code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_avail... | 77 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transf... | 77 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDif... | 77 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 | 1 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class a__ ( unittest.TestCase ):
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[int] ... | 77 |
"""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 Ba... | 77 | 1 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in ... | 77 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "att... | 77 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
A = loggi... | 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 1 |
"""simple docstring"""
import requests
A = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def _UpperCamelCase ( UpperCamelCase ) -> None:
"""simple docstring"""
# fetching a list of articles in json format
__UpperCAmelCase : U... | 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive co... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("String lengths must match!" )
__UpperCAmelCase : str ... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet ... | 77 |
"""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 ... | 77 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in ... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
A = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
A = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def _UpperCamelCase ( UpperCamelCase ) -> list[float]:
"""simple docstr... | 77 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
if index == r:
for j in range... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokeni... | 77 | 1 |
"""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():
fr... | 77 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 77 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 77 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str,... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Tuple:
"""simple docstring"""
# Checks if the entire collection has been sorted
if len(UpperCamelCase ) <= 1 or n <= 1:
return
in... | 77 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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
... | 77 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( ) -> int:
"""simple docstring"""
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(UpperCamelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]... | 77 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_c... | 77 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 77 | 1 |
"""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 import WavaVecaPh... | 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
... | 77 | 1 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# ... | 77 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 | 1 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch... | 77 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelC... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : List[Any] = [1]
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Any = 0, 0, 0
__Upper... | 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", ""... | 77 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 77 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : int = len(UpperCamelCase )
__UpperCAmelCase : int = len(UpperCamelCase )
_... | 77 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
... | 77 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 | 1 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=5 ) -> Optional[Any]:
"""simple docstring"""
# Adap... | 77 |
"""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 Ba... | 77 | 1 |
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def _UpperCamelCase ( UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : List[str] = min(UpperCamelCase ) # min() finds the minimum value
__UpperCAmelCase :... | 77 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "att... | 77 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _UpperCamelCase ( UpperCamelCase = "AAPL" ) -> str:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = f"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
__... | 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
... | 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive co... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
while a != 0:
__UpperCAmelCase , __UpperCAmelCase : str = b % a, a
return b
def _UpperCamelCase ( ... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | 1 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class a__ ( unittest.TestCase ):
def a_ ( self : Any):
"""simple docstring"""
debug_launcher(test_scr... | 77 |
"""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 ... | 77 | 1 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in ... | 77 | 1 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import cl... | 77 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )... | 77 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
A =... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokeni... | 77 | 1 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _UpperCamelCase ( UpperCamelCase = 8 ) -> str:
"""simple docstring"""
__UpperCAmelCase : List[Any] ... | 77 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 77 | 1 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_... | 77 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str,... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
A = list[tuple[int, int]]
A = [
[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, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0,... | 77 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : Any = ""
for word_or_phrase in separated:
if not isinstance(UpperCamelCase , UpperCamelCase... | 77 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Any , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Dict):
"""simple docstring"""
... | 77 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 | 1 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "att... | 77 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 77 | 1 |
"""simple docstring"""
import pytest
A = """__dummy_dataset1__"""
A = """
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_UR... | 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
... | 77 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
"""facebook/convnextv2-tiny-1k-224""": """... | 77 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 | 1 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelC... | 77 | 1 |
"""simple docstring"""
A = """Alexander Joslin"""
import operator as op
from .stack import Stack
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[int] = {"*": op.mul, "/": op.truediv, "+":... | 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", ""... | 77 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 | 1 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import ... | 77 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionP... | 77 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 | 1 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__ ( nn.Module ):
def __init__( self : Any , UpperCamelCase_ : int = 16 , UpperCamelCase_ : int = 88 , UpperCam... | 77 |
"""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 Ba... | 77 | 1 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAP... | 77 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "att... | 77 | 1 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
