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 |
|---|---|---|---|---|
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False ):
'''simple docstring'''
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
lowercase = len(set_a.interse... | 354 |
lowercase__ :List[str] = 6_5521
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = 1
lowercase = 0
for plain_chr in plain_text:
lowercase = (a + ord(lowerCAmelCase__ )) % MOD_ADLER
lowerca... | 97 | 0 |
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
from ...test_tokenization_commo... | 355 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRoberta... | 97 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=SCREAMING_SNAKE_CASE__ ):
lowercase_ : Optional[Any] =['''note_seq''']
def __init__( self ,*A__ ,**A__):
requires_backends(self ,['''note_seq'''])
... | 356 |
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 accelerate import Accelerator, Dist... | 97 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ :Any... | 357 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase__ :str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfi... | 97 | 0 |
import math
from datetime import datetime, timedelta
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = year % 19
lowercase = year % 4
lowercase = year % 7
lowercase = math.floor(year /... | 358 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 97 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowercase__ = _LazyModule(_... | 359 |
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( lowerCAmelCase__="ro" , lowerCAmelCase__="en" , lowerCAmelCase__="wmt16" , lowerCAmelCase__=None ):
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, Import... | 97 | 0 |
import math
import sys
import cva
import numpy as np
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = math.sqrt(lowerCAmelCase__ )
lowercase = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.ex... | 360 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase__ :str = logging.get_logger(__name__)
lowercase__ :Any = {"vocab_file": "sent... | 97 | 0 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowercase ( unittest.TestCase ):
def A__ ( self):
lowercase = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
lowercase = [2, 4, 6, 8, 1_0, 1_2]
... | 361 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 97 | 0 |
from math import factorial
class lowercase :
def __init__( self ,A__ ,A__):
lowercase = real
if isinstance(A__ ,A__):
lowercase = [1] * rank
else:
lowercase = ... | 362 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ :Optional[int] = logging.get_logger(__name__)
lowercase__ :List[Any] = {
"camembert-base": "https://huggingfac... | 97 | 0 |
lowercase__ :List[str] = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub":... | 363 |
lowercase__ :Any = 8.3_144_598
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Exception('''Molar mass cannot be le... | 97 | 0 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import Backb... | 364 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 97 | 0 |
import math
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 ):
'''simple docstring'''
lowercase = end or len(lowerCAmelCase__ )
for i in range(lowerCAmelCase__ , lowerCAmelCase__ ):
lowercase = ... | 365 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :Dict = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
... | 97 | 0 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = 0
lowercase = len(lowerCAmelCase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == s... | 366 |
from statistics import mean
import numpy as np
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = 0
# Number of processes finished
lowercase = 0
# ... | 97 | 0 |
from collections import deque
from .hash_table import HashTable
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self ,*A__ ,**A__):
super().__init__(*A__ ,**A__)
def A__ ( self ,A__ ,A__):
lowercase ... | 367 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works fo... | 97 | 0 |
"""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
fro... | 368 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# 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
lowercase__ ... | 97 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :Optional[int] = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusConfig",
"BigBirdPegasu... | 369 |
import numpy as np
import datasets
lowercase__ :Dict = "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C.... | 97 | 0 |
from typing import Any
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if not input_list:
return []
lowercase = [input_list.count(lowerCAmelCase__ ) for value in input_list]
lowercase = max(lowerCAmelCase__ ) # Gets the maximum... | 370 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ :Any = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_... | 97 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase__ :Optional[int] = logging.get_logger(... | 371 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ :Dict = logging.get_logger(__name__)
lowercase__ :Optional[Any] = "▁"
lowercase__ :str = ... | 97 | 0 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def UpperCamelCase ( a ) -> datetime:
'''simple docstring'''
__magic_name__ = year % 19
__magic_name__ = year % 4
__magic_name__ = year % 7
_... | 98 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 | 1 |
'''simple docstring'''
import math
def UpperCamelCase ( a ) -> list:
'''simple docstring'''
__magic_name__ = [True] * n
__magic_name__ = False
__magic_name__ = False
__magic_name__ = True
for i in... | 98 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 98 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _SCREAMING_SNAKE_CASE :
__SCREAMING_SNAKE_CASE :int
__SCREAMING_SNAKE_CASE :Node | None ... | 98 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( a , a , a ) -> bool:
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(a ) )
def UpperCamelCase ( a , a , a , ... | 98 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase = "\\n\n"
_lowerCAmelCase = "\nPerplexity (PPL) is one of the most co... | 98 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowerCAmelCase = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAv... | 98 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMI... | 98 | 1 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def UpperCamelCase ( )... | 98 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 98 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 1 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/... | 98 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ver... | 98 | 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 = Lock()
def UpperCamelCase ( a , a , a , a , a , a , a ) -> int:
... | 98 |
'''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 transfo... | 98 | 1 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def UpperCamelCase ( a ) -> typing.Counter[int]:
'''simple docstring'''
__magic_name__ = Counter()
for base in range(1 , max_perimeter + 1 ):
... | 98 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 98 | 1 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
_lowerCAmelCase = "<<<<<<< This should probably be modified because it mentions: "
