code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
import math
UpperCAmelCase_ : Tuple = '2020.9.26'
UpperCAmelCase_ : List[str] = 'xcodz-dot, cclaus, dhruvmanila'
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase ... | 719 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def A_ ( ) -> Tuple:
"""simple docstring"""
assert xnor_gate(0 , 0 ... | 720 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 11 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase_ : Union[str, Any] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = os.path.dirname(os.path.realpath(_... | 721 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 11 | 0 |
'''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
UpperCAmelCase_ ... | 700 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_v... | 701 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowe... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : typing.Counter[int] = Counter()
for base in range(1 ,... | 702 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_config... | 703 |
'''simple docstring'''
import random
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
for _ in range(len(_lowerCAmelCase ) ):
_lowerCamelCase : Any = random.randint(0 , len(_lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
import numpy
class UpperCAmelCase__ :
def __init__( self : Dict,__A : numpy.ndarray,__A : numpy.ndarray ):
_lowerCamelCase : Dict = input_array
# Random initial weights are assigned where first argume... | 704 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
UpperCAmelCase_ : Any = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C""... | 705 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Union[str, Any] = ... | 11 | 0 |
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase... | 706 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : ... | 11 | 0 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
UpperCAmelCase_ : List[Any] = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase__... | 707 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
import math
import qiskit
def A_ ( _lowerCAmelCase : Tuple = 1 , _lowerCAmelCase : str = 1 , _lowerCAmelCase : List[Any] = 1 ):
"""simple docstring"""
if (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
... | 708 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 0 |
from datetime import datetime as dt
import os
from github import Github
UpperCAmelCase_ : Any = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def A_ ( ):
"""simple docstring"""
_lo... | 709 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def A_ ( _lowerCAmelCase : NDArray[floataa] , _lowerCAmelCase : NDArray[floataa] , _lowerCAmelCase : list[int] , _lowerCAmelCase : i... | 710 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 | 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
UpperCAmelCase_ : Optional[Any] = 'scheduler_config.jso... | 711 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 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,... | 712 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 11 | 0 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Any ... | 713 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return 10 - x * x
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if equation(_lowerCAmelCase ) *... | 11 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/r... | 714 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fro... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] = 0 ):
"""simple docstring"""
_lowerCamelCase : Any = length or len(_UpperCAmelCase )
_lowerCamelCase : Union[str, Any] = False... | 715 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 11 | 0 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set()... | 716 |
'''simple docstring'''
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 .tok... | 11 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAM... | 717 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 11 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase__ ( __snake_case , unittest.TestCase ):
lowerCAmelCas... | 718 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 11 | 0 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 719 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : bool , _lowerCAmelCase : list[int] , _lowerCAmelCase : float ) -> int:
"""simple docstring"""
... | 720 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 11 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str=1 ):
"""simple docstring"""
if n_shave_prefix_segments >= 0:
return ".".join(path.spl... | 721 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 11 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase__ ( snake_case_ ):
def lowerCamelCase_ ( self : Any ):
return [
{"col_1": 3, "col_2": "a"},
... | 700 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Dict = len(SCREAMING_SNAKE_CASE_ )
_lowerCamelCase : str = sum(SCREAMING_SNAKE_CASE_ )
_lowerCamelCase : str = ... | 701 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowe... | 11 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase__ ( A , unitte... | 702 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
while second != 0:
_lowerCamelCase : List[str] = first & second
first ^= second
_lowerCamelCase : str ... | 703 |
'''simple docstring'''
import random
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
for _ in range(len(_lowerCAmelCase ) ):
_lowerCamelCase : Any = random.randint(0 , len(_lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
def merge(_lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] ) -> list:
def _merge():
while left and right:
... | 704 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any ):
"""simple docstring"""
if principal <= 0:
raise Exception("Principal borrowed must be > 0" )
if rate_per_annum < 0:
... | 705 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Union[str, Any] = ... | 11 | 0 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {... | 706 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : ... | 11 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffus... | 707 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
UpperCAmelCase_ : int = '%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input... | 708 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 0 |
