code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def UpperCAmelCase__ ( _A : Dict = 10_00 ):
'''simple docstring'''
a__ =2**power
a__ =0
while n:
a__ =r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 188 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 318 | 0 |
'''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 .toke... | 27 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging... | 318 | 0 |
import numpy
class _lowerCamelCase :
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Optional[Any]:
'''simple docstring'''
A_ : Optional[int] = input_array
# Random... | 186 |
'''simple docstring'''
import numpy
class __lowercase :
def __init__(self , A , A ):
lowerCamelCase_ : Optional[int] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is t... | 318 | 0 |
"""simple docstring"""
lowerCAmelCase__ : List[Any] = '''Input must be a string of 8 numbers plus letter'''
lowerCAmelCase__ : Any = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def a_ ( lowerCamelCase ):
if not isinstance(_lowercase , _lowercase ):
UpperCAmel... | 98 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokenize... | 318 | 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 ...te... | 315 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__lowercase : str = Lock()
def lowercase_ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _... | 318 | 0 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testin... | 220 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if ver... | 318 | 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
A = False
class __l... | 160 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : str = logging.get_logger(__name__)
__lowercase : Tuple = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __lowercase ( _lowercas... | 318 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
lowerCAmelCase :Union[str, Any] = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
''... | 331 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __lowercase ( tf.keras.layers.Layer ):
def __init__(self , ... | 318 | 0 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
a_ : int = pytest.mark.integration
@pytest.mark.p... | 55 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_r... | 318 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to... | 303 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
Aut... | 318 | 0 |
"""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 tensorflow as tf
from transformers import AutoTokenizer, ... | 264 |
'''simple docstring'''
from __future__ import annotations
import time
__lowercase : List[Any] = list[tuple[int, int]]
__lowercase : List[Any] = [
[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... | 318 | 0 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
lowerCamelCase = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''... | 188 |
'''simple docstring'''
import numpy as np
def lowercase_ ( _lowercase ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowercase_ ( _lowercase ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(_lowercase )
if __nam... | 318 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase : int = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructC... | 27 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImagePro... | 318 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class _lowerCamelCase ( _lowercase ):
... | 186 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( _lowercase ) -> list[int]: # This function is recursive
'''simple docstring'''
lowerCamelCase_ : Tuple = len(_lowercase )
# If the array contains only one element, we return it (it's the stop c... | 318 | 0 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def a_ ( lowerCamelCase ):
return np.dot(_lowercase , _lowercase )
class snake_case :
"""simple docstring"""
def __init__( ... | 98 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__lowercase : Dict = logging.get_logger(__name__)
class __lowercase ( _lowercase ):
def __init__(self , *A , **A ):
warnings.warn(
... | 318 | 0 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from... | 315 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
__lowercase : Optional[Any] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__lowercase : Any = ... | 318 | 0 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __snake_case : List[Any] = 4 ):
'''simple docstring'''
lowercase = abs(_lowercase ) or 4
return [[1 + x + y * row_size for x in range(_lowercase )] for y in range(_lowerca... | 220 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
... | 318 | 0 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __A ( a_ :Dict) -> np.ndarray:
__a : Any = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def __A ... | 160 |
'''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... | 318 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : List[str] = 50 ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start i... | 331 |
'''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 t... | 318 | 0 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __snake_case ( UpperCAmelCase_ : Optional[int] ):
if not is_accelerate_available():
return method
lowe... | 55 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
__lowercase : Dict = logging.get_logger(__name__)
__lowercase : str = ... | 318 | 0 |
def a__ ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_len... | 303 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProces... | 318 | 0 |
"""simple docstring"""
from __future__ import annotations
lowercase__ : Tuple = list[list[int]]
# assigning initial values to the grid
lowercase__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, ... | 264 |
'''simple docstring'''
from itertools import permutations
def lowercase_ ( _lowercase ) -> bool:
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
l... | 318 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase = {
'''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
... | 188 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 318 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase : int = logging.get_logger(__name__)
__lowercase : Li... | 27 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging... | 318 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class _lowerCamelCase ( _lowercase ):
"""simple... | 186 |
'''simple docstring'''
import numpy
class __lowercase :