_lowerCAmel... | 98 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threa... | 98 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowerCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class _SCREAMIN... | 98 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 98 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def UpperCamelCase ( a , a , a , a , a ) -> np.array:
'''simple docstring'''
__magic_name__ = int(np.ceil((x_end - xa) / step_size ) )
__magic_name__... | 98 |
'''simple docstring'''
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, pre... | 98 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( a , a ) -> str:
'''simple docstring'''
__magic_name__ = BeautifulSoup(requests.get(a , params=a ).content , '''html.parser''' )
__magic_name__ ... | 98 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 98 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapT... | 98 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 98 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extractio... | 98 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def UpperCamelCase ( a ) -> int:
'''simple docstring'''
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __nam... | 98 |
'''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_configurati... | 98 | 1 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tra... | 98 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( a , a ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__magic_name__ = str(bin(a ) )[2:] # remove the leading "0b"
__... | 98 |
'''simple docstring'''
import functools
def UpperCamelCase ( a , a ) -> int:
'''simple docstring'''
__magic_name__ = len(a )
__magic_name__ = len(a )
@functools.cache
def min_distance(a , a ) -> int:
# i... | 98 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 98 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] , ... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( a ) -> None:
'''simple docstring'''
__magic_name__ = generate_pascal_triangle(a )
for row_idx in range(a ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(e... | 98 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ):
__magic_name__ = []
__magic_name__ = 0
__magic_name__ ... | 98 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech... | 98 |
'''simple docstring'''
from random import randint, random
def UpperCamelCase ( a , a , a , a = False , a = False , a = 5 , ) -> list:
'''simple docstring'''
__magic_name__ = [[-1] * number_of_cells] # Create a highway without a... | 98 | 1 |
'''simple docstring'''
import argparse
_lowerCAmelCase = "docs/source/_static/js/custom.js"
def UpperCamelCase ( a ) -> str:
'''simple docstring'''
with open(a , encoding='''utf-8''' , newline='''\n''' ) as f:
__magic_name__ =... | 98 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 | 1 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classe... | 98 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 98 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapT... | 98 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 | 1 |
'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAK... | 98 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase = "\\n\n"
_lowerCAmelCase = "\nPerplexity (PPL) is one of the most co... | 98 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {"vocab_file... | 98 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMI... | 98 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 98 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 98 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils impor... | 98 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ver... | 98 | 1 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_... | 98 |
'''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 transfo... | 98 | 1 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num... | 98 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 98 | 1 |
'''simple docstring'''
import random
from typing import Any
def UpperCamelCase ( a ) -> list[Any]:
'''simple docstring'''
for _ in range(len(a ) ):
__magic_name__ = random.randint(0 , len(a ) - 1 )
__magic_name__ = ... | 98 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threa... | 98 | 1 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _SCREAMING_SN... | 98 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 98 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( __a ,unittest.TestCase ):
__SCREAMING_SNAKE_CASE :Lis... | 98 |
'''simple docstring'''
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, pre... | 98 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from .... | 98 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 98 | 1 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threa... | 98 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapT... | 98 | 1 |
'''simple docstring'''
# Copyright 2023 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
#
#... | 98 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extractio... | 98 | 1 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_lowerCAmelCase = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowerCAmelCase = [ord(letter) for letter in str... | 98 |
'''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_configurati... | 98 | 1 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_lowerCAmelCase = importlib.util.find_spec("s3fs") is not None
if _has_safs:
from... | 98 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 98 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
clas... | 98 |
'''simple docstring'''
import functools
def UpperCamelCase ( a , a ) -> int:
'''simple docstring'''
__magic_name__ = len(a )
__magic_name__ = len(a )
@functools.cache
def min_distance(a , a ) -> int:
# i... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
__magic_name__ = len(a ) + 1
__magic_name__ = len(a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of i... | 98 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] , ... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( a , a , a , a , a , a ) -> List[Any]:
'''simple docstring'''
if index == r:
for j in range(a ):
print(data[j] , end=''' ''' )
print(''' ''' )
return
... | 98 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ):
__magic_name__ = []
__magic_name__ = 0
__magic_name__ ... | 98 | 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_... | 98 |
'''simple docstring'''
from random import randint, random
def UpperCamelCase ( a , a , a , a = False , a = False , a = 5 , ) -> list:
'''simple docstring'''
__magic_name__ = [[-1] * number_of_cells] # Create a highway without a... | 98 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM... | 98 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 | 1 |
'''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_configurati... | 98 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_lowerCAmelCase = generate_large_matrix()
_lowerCAmelCase = (
[[4, 3, 2,... | 98 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMix... | 98 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase = "\\n\n"
_lowerCAmelCase = "\nPerplexity (PPL) is one of the most co... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( a ) -> bool:
