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"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.... | 701 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> ... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase = 4 ) -> list[list[int]]:
SCREAMING_SNAKE_CASE__ : str = abs(_SCREAMING_SNAKE_CASE ) or 4
return [[1 + x + y * row_size for x in range(_SCREAMING_SNAKE_C... | 702 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while in... | 12 | 0 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
a :Any = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
... | 703 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import T... | 12 | 0 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
a :Union[str, Any] = logging.getLogger()
d... | 704 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny":... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a :int = {"configuration_mbart": ["MBART_PR... | 705 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, 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... | 12 | 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... | 706 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> ... | 12 | 0 |
"""simple docstring"""
import baseaa
def _lowercase ( __lowerCAmelCase ) -> bytes:
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def _lowercase ( __lowerCAmelCase ) -> str:
return baseaa.aaadecode(__lowerCAmelCase ).decode("... | 707 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_A... | 12 | 0 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __a ... | 708 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from ... | 12 | 0 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig... | 709 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/r... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
a :List[Any] = "2020.9.26"
a :int = "xcodz-dot, cclaus, dhruvmanila"
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> ... | 710 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5... | 12 | 0 |
"""simple docstring"""
from sklearn.metrics import mean_squared_error
import datasets
a :Optional[Any] = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and ... | 711 |
"""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 .token... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a :Optional[int] = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available(... | 712 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d mat... | 12 | 0 |
"""simple docstring"""
import heapq
import sys
import numpy as np
a :str = tuple[int, int]
class __a :
'''simple docstring'''
def __init__( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = []
... | 713 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all mu... | 12 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenize... | 714 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = ... | 12 | 0 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
... | 715 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 12 | 0 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase... | 716 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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
... | 12 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Any = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
... | 717 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
... | 12 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
a :int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be... | 718 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 12 | 0 |
from __future__ import annotations
from typing import Any
class __a :
'''simple docstring'''
def __init__( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = num_of_nodes
SCREAMING_SNAKE_CASE__ :... | 719 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
impo... | 12 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .att... | 720 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNA... | 12 | 0 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test... | 721 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequence... | 12 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
re... | 700 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - ge... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Tuple = []
create_all_state(1 , __lowerCAmelCase , __lowerCAmelCase , [] , __lowerCAmelCase ... | 701 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> ... | 12 | 0 |
"""simple docstring"""
a :int = range(2, 20 + 1)
a :int = [10**k for k in range(ks[-1] + 1)]
a :dict[int, dict[int, list[list[int]]]] = {}
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[A... | 702 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while in... | 12 | 0 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def _lowercase ( __lowerCAmelCase ) -> Dict:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("""Undefined ... | 703 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import T... | 12 | 0 |
"""simple docstring"""
from collections.abc import Callable
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : float = a
SCREAMING_SNAKE_CASE__ : float = b
if... | 704 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny":... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase ) -> Any:
create_state_space_tree(snake_case__ , [] , 0 , [0 for i in range(len(snake_case__ ) )] )
def _lowercase ( __lowerCAmelCase , __low... | 705 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, 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... | 12 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
a :Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # n... | 706 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> ... | 12 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
a :List[Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _lowercase ( __l... | 707 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_A... | 12 | 0 |
"""simple docstring"""
import math
import qiskit
def _lowercase ( __lowerCAmelCase = 1 , __lowerCAmelCase = 1 , __lowerCAmelCase = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(_lowercase , _lowercase )
or isinstance(_lowercase ... | 708 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from ... | 12 | 0 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""... | 709 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/r... | 12 | 0 |
"""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 __a (__UpperCAmelCase):
'''... | 710 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
class __a :
'''simple docstring'''
def __init__( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = data
SCREAMING_SNAKE_CASE__... | 711 |
"""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 .token... | 12 | 0 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _lowercase ( __lowerCAmelCase ) -> str:
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump... | 712 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d mat... | 12 | 0 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ... | 713 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all mu... | 12 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import loggi... | 714 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = ... | 12 | 0 |
"""simple docstring"""
import math
def _lowercase ( __lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : List[Any] = F'''Input value of [number={number}] must be an integer'''
rai... | 715 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 12 | 0 |
"""simple docstring"""
import os
def _lowercase ( __lowerCAmelCase = "input.txt" ) -> Union[str, Any]:
with open(os.path.join(os.path.dirname(__lowerCAmelCase ) , __lowerCAmelCase ) ) as input_file:
SCREAMING_SNAKE_CASE__ : int = ... | 716 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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
... | 12 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import S... | 717 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
... | 12 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
a :Union[str, Any] = logging.get_logger(__name__)
a :Dict = {"... | 718 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 12 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase)
class __a (__lowercase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = f... | 719 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
impo... | 12 | 0 |
"""simple docstring"""
from collections import deque
from .hash_table import HashTable
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , *_a , **_a ) -> Tuple:
"""simple docstring"""
super().__init__(*UpperCAmelCase__ , ... | 720 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNA... | 12 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
a :Optional[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class __a ... | 721 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequence... | 12 | 0 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any = int(_SCREAMING_SNAKE_CASE )
assert noofclusters < len... | 700 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - ge... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase = 2 , __lowerCAmelCase = 1 , __lowerCAmelCase = 3 , ) -> Any:
# A value less than 2 can cause an infinite loop in the algorithm.
i... | 701 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> ... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a :Dict = {'configuration_xlnet': ['XLNET_PRETRAI... | 702 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while in... | 12 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Optional[int] = logging.get_logger(__name__)
a :Tuple = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
... | 703 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import T... | 12 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils imp... | 704 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny":... | 12 | 0 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accele... | 705 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, 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... | 12 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
fr... | 706 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> ... | 12 | 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__":
a :Optional[Any] = "%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search: ")))
... | 707 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_A... | 12 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_lowerCAmelCase)
class __a (_lowerCAmelCase):
'''simple docstring'''
# `task` is not a Clas... | 708 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from ... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
if len(snake_case_ ) == 0:
return False
SCREAMING_SNAKE_CASE__ : str = len(snake_case_ ) /... | 709 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/r... | 12 | 0 |
"""simple docstring"""
from timeit import timeit
def _lowercase ( __lowerCAmelCase ) -> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
SCREAMING_SNAKE_CASE__ : int = 0
while number:
... | 710 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5... | 12 | 0 |
"""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():
from ..mod... | 711 |
"""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 .token... | 12 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Dict = ""
for i in table:
res += inp[i - 1]
return res
def _lowercase ( __lowerCAmelCase ) -> int:
... | 712 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d mat... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
a :List[Any] = "#"
class __a :
'''simple docstring'''
def __init__( self ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = {}
def _a ( s... | 713 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all mu... | 12 | 0 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Opt... | 714 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = ... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
a :Optional[int] = {
'''configuration_speech_to_text''': ['''SPEEC... | 715 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 12 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __a :
'''simple docstring'''
def __init__( self , _a=2 , _a=3 , _a=64 , _a=None ) -> List[Any]:
... | 716 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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
... | 12 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a ... | 717 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
... | 12 | 0 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_t... | 718 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 12 | 0 |
a :Any = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 719 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
impo... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a :Union[str, Any] = {
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}
try:
if not i... | 720 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNA... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
a :Any = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}... | 721 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequence... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
a :Union[str, Any] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
a :List[Any] = typing.Union[np.floataa, int, float] # noqa: UP007
def _l... | 700 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - ge... | 12 | 0 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :int = {"vocab_file": "vocab.... | 701 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> ... | 12 | 0 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
a :Dict = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edg... | 702 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while in... | 12 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
while second != 0:
SCREAMING_SNAKE_CASE__ : List[str] = first & second
first ^= second
SCREAMING_SNAKE_CA... | 703 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import T... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a :Dict = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],... | 704 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny":... | 12 | 0 |
"""simple docstring"""
import math
import os
import sys
def _lowercase ( __lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ : List[str] = """"""
try:
with open(lowerCAmelCase__ , """rb""" ) as binary_file:
SCREAMING_SNAKE_C... | 705 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, 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... | 12 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inp... | 706 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> ... | 12 | 0 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> A... | 707 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_A... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a :Tuple = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Refor... | 708 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from ... | 12 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
if not (isinstance(A_ , A_ ) and isinstance(A_ , A_ )):
raise ValueError("""longest_common_substring() takes two strings for inputs""" ... | 709 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/r... | 12 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int ... | 710 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5... | 12 | 0 |
"""simple docstring"""
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:... | 711 |
"""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 .token... | 12 | 0 |
"""simple docstring"""
import json
import sys
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
with open(lowerCamelCase_ , encoding="""utf-8""" ) as f:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = json.load(lowerCamelCas... | 712 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d mat... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
a :Optional[Any] = 0
a :int = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, ... | 713 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all mu... | 12 | 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 Tokenizer... | 714 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = ... | 12 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
a :Union[str, Any] = logging.get_logger(__name__)
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , *_a , **_a ) ->... | 715 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a :int = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfi... | 716 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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
... | 12 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : List[str] = [int(__lowerCAmelCase ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(__lowerCAmelCase ) == 4 and all(0 <= int(__lowerCAmelCase... | 717 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
... | 12 | 0 |
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def _lowercase ( *__lowerCAmelCase ) -> Any:
with open(__lowerCAmelCase , """r""" ) as fh:
fcntl.flock(__lowerCAmelCase , fcntl.LOCK_EX )
try:
... | 718 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 12 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Union[str, Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggi... | 719 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
impo... | 12 | 0 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
if start < end:
... | 720 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNA... | 12 | 0 |
"""simple docstring"""
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
a :Dict = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
d... | 721 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequence... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a :Optional[int] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_torch_availa... | 700 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - ge... | 12 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, i... | 701 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> ... | 12 | 0 |
"""simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from tran... | 702 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while in... | 12 | 0 |
"""simple docstring"""
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("""repo_id""" , ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path""" , ["""filename.csv""", """filename with blank... | 703 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import T... | 12 | 0 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a :Optional[Any] = logging.get_logger(__name__)
a :Any = O... | 704 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny":... | 12 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
a :Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
a ... | 705 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, 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... | 12 | 0 |
"""simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
a :List[str] = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention.self"... | 706 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> ... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a :Optional[Any] = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],... | 707 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_A... | 12 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Dict = logging.get_logger(__name__)
a :Any = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json... | 708 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from ... | 12 | 0 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
a :List[Any] = TypeVar("_T")
class __a (Generic[_T]):
'''simple docstring'''
def __init__( self , _a = None ) -> None:
"""simple docs... | 709 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/r... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a :List[str] = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a :Union[str, Any] = _LazyModule(__name__, global... | 710 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5... | 12 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> list[int]:
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = [True] * (num + 1)
SCREAMING_SNAKE_CASE__ : An... | 711 |
"""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 .token... | 12 | 0 |
"""simple docstring"""
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncod... | 712 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d mat... | 12 | 0 |
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