code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
... | 37 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"g... | 37 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : Union[str, Any] ... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
try:
__UpperCAmelCase : Union[str, Any] = float(_UpperCAmelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
__UpperCAmelCase : str = decimal - int(_U... | 37 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( _UpperCAmelCase ):
__UpperCAmelCase : Dict = 0.00
__UpperCAmelCase : Dict = 0
for resistor in resistors:
if resistor <= 0:
__UpperCAmelCase : Optional[Any] ... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
__UpperCAmelCase : List[str] = 4
__UpperCAmelCase : int = (1 << p) - 1
for _ in range(p - ... | 37 | 1 |
'''simple docstring'''
import argparse
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 accele... | 37 |
'''simple docstring'''
from collections.abc import Callable
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : float = a
__UpperCAmelCase : float = b
if function(_UpperCAmelCase ) == 0: # one of the a or... | 37 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue... | 37 |
'''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 Toke... | 37 | 1 |
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
lowerCAmelCase__ : Optional[int] = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102... | 37 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def __UpperCamelCase ( _UpperCAmelCase ):
return word_by_signature[signatu... | 37 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
__UpperCAmelCase : Optional[int] = len(_UpperCAmelCase )
for i in range(1, _UpperCAmelCase ):
__UpperCAmelCase : int = collection[i]
__UpperCAmelCase : Optional[int] ... | 37 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurat... | 37 | 1 |
'''simple docstring'''
from copy import deepcopy
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase_ : list[int] | None = None , UpperCAmelCase_ : int | None = None ):
"""simpl... | 37 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( _UpperCAmelCase ):
if not nums:
raise ValueError("List is empty" )
return sum(_UpperCAmelCase ) / len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 37 | 1 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_... | 37 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnod... | 37 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_u... | 37 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''M-CLIP'''
def __init__( self : List[str] ,... | 37 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"g... | 37 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase_ : Optional[int]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
__UpperCAmelCase :... | 37 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = position
__UpperCAmelCase : Union[str, Any] = [
(y + 1, x + 2),
(y - ... | 37 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowerCAmelCase__ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __UpperCamelCase ( ):
... | 37 | 1 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lo... | 37 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, ):
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = grid.shape
__UpperCAmelCase : List[... | 37 | 1 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Dict , ... | 37 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_ber... | 37 | 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_retribert import RetriBertTokenizer
lowerCAmelCase__ : Optional[Any] = l... | 37 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 37 | 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_av... | 37 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _UpperCAmelCase ):
... | 37 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : Union[str, Any] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelC... | 37 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMix... | 37 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ : Any = logging.get_logger(__name__)
def __UpperCamelCase ( _UpperCAmelCase ):... | 37 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
lowerCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
... | 37 | 1 |
'''simple docstring'''
import numpy as np
import qiskit
def __UpperCamelCase ( _UpperCAmelCase = 8, _UpperCAmelCase = None ):
__UpperCAmelCase : Union[str, Any] = np.random.default_rng(seed=_UpperCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So... | 37 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
... | 37 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowerCAmelCase__ : Any = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"... | 37 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 37 | 1 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffus... | 37 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 37 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : str = logging.get_logger(__name__)
lowerCAmelCase__ : Union[str, Any] = {
"google/pix2struct-textcaps-base": ... | 37 |
'''simple docstring'''
from __future__ import annotations
import math
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if not scores:
raise ValueErro... | 37 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowerCAmelCase__ : Optional[int] = False
class SCREAMING_SNAKE... | 37 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"g... | 37 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : str = logging.get_logger(__name__)
lowerCAmelCase__ : str = {
"nielsr/canine-s": 20_48,
}
# Unicode ... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
try:
__UpperCAmelCase : Union[str, Any] = float(_UpperCAmelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
__UpperCAmelCase : str = decimal - int(_U... | 37 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.u... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
__UpperCAmelCase : List[str] = 4
__UpperCAmelCase : int = (1 << p) - 1
for _ in range(p - ... | 37 | 1 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransfo... | 37 |
'''simple docstring'''
from collections.abc import Callable
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : float = a
__UpperCAmelCase : float = b
if function(_UpperCAmelCase ) == 0: # one of the a or... | 37 | 1 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
lowerCAmelCase__ : Tuple = ""
lowerCAmelCase__ : Optional[int] = ""
lowerCAmelCase__ : int = ""
lowerCAmelCase__ : str = ""
def __UpperCamelCase ( _UpperCAmelCase ):
# auth... | 37 |
'''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 Toke... | 37 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ : str = logging.get_logger(__name__)
lowerCAmelCase__ : List[Any] = ... | 37 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def __UpperCamelCase ( _UpperCAmelCase ):
return word_by_signature[signatu... | 37 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowerCAmelCase__ : str = 50_00_00
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = os.path.split(__file__)
lowerCAmelCa... | 37 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurat... | 37 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
__UpperCAmelCase : Any = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 37 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( _UpperCAmelCase ):
if not nums:
raise ValueError("List is empty" )
return sum(_UpperCAmelCase ) / len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 37 | 1 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
lowerCAmelCase__ : Tuple = datasets.logging.get_logger(__name__)
lowerCAmelCase__ : Tuple = "\\n@inproceedings{bleurt,\n title={BLEURT: Learning Ro... | 37 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnod... | 37 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_ta... | 37 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''M-CLIP'''
