code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def A__ ( self ,A__):
return 0.0
def UpperCamelCase ( lowerCAmelCase_... | 101 |
"""simple docstring"""
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ) -> Tuple:
from torch.utils.cpp_extension import load
A__ = Path(lowercase_ ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
A__ = [
root / filename
... | 247 | 0 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE : str = 2_9979_2458
# Symbols
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict ... | 102 |
"""simple docstring"""
import os
import numpy
import onnx
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Dict:
A__ = a.name
A__ = b.name
A__ = ""
A__ = ""
A__ = a == b
A__ = name_a
A__ = name_b
re... | 247 | 0 |
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.utils import logging
logg... | 103 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_ava... | 247 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import gcd
def _A ( A__ , A__ = 2 , A__ = 1 , A__ = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError('''The input value cannot be less than 2''' )
# Because of the relationsh... | 104 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE = (720, 1280) # Height, Width
SCREAMING_SNAKE_CASE = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAM... | 247 | 0 |
"""simple docstring"""
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __UpperCamelCase ( a__ , a__ ):
@register_to_config
def __init__( sel... | 105 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE = "src/diffusers"
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE = re.... | 247 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=a_ ):
"""simple docstring"""
lowercase__ = ["flax", "transformers"]
def __init__( self : int ,*lowercase_ : List[str] ... | 106 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[str]:
A__ = len(lowercase_ )
while cur > 1:
# Find the maximum number in arr
A__ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A__ = arr[mi::-1] + arr[mi + 1 :... | 247 | 0 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,... | 107 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(lowercase_ , exponent // 2 , lowercase_ ) % modulo_value
return (x * x) % modulo_value
else:
... | 247 | 0 |
"""simple docstring"""
from statistics import mean
import numpy as np
def a__ ( SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
lowerCAmelCa... | 108 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_p... | 247 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : str = [0] * len(UpperCamelCase )
for i in range(1 , len(UpperCamelCase ) ):
# use last results for better performance - dynamic programming
UpperCAmelCase : int = prefix_resu... | 109 |
"""simple docstring"""
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , **lowercase_ ) -> List[Any]:
A__ = AutoConfig.from_pretrained(lowercase_ , **lowercase_ )
A__ ... | 247 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/... | 336 |
"""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_MAPPI... | 247 | 0 |
from __future__ import annotations
import bisect
def UpperCamelCase__ ( A__ , A__ , A__ = 0 , A__ = -1 ) -> int:
if hi < 0:
snake_case__ : int = len(lowercase_ )
while lo < hi:
snake_case__ : Union[str, Any] = lo + (hi ... | 143 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''feature_extractor''']
lowercase__ = '''TvltImageProcessor'''
lowercase__ = '''TvltFeatureExtracto... | 247 | 0 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import Poly... | 283 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 247 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowerCAmelCa... | 37 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
... | 247 | 0 |
from __future__ import annotations
lowerCamelCase = 10
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : int = 1
lowerCAmelCase__ : Any = max(lowercase_ )
while placement <= max_digi... | 131 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
if length <= 0 or not isinstance(lowercase_ , lowercase_ ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(lowercase_ )]
if __name__ == "__ma... | 247 | 0 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class A__ :
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Dict = data
UpperCamelCase : Tuple = [0x6_7_4_5_2_3_0_1, 0... | 52 |
"""simple docstring"""
import qiskit
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> qiskit.result.counts.Counts:
A__ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
A__ = qiskit.QuantumCircuit(lower... | 247 | 0 |
def lowerCAmelCase__(__snake_case ) -> list:
'''simple docstring'''
lowerCamelCase__ = len(lowercase_ )
for i in range(1 ,lowercase_ ):
lowerCamelCase__ = collection[i]
lowerCamelCase__ = 0
lowerCamelCase__ = i - 1
while low... | 209 |
"""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-... | 247 | 0 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ = True , UpperCAmelCase_ = math.inf , UpperCAmelCase_ = -math.inf , UpperCAmelCase_ = math.inf... | 151 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , ) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif... | 247 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Padding... | 243 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase_ ... | 247 | 0 |
"""simple docstring"""
def lowercase ( A_ )-> List[str]:
'''simple docstring'''
a : List[Any] = len(lowercase_ )
while cur > 1:
# Find the maximum number in arr
a : int = arr.index(max(arr[0:cur] ... | 40 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from acceler... | 247 | 0 |
import unittest
import numpy as np
from datasets import load_dataset
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... | 259 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentP... | 247 | 0 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __UpperCAmelCase ( nn.Module ):
def __init__( self : Any, __A : int = 1_6, __A : int = 8_8, __A : Optional[i... | 336 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 247 | 0 |
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 import TOKEN, USER, get_... | 143 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preproce... | 247 | 0 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.uti... | 283 |
"""simple docstring"""
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ) -> Tuple:
from torch.utils.cpp_extension import load
A__ = Path(lowercase_ ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
A__ = [
root / filename
... | 247 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCAmelCase... | 37 |
"""simple docstring"""
import os
import numpy
import onnx
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Dict:
A__ = a.name
A__ = b.name
A__ = ""
A__ = ""
A__ = a == b
A__ = name_a
A__ = name_b
re... | 247 | 0 |
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 compute_effective_ax... | 131 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_ava... | 247 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowerCamelCase : List[Any] = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-... | 52 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE = (720, 1280) # Height, Width
SCREAMING_SNAKE_CASE = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAM... | 247 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_a = logging.g... | 209 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE = "src/diffusers"
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE = re.... | 247 | 0 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
if n_term == "":
return []
UpperCAmelCase : List[str] = []
for temp in range(int(lowercase_ ) ):
series.append(F"""1/{temp + 1}""" if series else '1' )
return series
if __name__ == "__main__":
lowercase__... | 151 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[str]:
A__ = len(lowercase_ )
while cur > 1:
# Find the maximum number in arr
A__ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A__ = arr[mi::-1] + arr[mi + 1 :... | 247 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
a_ = Path(lowercase_ )
a_ = Path(lowercase_ )
dest_dir.mkdir(exist_ok=lowercase_ )
... | 243 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(lowercase_ , exponent // 2 , lowercase_ ) % modulo_value
return (x * x) % modulo_value
else:
... | 247 | 0 |
"""simple docstring"""
from __future__ import annotations
__lowercase = 8.988e9 # units = N * m^s * C^-2
def lowercase ( A_ , A_ , A_ , A_ )-> dict[str, float]:
'''simple docstring'''
a : Optional[int] = abs... | 40 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_p... | 247 | 0 |
from __future__ import annotations
def _A ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int , ):
UpperCamelCase :List[Any] = len(lo... | 259 |
"""simple docstring"""
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , **lowercase_ ) -> List[Any]:
A__ = AutoConfig.from_pretrained(lowercase_ , **lowercase_ )
A__ ... | 247 | 0 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # s... | 336 |
"""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_MAPPI... | 247 | 0 |
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
lowerCAmelCase__ :... | 143 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''feature_extractor''']
lowercase__ = '''TvltImageProcessor'''
lowercase__ = '''TvltFeatureExtracto... | 247 | 0 |
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
_snake_case = '''tiny-wmt19-en-ru'''
# Build
# borrowed from a test
_snake_... | 283 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 247 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QFormer... | 37 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
... | 247 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCamelCase_ ( ):
"""simple docstring"""
lowerCAmelCase__ : int = {
'''repo_name''': ['''... | 131 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
if length <= 0 or not isinstance(lowercase_ , lowercase_ ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(lowercase_ )]
if __name__ == "__ma... | 247 | 0 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s""",
datefmt="""%m/%d/%Y %H:%M:%S""",
level=logging.INFO,
)
__lowerCamelCase : Optional[int]... | 52 |
"""simple docstring"""
import qiskit
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> qiskit.result.counts.Counts:
A__ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
A__ = qiskit.QuantumCircuit(lower... | 247 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 209 |
"""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-... | 247 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pi... | 151 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , ) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif... | 247 | 0 |
"""simple docstring"""