'''simple docstring'''
return str(a ) == str(a )[::-1]
def UpperCamelCase ( a ) -> int:
'''simple docstring'''
return int(a ) + int(str(a )[::-1] )
def UpperCamelCase ... | 98 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMI... | 98 | 1 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for test... | 98 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 1 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase = ... | 98 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCAmelCase = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAECon... | 98 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ver... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( a = 10**12 ) -> int:
'''simple docstring'''
__magic_name__ = 1
__magic_name__ = 0
__magic_name__ = 1
__magic_name__ = 1
while numerator <= 2 * min_total - ... | 98 |
'''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 transfo... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( a ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(a , a ):
return 0
elif n == 2:
return 1
else:
__magic_name__ = [0, 1]
for i in range(2 , n + 1 ... | 98 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 98 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :Optional[Any] = """Speech2TextFeatureExtractor"""
__SCREAMING_SNAKE_CASE :Optional[Any] ... | 98 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threa... | 98 | 1 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_lowerCAmelCase = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any memb... | 98 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 98 | 1 |
'''simple docstring'''
class _SCREAMING_SNAKE_CASE :
def __init__( self : Dict , a__ : Any , a__ : Tuple , a__ : Optional[Any] ):
__magic_name__ = name
__magic_name__ = value
__magic_name__ = weight
def __... | 98 |
'''simple docstring'''
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, pre... | 98 | 1 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __a ):
def __init__( self : Any , *a__ : Dict , **a__ : List[str] ):
super().__init__(*a__ , **a__ )
def snake_case__ ( self... | 98 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 98 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def UpperCamelCase ( a ) -> Any:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , ... | 98 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapT... | 98 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCAmelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of tha... | 98 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extractio... | 98 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( a ) -> bool:
'''simple docstring'''
__magic_name__ = str(a )
return len(a ) == 9 and set(a ) == set('''123456789''' )
def UpperCamelCase ( ) -> int | N... | 98 |
'''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_configurati... | 98 | 1 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ver... | 98 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 98 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :int = """ClapFeatureExtractor"""
__SCREAMING_SNAKE_CASE :List[str] = ("""RobertaToke... | 98 |
'''simple docstring'''
import functools
def UpperCamelCase ( a , a ) -> int:
'''simple docstring'''
__magic_name__ = len(a )
__magic_name__ = len(a )
@functools.cache
def min_distance(a , a ) -> int:
# i... | 98 | 1 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRC... | 98 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] , ... | 98 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils ... | 98 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ):
__magic_name__ = []
__magic_name__ = 0
__magic_name__ ... | 98 | 1 |
'''simple docstring'''
from __future__ import annotations
import queue
class _SCREAMING_SNAKE_CASE :
def __init__( self : Tuple , a__ : int ):
__magic_name__ = data
__magic_name__ = None
__magic_name__ = None
def ... | 98 |
'''simple docstring'''
from random import randint, random
def UpperCamelCase ( a , a , a , a = False , a = False , a = 5 , ) -> list:
'''simple docstring'''
__magic_name__ = [[-1] * number_of_cells] # Create a highway without a... | 98 | 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 ( __a ... | 98 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 | 1 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embeddi... | 98 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 98 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testin... | 98 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 | 1 |
'''simple docstring'''
import math
_lowerCAmelCase = 10
_lowerCAmelCase = 7
_lowerCAmelCase = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( a = 20 ) -> str:
'''simple docstring'''
__magic_name__ = math.comb(a , ... | 98 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase = "\\n\n"
_lowerCAmelCase = "\nPerplexity (PPL) is one of the most co... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( a ) -> int:
'''simple docstring'''
if not isinstance(a , a ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
__magic_name__ = 0
while number:
# This way... | 98 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMI... | 98 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
__magic_name__ = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''... | 98 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :List[Any] = (PNDMScheduler,)
__SCREAMING_SNAKE_CASE :Any = (("""num... | 98 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ver... | 98 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase ... | 98 |
'''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 transfo... | 98 | 1 |
'''simple docstring'''
def UpperCamelCase ( a = 1000 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 98 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 98 | 1 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def UpperCamelCase ( a ) -> float:
'''simple docstring'''
return np.dot(a , a )
class _SCREAMING_SNAKE_CASE :
def __init__( ... | 98 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threa... | 98 | 1 |
'''simple docstring'''
from statistics import mean
import numpy as np
def UpperCamelCase ( a , a , a , a ) -> list:
'''simple docstring'''
__magic_name__ = 0
# Number of processes finished
__magic_name__ = 0
# Dis... | 98 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 98 | 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, TableTransfor... | 98 |
'''simple docstring'''
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, pre... | 98 | 1 |
'''simple docstring'''
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase = logging.get_logge... | 98 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 98 | 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 transfo... | 98 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapT... | 98 | 1 |
'''simple docstring'''
import math
import os
import sys
def UpperCamelCase ( a ) -> str:
'''simple docstring'''
__magic_name__ = ''''''
try:
with open(a , '''rb''' ) as binary_file:
__magic_name__ = binary_fil... | 98 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extractio... | 98 | 1 |
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