UpperCAmelCase_ : Any = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'dataclasses',... | 709 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = len(_lowerCAmelCase )
for _ in range(_lowerCAmelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
... | 710 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 | 0 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
UpperCAmelCase_ : Optional[Any] = 'src/transformers'
# Matches is_xxx_available()
UpperCAmelCase_ : int = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line ... | 711 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase__ ( lowercase_ ):
def lowerCamelCase_ ( self : Union[str, Any],__A : float ):
... | 712 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 11 | 0 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
fro... | 713 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return 10 - x * x
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if equation(_lowerCAmelCase ) *... | 11 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Bat... | 714 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fro... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
class UpperCAmelCase__ :
def __init__( self : Optional[Any],__A : int ):
_lowerCamelCase : Tuple = data
_lowerCamelCase : Union[str, Any] = None
_lowerCamelC... | 715 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 11 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase_ : Any = logging.get_logger(__name__)
class UpperCAmelCase__ ( __lowerCAmelCase ):
def __init__( self : Any,*__A... | 716 |
'''simple docstring'''
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 .tok... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ra... | 717 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 11 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : str = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONF... | 718 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 11 | 0 |
'''simple docstring'''
from collections import deque
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Tuple = len(__a )
_lowerCamelCase : Dict = deque()
_lowerCamelCase : Union[str,... | 719 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 11 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
UpperCAmelCase_ : Tuple = {
"""configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", "... | 720 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 11 | 0 |
from collections import Counter
from timeit import timeit
def A_ ( _lowerCAmelCase : str = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def A_ ( _lowerCAmelCase : ... | 721 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 11 | 0 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
UpperCAmelCase_ : int = False
class UpperCAmelCase_... | 700 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = u
for i in range(1 , lowerCamelCase_ ):
... | 701 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowe... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = len(UpperCamelCase__ )
for i in range(1 , UpperCamelCase__ ):
_lowerCamelCase : List[Any] ... | 702 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 11 | 0 |
'''simple docstring'''
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 Tokeni... | 703 |
'''simple docstring'''
import random
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
for _ in range(len(_lowerCAmelCase ) ):
_lowerCamelCase : Any = random.randint(0 , len(_lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCa... | 704 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 11 | 0 |
'''simple docstring'''
import numpy
# List of input, output pairs
UpperCAmelCase_ : Tuple = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCAmelCase_ : Tuple = (((515, 22, 13), 555), ((61, 35, 49), 150))
Uppe... | 705 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Union[str, Any] = ... | 11 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 706 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : ... | 11 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_availab... | 707 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
return "".join(sorted(lowerCAmelCase__ ) )
def A_ ( _lowerCAmelCase... | 708 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 0 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
... | 709 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa... | 11 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class UpperCAmelCase__ ( datasets.BeamBasedBuilder ):
def lowerCamelCase_ ( ... | 710 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 | 0 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
... | 711 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IM... | 712 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 11 | 0 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Dict = loggin... | 713 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return 10 - x * x
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if equation(_lowerCAmelCase ) *... | 11 | 0 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class UpperCAmelCase__ ( unittest.TestCase ):
def lowerCamelCase_ ( self : Dict ):
deb... | 714 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fro... | 11 | 0 |
'''simple docstring'''
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 OptionalDependencyNotAvail... | 715 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 11 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.... | 716 |
'''simple docstring'''
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 .tok... | 11 | 0 |
'''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase_ : List[Any] = "1"
UpperCAmelCase_ : Optional[int] = "0"
UpperCAmelCase_ : str = "1"
UpperCAmelCase_ : str = ort.SessionOptions()
UpperC... | 717 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 11 | 0 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
UpperCAmelCase_ : List[str] = 'src/transformers'
# Matches is_xxx_available()
UpperCAmelCase_ : Optional[int] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one... | 718 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 11 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 719 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 11 | 0 |