import functools
from typing import Any
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->bool:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ) or len(lowercase_ ) == 0:
raise ValueError("the string should be not empt... | 243 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase_ ... | 247 | 0 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase ( A_ )-> bool:
'''simple docstring'''
a : Dict = int(number**0.5 )
return number == sq * s... | 40 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from acceler... | 247 | 0 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_avail... | 259 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentP... | 247 | 0 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
_lowerCamelCase : Optional[Any] = "http://www.mocksi... | 336 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 247 | 0 |
lowerCAmelCase__ : Tuple = [0, 2, 4, 6, 8]
lowerCAmelCase__ : Union[str, Any] = [1, 3, 5, 7, 9]
def UpperCamelCase__ ( A__ , A__ , A__ , A__ ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range... | 143 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preproce... | 247 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_... | 283 |
"""simple docstring"""
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ) -> Tuple:
from torch.utils.cpp_extension import load
A__ = Path(lowercase_ ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
A__ = [
root / filename
... | 247 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 37 |
"""simple docstring"""
import os
import numpy
import onnx
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Dict:
A__ = a.name
A__ = b.name
A__ = ""
A__ = ""
A__ = a == b
A__ = name_a
A__ = name_b
re... | 247 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {'''vocab_... | 131 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_ava... | 247 | 0 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def A_ ( ) -> Dict:
raise RuntimeError("CUDA out of memory." )
class A__ ( nn.Module ):
... | 52 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE = (720, 1280) # Height, Width
SCREAMING_SNAKE_CASE = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAM... | 247 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
"funnel-transformer/small-base": "https://huggingface.c... | 209 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE = "src/diffusers"
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE = re.... | 247 | 0 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ =... | 151 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[str]:
A__ = len(lowercase_ )
while cur > 1:
# Find the maximum number in arr
A__ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A__ = arr[mi::-1] + arr[mi + 1 :... | 247 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionContro... | 243 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(lowercase_ , exponent // 2 , lowercase_ ) % modulo_value
return (x * x) % modulo_value
else:
... | 247 | 0 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
fro... | 40 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_p... | 247 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
exce... | 259 |
"""simple docstring"""
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , **lowercase_ ) -> List[Any]:
A__ = AutoConfig.from_pretrained(lowercase_ , **lowercase_ )
A__ ... | 247 | 0 |
from manim import *
class __UpperCAmelCase ( A_ ):
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : Union[str, Any] = Rectangle(height=0.5, width=0.5 )
UpperCAmelCase : Optional[int] = Rectangle(height=0.4... | 336 |
"""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_MAPPI... | 247 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
from ... | 143 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''feature_extractor''']
lowercase__ = '''TvltImageProcessor'''
lowercase__ = '''TvltFeatureExtracto... | 247 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_cas... | 283 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 247 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoi... | 37 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
... | 247 | 0 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _a ( A... | 131 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
if length <= 0 or not isinstance(lowercase_ , lowercase_ ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(lowercase_ )]
if __name__ == "__ma... | 247 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
fro... | 52 |
"""simple docstring"""
import qiskit
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> qiskit.result.counts.Counts:
A__ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
A__ = qiskit.QuantumCircuit(lower... | 247 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCAmelCase__(__snake_case = "" ) -> dict[str, float]:
'''simple docstring'''
lowerCamelCase__ = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
lowerCamelCa... | 209 |
"""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-... | 247 | 0 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
f... | 151 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , ) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif... | 247 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