def __init__( self : List[str] ,... | 37 | 1 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase__ : Tuple = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def __UpperCamelCase ( ):
__UpperCAmelCase : List[str] = os.path.dirname(os.path.realpath(_UpperCAmelCase ... | 37 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase_ : Optional[int]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
__UpperCAmelCase :... | 37 | 1 |
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
lowerCAmelCase_... | 37 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowerCAmelCase__ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __UpperCamelCase ( ):
... | 37 | 1 |
'''simple docstring'''
import math
def __UpperCamelCase ( _UpperCAmelCase = 100 ):
__UpperCAmelCase : List[Any] = sum(i * i for i in range(1, n + 1 ) )
__UpperCAmelCase : List[Any] = int(math.pow(sum(range(1, n + 1 ) ), 2 ) ... | 37 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, ):
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = grid.shape
__UpperCAmelCase : List[... | 37 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_token... | 37 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_ber... | 37 | 1 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnod... | 37 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 37 | 1 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[int] ... | 37 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _UpperCAmelCase ):
... | 37 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase = 50 ):
__UpperCAmelCase : Optional[int] = [1] * (length + 1)
for row_length in range(3, length + 1 ):
for block_length in range(3, row_length + 1 ):
for block_start in range(row_length - bloc... | 37 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMix... | 37 | 1 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
fr... | 37 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
lowerCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
... | 37 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_comm... | 37 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
... | 37 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
return round(float(moles / volume ) * nfactor )
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
return round(float((moles * 0.0_821 * temperat... | 37 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 37 | 1 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, ):
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = grid.shape
__UpperCAmelCase : List[... | 37 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 37 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase__ : Optional[... | 37 |
'''simple docstring'''
from __future__ import annotations
import math
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if not scores:
raise ValueErro... | 37 | 1 |
'''simple docstring'''
import os
from math import logaa
def __UpperCamelCase ( _UpperCAmelCase = "base_exp.txt" ):
__UpperCAmelCase : float = 0
__UpperCAmelCase : Any = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(_UpperCAmelCase ), _... | 37 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"g... | 37 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = None
SCREAMI... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
try:
__UpperCAmelCase : Union[str, Any] = float(_UpperCAmelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
__UpperCAmelCase : str = decimal - int(_U... | 37 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class SCREAMING_SNAKE_CASE__ ( snake_case__ ... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
__UpperCAmelCase : List[str] = 4
__UpperCAmelCase : int = (1 << p) - 1
for _ in range(p - ... | 37 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identif... | 37 |
'''simple docstring'''
from collections.abc import Callable
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : float = a
__UpperCAmelCase : float = b
if function(_UpperCAmelCase ) == 0: # one of the a or... | 37 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ : Any = logging.get_logger(__name__)
lowerCAmelCase__ : Optional[int] ... | 37 |
'''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 Toke... | 37 | 1 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmel... | 37 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def __UpperCamelCase ( _UpperCAmelCase ):
return word_by_signature[signatu... | 37 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['''keras_nlp''']
def __init__( self : Dict , *UpperCAmelCase_ : ... | 37 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurat... | 37 | 1 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
de... | 37 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( _UpperCAmelCase ):
if not nums:
raise ValueError("List is empty" )
return sum(_UpperCAmelCase ) / len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 37 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ( snake_... | 37 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnod... | 37 | 1 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Any = logging.get_logger(__name__)
# TODO Update this
lowerCAmelCase__ : Optional[int] = {... | 37 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''M-CLIP'''
def __init__( self : List[str] ,... | 37 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
__UpperCAmelCase : List[str] = 4
__UpperCAmelCase : int = (1 << p) - 1
for _ in range(p - ... | 37 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase_ : Optional[int]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
__UpperCAmelCase :... | 37 | 1 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase__ : Tuple = Lock()
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmel... | 37 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowerCAmelCase__ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __UpperCamelCase ( ):
... | 37 | 1 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_a... | 37 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, ):
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = grid.shape
__UpperCAmelCase : List[... | 37 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ : Any = {
"configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHI... | 37 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_ber... | 37 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase__ : Union[str, ... | 37 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 37 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (DDPMScheduler,)
def lowerCamelCase_ ( ... | 37 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _UpperCAmelCase ):
... | 37 | 1 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
def __init__( self : ... | 37 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMix... | 37 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wava... | 37 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
lowerCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
... | 37 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase = 1000 ):
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = 1, 1
__UpperCAmelCase : Any = []
for i in range(1, n + 1 ):
__UpperCAmelCase : Union[str, Any] = ... | 37 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
... | 37 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase = False ):
if not arr:
return 0
__UpperCAmelCase : Union[str, Any] = 0 if allow_empty_subarrays else float("-inf" )
__UpperCAmelCase : Option... | 37 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 37 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__UpperCAmelCase : int = gray_code_seque... | 37 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 37 | 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():
from .t... | 37 |
'''simple docstring'''
from __future__ import annotations
import math
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if not scores:
raise ValueErro... | 37 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.u... | 37 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"g... | 37 | 1 |
'''simple docstring'''
import os
from distutils.util import strtobool
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase ):
for e in env_keys:
__UpperCAmelCase : Optional[int] = int(os.environ.get(_UpperCAmelCase, -1 ) )
if val >= 0:
return val
... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
try:
__UpperCAmelCase : Union[str, Any] = float(_UpperCAmelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
__UpperCAmelCase : str = decimal - int(_U... | 37 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase__ : Dict = {
"voc... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
__UpperCAmelCase : List[str] = 4
__UpperCAmelCase : int = (1 << p) - 1
for _ in range(p - ... | 37 | 1 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase_ : list[list[float]] , UpperCAmelCase_ : list[int] ):
"""simple docs... | 37 |
'''simple docstring'''
from collections.abc import Callable
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : float = a
__UpperCAmelCase : float = b
if function(_UpperCAmelCase ) == 0: # one of the a or... | 37 | 1 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logg... | 37 |
'''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 Toke... | 37 | 1 |
'''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 VaeImagePr... | 37 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def __UpperCamelCase ( _UpperCAmelCase ):
return word_by_signature[signatu... | 37 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __UpperCamelCase ( _UpperCAmelCase ):
if "cls_token" in name:
__UpperCAmelCase : Tuple = name.repla... | 37 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurat... | 37 | 1 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase=1024, _UpperCAmelCase=1024, _UpperCAmelCase=... | 37 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( _UpperCAmelCase ):
if not nums:
raise ValueError("List is empty" )
return sum(_UpperCAmelCase ) / len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 37 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_... | 37 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnod... | 37 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def __UpperCamelCase ( ):
__UpperCAmelCase : dict[int, int] = {}
__UpperCAmelCase : Optional[Any] = 2
while True:
__UpperCAmelCase : Tuple = fac... | 37 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''M-CLIP'''
def __init__( self : List[str] ,... | 37 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transfo... | 37 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase_ : Optional[int]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
__UpperCAmelCase :... | 37 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
lowerCAmelCase__ : Optional[Any] = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNA... | 37 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowerCAmelCase__ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __UpperCamelCase ( ):
... | 37 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase__ : Optional[Any] = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-... | 37 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, ):
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = grid.shape
__UpperCAmelCase : List[... | 37 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ : Dict = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIV... | 37 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_ber... | 37 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
__UpperCAmelCase : int = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __UpperCamelCase ( _UpperCAmelCase = 100 ):
__UpperCAmelCase : Any = 1
__Upp... | 37 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 37 | 1 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 37 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _UpperCAmelCase ):
... | 37 | 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_ARCHIVE... | 37 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMix... | 37 | 1 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transf... | 37 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
lowerCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
... | 37 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _UpperCAmelCase ):
... | 37 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
... | 37 | 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/LICENSE-2... | 37 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 37 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_av... | 37 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 37 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
lowerCAmelCase__ : Any ... | 37 |
'''simple docstring'''
from __future__ import annotations
import math
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if not scores:
raise ValueErro... | 37 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ : Dict ... | 37 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"g... | 37 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
try:
__UpperCAmelCase : Union[str, Any] = float(_UpperCAmelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
__UpperCAmelCase : str = decimal - int(_U... | 37 | 1 |
'''simple docstring'''
import torch
from transformers import AutoModel
class SCREAMING_SNAKE_CASE__ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : int , UpperCAmelCase_ : Any="sayef/fsner-bert-base-uncased" ):
"""s... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
__UpperCAmelCase : List[str] = 4
__UpperCAmelCase : int = (1 << p) - 1
for _ in range(p - ... | 37 | 1 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transf... | 37 |
'''simple docstring'''
from collections.abc import Callable
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : float = a
__UpperCAmelCase : float = b
if function(_UpperCAmelCase ) == 0: # one of the a or... | 37 | 1 |
'''simple docstring'''
from math import pow
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, ):
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
re... | 37 |
'''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 Toke... | 37 | 1 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase=None, **_UpperCAmelCase ):
__UpperCAmelCase : Optional[Any] = [x.strip() for x in open(_UpperCAmelCase ).readlines(... | 37 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def __UpperCamelCase ( _UpperCAmelCase ):
return word_by_signature[signatu... | 37 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 37 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurat... | 37 | 1 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionMo... | 37 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( _UpperCAmelCase ):
if not nums:
raise ValueError("List is empty" )
return sum(_UpperCAmelCase ) / len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 37 | 1 |
'''simple docstring'''
import argparse
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 accele... | 37 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnod... | 37 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def __UpperCamelCase ( _UpperCAmelCase ):
return word_by_signature[signatu... | 37 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''M-CLIP'''
def __init__( self : List[str] ,... | 37 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowerCAmelCase__ : Optional[int] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Unde... | 37 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase_ : Optional[int]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
__UpperCAmelCase :... | 37 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transf... | 37 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowerCAmelCase__ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __UpperCamelCase ( ):
... | 37 | 1 |
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