv/wkv_op.cpp""",
"""kernels/deformable_detr/ms_deform_attn.h""",
"""kernels/deformable_detr/cuda/ms_deform... | 348 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A: List[str] = logging.get_... | 126 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A_ : List[str] =logging.get_logger(__name__) # pylint: disable=invalid-name
class __a ( ... | 650 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A: Optional[Any] = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R... | 126 | 0 |
'''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 logging
__lowercase : Dict = logging.get_logger(__... | 476 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Optional[int] = CustomTokenizer
pass
| 126 | 0 |
'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__lowerCAmelCase = datasets.uti... | 358 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A: Optional[int] = True
except (ImportError, ModuleNotFoundError):
_A: Dict = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt"... | 126 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_sched... | 16 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase = 2**power
__UpperCAmelCase = 0
while n:
__UpperCAmelCase , __UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).... | 126 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
a_ : str = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""": """https://hugging... | 439 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Any:
__UpperCAmel... | 126 | 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)
__snake_case :str =... | 106 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 126 | 0 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import ... | 698 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : List[str] = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_tor... | 120 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_A: List[Any] = datasets.load_iris()
_A: Union[str, Any] = np.array(data["""data"""])
_A: Union[str, Any] ... | 126 | 0 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_com... | 206 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCAmelCase ( _lowerCAmelCase="ro" , _lowerCAmelCase="en" , _lowerCAmelCase="wmt16" , _lowerCAmelCase=None )-> None:
try:
import datasets
except (ModuleNotFoundError, Imp... | 126 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase = {
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""tokenization_canine""": ["""Cani... | 699 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 126 | 0 |
from collections.abc import Sequence
def UpperCAmelCase__ ( __magic_name__ : Tuple = None ):
'''simple docstring'''
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
lowerCAmelCase : Any = nums[0]
for i in range(1 ... | 348 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 126 | 0 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __a ( tf.keras.layers.Layer ):
def __in... | 650 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
__lowercase : List[str] = {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 476 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 358 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 0 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast... | 16 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAme... | 126 | 0 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ : Any = logging.get_logger(__name__)
@add_end... | 439 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase = " " )-> list:
__UpperCAmelCase = []
__UpperCAmelCase = 0
for index, char in enumerate(_lowerCAmelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__Uppe... | 126 | 0 |
def lowerCamelCase_ ( lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : int ) -> str:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
A = str(bin(_lowerCAmelC... | 106 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, ... | 126 | 0 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowerCamelCase__ : List[Any] = datasets.load_iris()
lowerCamelCase__ : Union[str, Any] = np.array(data["data"])
lowerC... | 698 |
'''simple docstring'''
from __future__ import annotations
_A: Tuple = list[list[int]]
# assigning initial values to the grid
_A: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 126 | 0 |
'''simple docstring'''
import numpy as np
import qiskit
def UpperCAmelCase_ ( A = 8 , A = None ):
'''simple docstring'''
_a : str = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we ne... | 120 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_A: Any = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A ):
... | 126 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCamelCase : Dict ={
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenizat... | 206 |
'''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_vision... | 126 | 0 |
from __future__ import annotations
from typing import Any
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] = 0 ):
A , ... | 699 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__UpperCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lo... | 126 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__SCREAMING_SNAKE_CASE : Any = {
"""configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
... | 348 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A: List[str] = logging.get_... | 126 | 0 |
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