def __init__(self , A , A ):
lowerCamelCase_ : Optional[int] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is t... | 318 | 0 |
"""simple docstring"""
from __future__ import annotations
def a_ ( lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = False , ):
UpperCAmelCase__ = cipher_alphabet or [chr(_lowercase ) for i in range(9_7 , 1_2_3... | 98 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokenize... | 318 | 0 |
"""simple docstring"""
class lowercase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int ):
_A = None
_A... | 315 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__lowercase : str = Lock()
def lowercase_ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _... | 318 | 0 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class a ( _lo... | 220 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if ver... | 318 | 0 |
"""simple docstring"""
A = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
A... | 160 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : str = logging.get_logger(__name__)
__lowercase : Tuple = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __lowercase ( _lowercas... | 318 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_... | 331 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __lowercase ( tf.keras.layers.Layer ):
def __init__(self , ... | 318 | 0 |
'''simple docstring'''
from math import loga
def __snake_case ( UpperCAmelCase_ : Optional[Any] ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("Input value must be a \'int\' ... | 55 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_r... | 318 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = {}
__SCREAMING_SNAKE_C... | 303 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
Aut... | 318 | 0 |
"""simple docstring"""
def __lowercase ( _a , _a ):
snake_case_ : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case_ : Dict = n - k
# Calculate C(n,k)
for i in range(_lowercase ):
result *= n - i
... | 264 |
'''simple docstring'''
from __future__ import annotations
import time
__lowercase : List[Any] = list[tuple[int, int]]
__lowercase : List[Any] = [
[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... | 318 | 0 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __magic_name__ ( _lowercase , _lowercase ):
'''simpl... | 188 |
'''simple docstring'''
import numpy as np
def lowercase_ ( _lowercase ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowercase_ ( _lowercase ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(_lowercase )
if __nam... | 318 | 0 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__lowercase : Union[str, Any] = '''naver-clova-ix/donut-base'''
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
... | 27 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImagePro... | 318 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
UpperCamelCase = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if isinstance(_lowercase , np.ndarray ):
return list(tensor.shape ... | 186 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( _lowercase ) -> list[int]: # This function is recursive
'''simple docstring'''
lowerCamelCase_ : Tuple = len(_lowercase )
# If the array contains only one element, we return it (it's the stop c... | 318 | 0 |
"""simple docstring"""
lowerCAmelCase__ : Any = 8.314_4598
def a_ ( lowerCamelCase , lowerCamelCase ):
if temperature < 0:
raise Exception('Temperature cannot be less than 0 K' )
if molar_mass <= 0:
raise Exception('Molar mass cannot be less tha... | 98 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__lowercase : Dict = logging.get_logger(__name__)
class __lowercase ( _lowercase ):
def __init__(self , *A , **A ):
warnings.warn(
... | 318 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 315 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
__lowercase : Optional[Any] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__lowercase : Any = ... | 318 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _SCREAMING_SNAKE_CASE ( __snake_case : Dict , __snake_case : int , __snake_case : Optional[int] , __snake_case : List[str] , ):
... | 220 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
... | 318 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See a... | 160 |
'''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... | 318 | 0 |
'''simple docstring'''
lowerCAmelCase :int = 9.8_06_65
def lowerCamelCase ( lowerCAmelCase : Dict , lowerCAmelCase : int , lowerCAmelCase : int = g ):
"""simple docstring"""
if fluid_density <= 0:
raise ValueError('Impossible fluid density' )
if volume < 0:
rais... | 331 |
'''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 t... | 318 | 0 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : Optional[Any] ):
assert (
isinstance(_lowercase , _lowercase ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
... | 55 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
__lowercase : Dict = logging.get_logger(__name__)
__lowercase : str = ... | 318 | 0 |
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.utils import write_basic_c... | 303 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProces... | 318 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 264 |
'''simple docstring'''
from itertools import permutations
def lowercase_ ( _lowercase ) -> bool:
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
l... | 318 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert im... | 188 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 318 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RE... | 27 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging... | 318 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acc... | 186 |
'''simple docstring'''
import numpy
class __lowercase :
def __init__(self , A , A ):
lowerCamelCase_ : Optional[int] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is t... | 318 | 0 |
"""simple docstring"""
import numpy as np
def a_ ( lowerCamelCase ):
return 1 / (1 + np.exp(-vector ))
def a_ ( lowerCamelCase ):
return vector * sigmoid(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 98 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokenize... | 318 | 0 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchF... | 315 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__lowercase : str = Lock()