'configuratio... | 243 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase_ ... | 247 | 0 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynam... | 40 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from acceler... | 247 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( A_ ):
"""simple docstring"""
UpperCamelCase_ : Dict ='Speech2TextFeatureExtractor'
UpperCamelCase_ : Dict ='Speec... | 259 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentP... | 247 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __UpperCAmelCase ( A_ ):... | 336 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 247 | 0 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ : List[str] = '''src/transformers'''
# This is to make sure the transformers mo... | 143 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preproce... | 247 | 0 |
import operator as op
_snake_case = '''scaler.pt'''
_snake_case = '''pytorch_model'''
_snake_case = '''random_states'''
_snake_case = '''optimizer'''
_snake_case = '''scheduler'''
_snake_case = '''pytorch_mod... | 283 |
"""simple docstring"""
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ) -> Tuple:
from torch.utils.cpp_extension import load
A__ = Path(lowercase_ ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
A__ = [
root / filename
... | 247 | 0 |
'''simple docstring'''
import sys
from pathlib import Path
_lowerCAmelCase = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
f... | 37 |
"""simple docstring"""
import os
import numpy
import onnx
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Dict:
A__ = a.name
A__ = b.name
A__ = ""
A__ = ""
A__ = a == b
A__ = name_a
A__ = name_b
re... | 247 | 0 |
import torch
def lowerCamelCase_ ( ):
"""simple docstring"""
if torch.cuda.is_available():
lowerCAmelCase__ : Optional[Any] = torch.cuda.device_count()
else:
lowerCAmelCase__ : str = 0
print(f'Successfully ran on ... | 131 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_ava... | 247 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class A__ :
def __init__( self ):
'''simple docstring'''
UpperCamelCase : Any = ""
UpperCamelCase : Union[str, Any] = ""
UpperCamelCase : U... | 52 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE = (720, 1280) # Height, Width
SCREAMING_SNAKE_CASE = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAM... | 247 | 0 |
def lowerCAmelCase__(__snake_case = 1000 ) -> int:
'''simple docstring'''
lowerCamelCase__ = 2**power
lowerCamelCase__ = 0
while n:
lowerCamelCase__ , lowerCamelCase__ = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(st... | 209 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE = "src/diffusers"
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE = re.... | 247 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.ja... | 151 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[str]:
A__ = len(lowercase_ )
while cur > 1:
# Find the maximum number in arr
A__ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A__ = arr[mi::-1] + arr[mi + 1 :... | 247 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
}
try:
if not is_to... | 243 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(lowercase_ , exponent // 2 , lowercase_ ) % modulo_value
return (x * x) % modulo_value
else:
... | 247 | 0 |
"""simple docstring"""
import string
def lowercase ( A_ )-> None:
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
a : List[str] = ""
for symbol in message:
if symbol in string.asci... | 40 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_p... | 247 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _A ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE_... | 259 |
"""simple docstring"""
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , **lowercase_ ) -> List[Any]:
A__ = AutoConfig.from_pretrained(lowercase_ , **lowercase_ )
A__ ... | 247 | 0 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_lowerCamelCase : Dict = "src/diffusers"
# Matches is_xxx_available()
_lowerCamelCase : Any = re.compile(... | 336 |
"""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_MAPPI... | 247 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def UpperCamelCase__ ( A__ = True , *A__ , **A__ ) -> int:
if not is_tqdm_available():
raise ImportError('Accelerate\'s `tqdm` module requ... | 143 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''feature_extractor''']
lowercase__ = '''TvltImageProcessor'''
lowercase__ = '''TvltFeatureExtracto... | 247 | 0 |
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Tuple = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 283 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 247 | 0 |
'''simple docstring'''
import math
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase=0 ) -> List[str]: # a graph with Node 0,1,...,N-1
lowerCAmelCase__ : Union[str, Any] = n
lowerCAmelCase__ : Dict = ... | 37 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
... | 247 | 0 |
import numpy as np
def lowerCamelCase_ ( _a ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 131 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
if length <= 0 or not isinstance(lowercase_ , lowercase_ ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(lowercase_ )]
if __name__ == "__ma... | 247 | 0 |
from __future__ import annotations
__lowerCamelCase : Any = """#"""
class A__ :
def __init__( self ):
'''simple docstring'''
UpperCamelCase : str = {}
def __UpperCamelCase( self , A_ ):
'''simple docstrin... | 52 |