A = HfApi()
A = {}
# fmt: off
A = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0... | 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
class a__ ( __magic_name__ , __magic_name... | 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive co... | 77 | 1 |
"""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, ... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 77 |
"""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 ... | 77 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeSeriesTransforme... | 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in ... | 77 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
A = """"""
A = """"""
A = """"""
A = 1 # (0 is vertical, 1 is horizontal)
def _UpperCamelCase ( ) -> None:
"""simple docstring"... | 77 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )... | 77 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokeni... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
return number | (1 << position)
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple do... | 77 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 77 | 1 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
A = logging.get_logger(__name__)
A = """https://op... | 77 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str,... | 77 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : List[Any] = [
... | 77 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 | 1 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _UpperCamelCase ( ... | 77 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[str]: # noqa: E741
"""simple docstring"""
while r - l > 1:
__UpperCAmelCase ... | 77 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = 100 , ) -> float:
"""simple docstring"""
__UpperC... | 77 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 77 | 1 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCH... | 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
... | 77 | 1 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class ... | 77 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 | 1 |
"""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():
fr... | 77 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelC... | 77 | 1 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers i... | 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", ""... | 77 | 1 |
"""simple docstring"""
import sys
def _UpperCamelCase ( UpperCamelCase ) -> Tuple:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = len(UpperCamelCase )
__UpperCAmelCase : str = [[0 for x in range(UpperCamelCa... | 77 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 | 1 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = sum(i * i for i in range(1 , n + 1 ) )
__UpperCAmelCase : Union[str, Any] =... | 77 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 | 1 |
"""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 AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils i... | 77 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
A = []
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> bool:
"""simple docstring"""
for i in range(len(UpperCamelCase ) ):
if board[row][i] == 1... | 77 |
"""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 Ba... | 77 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, ... | 77 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "att... | 77 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class a__ ( unittest.TestCase ):
lowercase_ = inspec... | 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 1 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive co... | 77 | 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()
except OptionalDep... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
A = logging.get_logger(__name__)
A = ... | 77 |
"""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 ... | 77 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
A = logging.get_logger(__name__)
A = {"... | 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in ... | 77 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generati... | 77 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )... | 77 | 1 |
"""simple docstring"""
A = 0 # The first color of the flag.
A = 1 # The second color of the flag.
A = 2 # The third color of the flag.
A = (red, white, blue)
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
if n... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokeni... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_t... | 77 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 77 | 1 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class a__ :
def __init__( self : Optional[Any] , UpperCamelCase_ : List[str]):
"""simple docstring"""
__UpperCAmelCase : Tuple = str(id_)
__U... | 77 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str,... | 77 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
A = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def _... | 77 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.... | 77 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 | 1 |
"""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
fr... | 77 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 77 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeech... | 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
__UpperCAmelCase : List[Any] = 4
__UpperCAme... | 77 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : List[Any] = len(UpperCamelCase )
for _ in range(UpperCamelCase ):
for i in range(_ % 2 , arr_size - 1 , ... | 77 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelC... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation a... | 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", ""... | 77 | 1 |
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
A = 6378137.0
A = 6356752.314245
A = 6_378_137
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , ... | 77 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
A = list[list[int]]
# assigning initial values to the grid
A = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
... | 77 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 | 1 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )... | 77 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 77 |
"""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 Ba... | 77 | 1 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "att... | 77 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config... | 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 1 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive co... | 77 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...t... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> float:
"""simple docstring"""
__UpperCAmelCase : List[Any] = 0
while len(UpperCamelCase ) > 1:
__UpperCAmelCase : Optional[int] = 0
# Consider... | 77 |
"""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 ... | 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : list[list[str]] = [[] for _ in range(UpperCamelCase )]
__UpperCAmelCase : Union[str, Any] ... | 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in ... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
if len(UpperCamelCase ) != 2 or len(a[0] ) != 2 or len(UpperCamelCase ) != 2 or len(b[0] ) ... | 77 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )... | 77 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalD... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokeni... | 77 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.