def lowercase_ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _... | 318 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImag... | 220 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if ver... | 318 | 0 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
A ... | 160 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : str = logging.get_logger(__name__)
__lowercase : Tuple = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __lowercase ( _lowercas... | 318 | 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
lowerCAmelCase :List[Any] = logging.get_lo... | 331 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __lowercase ( tf.keras.layers.Layer ):
def __init__(self , ... | 318 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
a_ : Any = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv pr... | 55 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_r... | 318 | 0 |
import math
import flax.linen as nn
import jax.numpy as jnp
def a__ ( snake_case , snake_case , snake_case = 1 , snake_case = 1 , snake_case = 1.0E4 , snake_case = False , snake_case = 1.0 , ):
"""simple docstring"""
assert timesteps.ndi... | 303 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
Aut... | 318 | 0 |
"""simple docstring"""
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'''pipelines_utils''',
'''0.22.0''',
'''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from dif... | 264 |
'''simple docstring'''
from __future__ import annotations
import time
__lowercase : List[Any] = list[tuple[int, int]]
__lowercase : List[Any] = [
[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... | 318 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 188 |
'''simple docstring'''
import numpy as np
def lowercase_ ( _lowercase ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowercase_ ( _lowercase ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(_lowercase )
if __nam... | 318 | 0 |
'''simple docstring'''
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers... | 27 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImagePro... | 318 | 0 |
import operator
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = None ):
A_ : Optional[Any] = operator.lt if reverse else operator.gt
A_ : Optional[int] = solution or []
if not arr:
return solution
... | 186 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( _lowercase ) -> list[int]: # This function is recursive
'''simple docstring'''
lowerCamelCase_ : Tuple = len(_lowercase )
# If the array contains only one element, we return it (it's the stop c... | 318 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ : List[str] = {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 98 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__lowercase : Dict = logging.get_logger(__name__)
class __lowercase ( _lowercase ):
def __init__(self , *A , **A ):
warnings.warn(
... | 318 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
A: ... | 319 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing impor... | 319 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase ) -> list:
A: str = len(__lowercase )
for i in range(1 , __lowercase ):
A: str = collection[i]
A: Tuple = 0
A: ... | 319 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetCon... | 319 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase = 1 , __lowercase = 1_0_0_0 ) -> int:
A: Any = 1
A: Optional[Any] = 0
for divide_by_number in range(__lowercase , digit + 1 ):
A: li... | 319 |
'''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... | 319 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json'''... | 319 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''Yi... | 319 | 1 |
'''simple docstring'''
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class lowerCAmelCase_ :
'''simple docstring'''
UpperCamelCase_ : int = None
def _snake_case ... | 319 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE( __lowercase ) -> bool:
if len(__lowercase ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
... | 319 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.conf... | 319 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTe... | 319 | 1 |
'''simple docstring'''
from math import sqrt
def SCREAMING_SNAKE_CASE( __lowercase ) -> int:
A: Dict = 0
for i in range(1 , int(sqrt(__lowercase ) + 1 ) ):
if n % i == 0 and i != sqrt(__lowercase ):
tota... | 319 |
'''simple docstring'''
import heapq
import sys
import numpy as np
UpperCamelCase = tuple[int, int]
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : List[Any] ) -> str:
'''simple ... | 319 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase = {
'''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioC... | 319 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase = 1 , __lowercase = 1_0_0_0 ) -> int:
A: Any = 1
A: Optional[Any] = 0
for divide_by_number in range(__lowercase , digit + 1 ):
A: li... | 319 | 1 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing impor... | 319 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_vision_encoder_decoder''': ['''VisionEn... | 319 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
UpperCamelCase = False
UpperCamelCase = True
UpperCamelCase = False
if __name__ ==... | 319 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase=None , **__lowercase ) -> Any:
A: Any = [x.strip() for x in open(__lowercase ... | 319 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list:
if len(__lowercase ) != 2 or len(a[0] ) != 2 or len(__lowercase ) != 2 or len(b[0] ) != 2:
raise Excepti... | 319 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = 0 ) -> list:
A: Dict = length or len(__lowercase )
A: Dict = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]... | 319 | 1 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
Distil... | 319 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasonin... | 319 | 1 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE( __lowercase = 1_0_0 ) -> int:
A: List[Any] = sum(i * i for i in range(1 , n + 1 ) )
A: Dict = int(math.pow(sum(range(1 , n + 1 ) ) , ... | 319 |
'''simple docstring'''
from itertools import permutations
def SCREAMING_SNAKE_CASE( __lowercase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
... | 319 | 1 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from dif... | 319 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
... | 319 | 1 |
'''simple docstring'''
import functools
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> int:
# Validation
if not isinstance(__lowercase , __lowercase ) or not all(isinstance(__lowercase , __lowercase ) for day in days ... | 319 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase ) -> int:
if not isinstance(__lowercase , __lowercase ):
raise TypeError('''only integers accepted as input''' )
else:
A: str = str(abs(__lowercase ) ... | 319 | 1 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( ... | 319 |
'''simple docstring'''
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list:
if len(__lowercase ) != 2 or len(a[0] ) != 2 or len(__lowercase ) != 2 or len(b[0] ) != 2:
raise Excepti... | 319 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetCon... | 319 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
... | 319 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
... | 319 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPP... | 319 | 1 |
'''simple docstring'''
import heapq
import sys
import numpy as np
UpperCamelCase = tuple[int, int]
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : List[Any] ) -> str:
'''simple ... | 319 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme impor... | 319 | 1 |
'''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()
ex... | 319 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
def __... | 319 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ["""flax"""]
def __init__( self : ... | 319 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCamelCase = '''
import os
'''
UpperCamelCase = '''
def foo():
import os
return False
'''
UpperCamelCase = '''
def foo():
def bar():
... | 319 | 1 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> set[str]:
A , A: Dict = set(__lowercase ), [start]
while stack:
A: List[str] = stack.pop()
... | 319 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTex... | 319 | 1 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from d... | 319 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing impor... | 319 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.ut... | 319 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetCon... | 319 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def SCREAMING_SNAKE_CASE( __lowercase ) -> str:
if "model" in orig_key:
A: List[Any] = orig_key.replace('''model.''' , '''''' ... | 319 |
'''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... | 319 | 1 |
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_V... | 319 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''Yi... | 319 | 1 |
'''simple docstring'''
UpperCamelCase = {str(digit): digit**5 for digit in range(10)}
def SCREAMING_SNAKE_CASE( __lowercase ) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(__lowercase ) )
def SCREAMING_SNAKE_CASE( ) -> int:
... | 319 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE( __lowercase ) -> bool:
if len(__lowercase ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
... | 319 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from to... | 319 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTe... | 319 | 1 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Any ) -> List[Any]:
'''sim... | 319 |
'''simple docstring'''
import heapq
import sys
import numpy as np
UpperCamelCase = tuple[int, int]
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : List[Any] ) -> str:
'''simple ... | 319 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = 0 ) -> list:
A: Dict = length or len(__lowercase )
A: Dict = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]... | 319 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase = 1 , __lowercase = 1_0_0_0 ) -> int:
A: Any = 1
A: Optional[Any] = 0
for divide_by_number in range(__lowercase , digit + 1 ):
A: li... | 319 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = True , __lowercase = math.inf , __lowercase = -math.inf , __lowercase = math.inf ... | 319 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_vision_encoder_decoder''': ['''VisionEn... | 319 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torc... | 319 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase=None , **__lowercase ) -> Any:
A: Any = [x.strip() for x in open(__lowercase ... | 319 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase = 4_0_0_0_0_0_0 ) -> int:
A: Union[str, Any] = []
A , A: Union[str, Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__lowercase )
... | 319 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = 0 ) -> list:
A: Dict = length or len(__lowercase )
A: Dict = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]... | 319 | 1 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = 0.0 , __lowercase = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main... | 319 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasonin... | 319 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
... | 319 |
'''simple docstring'''
from itertools import permutations
def SCREAMING_SNAKE_CASE( __lowercase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
... | 319 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
... | 319 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
... | 319 | 1 |
'''simple docstring'''
from typing import Any
def SCREAMING_SNAKE_CASE( __lowercase ) -> list[Any]:
if not input_list:
return []
A: Dict = [input_list.count(__lowercase ) for value in input_list]
A: Dict = max(__lowercase... | 319 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase ) -> int:
if not isinstance(__lowercase , __lowercase ):
raise TypeError('''only integers accepted as input''' )
else:
A: str = str(abs(__lowercase ) ... | 319 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
UpperCame... | 319 |
'''simple docstring'''
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list:
if len(__lowercase ) != 2 or len(a[0] ) != 2 or len(__lowercase ) != 2 or len(b[0] ) != 2:
raise Excepti... | 319 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCamelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Optional... | 319 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
... | 319 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def SCREAMING_SNAKE_CASE( __lowercase ) ... | 319 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPP... | 319 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
... | 319 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme impor... | 319 | 1 |
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