"""simple docstring"""
import qiskit
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> qiskit.result.counts.Counts:
A__ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
A__ = qiskit.QuantumCircuit(lower... | 247 | 0 |
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
from ..image_utils impor... | 209 |
"""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-... | 247 | 0 |
'''simple docstring'''
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self : str , lowercase_ : int = 0 ) -> List[Any]:
UpperCAmelCase : int = key
def UpperCAmelCase_ ... | 151 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , ) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif... | 247 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
fr... | 243 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase_ ... | 247 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from... | 40 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from acceler... | 247 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_CO... | 259 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentP... | 247 | 0 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def a__ ( UpperCAmelCase : str ) -> int:
UpperCAmelCase : List[str] = prime_factors(lowercase_ )
if is_square_free(lowercase_ ):
return -1 if len(lowercase_ ) % 2 else 1
... | 336 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 247 | 0 |
def UpperCamelCase__ ( A__ , A__ , A__ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case__ : Any = _modexpt(lowercase_ , exponent // 2 , lowercase_ ) % modulo_value
return (x * x) % modulo_value
else:
... | 143 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preproce... | 247 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCAmelCase_ ( A_ ):
'''simple docstring'''
def __init__( self , __A , __A , __A ):
"""simple docstr... | 283 |
"""simple docstring"""
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ) -> Tuple:
from torch.utils.cpp_extension import load
A__ = Path(lowercase_ ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
A__ = [
root / filename
... | 247 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ): # noqa: E741
"""simple docstring"""
lowerCAmelCase__ : List[Any] = len(lowercase_ )
lowerCAmelCase__ : Union[str, Any] = 0
lowerCAmelCase__ : int = [0] ... | 37 |
"""simple docstring"""
import os
import numpy
import onnx
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Dict:
A__ = a.name
A__ = b.name
A__ = ""
A__ = ""
A__ = a == b
A__ = name_a
A__ = name_b
re... | 247 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase = {
'''configuration_vision_text_dual_encoder''': ['''VisionTextDualEncoderConfig'''],
'''... | 131 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_ava... | 247 | 0 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"""{price_plus_tax(100, 0.2_5) = }""")
print(f"""{price_plus_tax(1_2_5.5_0, 0.0_5) = }""")
| 52 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE = (720, 1280) # Height, Width
SCREAMING_SNAKE_CASE = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAM... | 247 | 0 |
def lowerCAmelCase__(__snake_case ) -> Optional[int]:
'''simple docstring'''
for i in range(0 ,lowercase_ ):
for _ in range(0 ,n - i - 1 ): # printing spaces
print(''' ''' ,end='''''' )
for _ in range(0 ,i + 1 ): # printing sta... | 209 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE = "src/diffusers"
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE = re.... | 247 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
... | 151 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[str]:
A__ = len(lowercase_ )
while cur > 1:
# Find the maximum number in arr
A__ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A__ = arr[mi::-1] + arr[mi + 1 :... | 247 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRob... | 243 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(lowercase_ , exponent // 2 , lowercase_ ) % modulo_value
return (x * x) % modulo_value
else:
... | 247 | 0 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def lowercase ( A_ )-> Union[str, Any]:
'''simple docstring'''
a : int = tf.convert_to_tensor(lowercase_ )
a : Union[str, Any... | 40 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_p... | 247 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple ... | 259 |
"""simple docstring"""
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , **lowercase_ ) -> List[Any]:
A__ = AutoConfig.from_pretrained(lowercase_ , **lowercase_ )
A__ ... | 247 | 0 |
class __UpperCAmelCase :
def __init__( self : List[Any], __A : list ):
UpperCAmelCase : str = set_counts
UpperCAmelCase : Union[str, Any] = max(snake_case_ )
UpperCAmelCase : str = len(snake_case_ )
Upp... | 336 |
"""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_MAPPI... | 247 | 0 |
def UpperCamelCase__ ( A__ ) -> list[int]:
if num <= 0:
raise ValueError('Input must be a positive integer' )
snake_case__ : Union[str, Any] = [True] * (num + 1)
snake_case__ : Optional[int] = 2
while p * p <= num:
... | 143 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''feature_extractor''']
lowercase__ = '''TvltImageProcessor'''
lowercase__ = '''TvltFeatureExtracto... | 247 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 283 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 247 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.