'''simple docstring'''
import cva
import numpy as np
class UpperCAmelCase__ :
def __init__( self : List[Any],__A : float,__A : int ):
if k in (0.04, 0.06):
_lowerCamelCase : Optional[int] = k
_lowerCamelCa... | 720 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 11 | 0 |
import argparse
import json
import subprocess
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Any = []
_lowerCamelCase : Optional[Any] = (
F'curl -H "Accept: application/... | 721 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 11 | 0 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class UpperCAmelCase__ :
def __init__( self : Tuple,__A : Any ):
_lowerCamelCase : Any = str(id_ )
_lowerCamelCase : Optional[int] =... | 700 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 | 0 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
UpperCAmelCase_ : Dict = 4
UpperCAmelCase_ : List[str] = 3
... | 701 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowe... | 11 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['image_processor', 'feature_extractor']
lowerCAmelCase_ = 'TvltImageProcessor'
lowerCAmelCase_ = 'TvltFeatureExtractor'
def __init__( self... | 702 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 11 | 0 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def A_ ( _lowerCAmelCase : int , _lowerCAmelCa... | 703 |
'''simple docstring'''
import random
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
for _ in range(len(_lowerCAmelCase ) ):
_lowerCamelCase : Any = random.randint(0 , len(_lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenizat... | 704 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 11 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 705 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Union[str, Any] = ... | 11 | 0 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def A_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : bool = True , _lowerCAmelCase : float = math.inf , _lowerCAmelCase : float = -math.inf ... | 706 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : ... | 11 | 0 |
'''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
UpperCAmelCase_ : Any = loggi... | 707 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
... | 708 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : Tuple = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vision_available():... | 709 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa... | 11 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCAmelCase_ : str = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_... | 710 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 | 0 |
'''simple docstring'''
import copy
import re
class UpperCAmelCase__ :
lowerCAmelCase_ = 'hp'
lowerCAmelCase_ = {}
lowerCAmelCase_ = None
@classmethod
def lowerCamelCase_ ( cls : Any,__A : Tuple,__A : Dict ):
_... | 711 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPri... | 712 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 713 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return 10 - x * x
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if equation(_lowerCAmelCase ) *... | 11 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoToken... | 714 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fro... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCAmelCase_ : List[str] = 1.0_5457_1817E-34 # unit of ℏ : J * s
UpperCAmelCase_ : Any = 3E8 # uni... | 715 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 11 | 0 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_loggi... | 716 |
'''simple docstring'''
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 .tok... | 11 | 0 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ : str = get_tests_dir('fixture... | 717 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
while a != 0:
_lowerCamelCase : Union[str, Any] = b % a, a
return b
def A_ ( _lowerCAmelCase : int , _lowerC... | 718 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 11 | 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... | 719 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int ) -> List[str]:
"""simple docstring"""
if len(_lowerCAmelCase ) == 0:
return False
_lowerCamelCase : int ... | 720 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 11 | 0 |
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__ ( A ):
lowerCAmelCase_ = ['image_p... | 721 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_config... | 700 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 | 0 |
'''simple docstring'''
from collections.abc import Sequence
def A_ ( _lowerCAmelCase : Sequence[int] | None = None ):
"""simple docstring"""
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
_lowerCamelCase ... | 701 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowe... | 11 | 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_available():
import torc... | 702 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 11 | 0 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def A_ ( ):
... | 703 |
'''simple docstring'''
import random
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
for _ in range(len(_lowerCAmelCase ) ):
_lowerCamelCase : Any = random.randint(0 , len(_lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : List[Any] = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M1... | 704 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 11 | 0 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_... | 705 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Union[str, Any] = ... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
assert column_title.isupper()
_lowerCamelCase : Optional[Any] = 0
_lowerCamelCase : Union[str, Any] = len(_lowerCAmelCase ) - 1
_lowerCamelC... | 706 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : ... | 11 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCAmelCase_ : int =... | 707 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
UpperCAmelCase_ : int = get_logger(__name__)
class UpperCAmelCase__ :
def __init__( self : List[str],__A : Optional[Any],__A : Optional[Any]=None ):
... | 708 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 0 |
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