code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
a_ : Optional[Any] = [0, 2, 4, 6, 8]
a_ : str = [1, 3, 5, 7, 9]
def _A (lowerCAmelCase__ :int , lowerCAmelCase__ :int , lowerCAmelCase__ :list[int] , lowerCAmelCase__ :int ) -> int:
'... | 168 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModel... | 168 | 1 |
def _a ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest m... | 51 |
def _a ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest m... | 51 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers imp... | 165 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A_ : str = logging.get_logger(__name__)
# TODO: upload to AWS
A_ : Optional[int] = {
"yjernite/retribert-base-uncased": (
"https://huggingface.... | 165 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str ):
__lowercase = len(lowerCamelCase_ )
__lowercase = len(lowerCamelCase_ )
__lowercase = [[False for _ in range(m + 1 )] for _ in range(n + 1 ... | 357 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX... | 217 | 0 |
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_... | 37 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetCon... | 319 | 0 |
'''simple docstring'''
def UpperCamelCase ( _lowerCamelCase : str , _lowerCamelCase : str ):
A__ = len(_lowerCamelCase ) + 1
A__ = len(_lowerCamelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches ... | 123 |
'''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... | 123 | 1 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ = '''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and F... | 153 |
"""simple docstring"""
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = set(_SCREAMING_SNAKE_CASE ), [start]
while stack:
UpperCamelCase = stack.pop()
... | 153 | 1 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : int ) -> int:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
... | 11 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConf... | 11 | 1 |
def A (__A : int , __A : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def A () -> None:
"""simple docstring"""
assert or_gate(0 , 0 ... | 51 |
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
snake_case_ ... | 51 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 351 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fr... | 157 | 0 |
'''simple docstring'''
def __lowerCAmelCase (__lowerCAmelCase = 600_851_475_143 ):
try:
_UpperCAmelCase : int = int(__SCREAMING_SNAKE_CASE )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <=... | 234 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
"SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/r... | 217 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def A__ ( __lowerCamelCase ):
SCREAMING_SN... | 257 |
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... | 257 | 1 |
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 123 |
from __future__ import annotations
_snake_case : Any = "Muhammad Umer Farooq"
_snake_case : Optional[int] = "MIT"
_snake_case : Union[str, Any] = "1.0.0"
_snake_case : Optional[Any] = "Muhammad Umer Farooq"
_snake_case : List[Any] = "contact@muhammadu... | 123 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import lo... | 48 |
'''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
l... | 48 | 1 |
def _UpperCAmelCase (UpperCamelCase__ : int ):
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(... | 11 |
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()
def _UpperCAmelCase (Upp... | 11 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__snake_case = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailabl... | 112 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __lowerCAmelCase ( lowercase : Union[str, Any] , lowercase : Optional[int]=False ) -> Union[str, Any]:
"""simple docstrin... | 112 | 1 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A__ ( nn.Module):
A_ : int
A_ : int
A_ : float = 0... | 86 | from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__ ) -> tuple[int | None, int | None, float]:
if not arr:
... | 157 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
__UpperCAmelCase : Tuple = 300 # TEMPERATURE (unit = K)
def a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ... | 364 |
def a ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
UpperCamelCase : Any = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Unio... | 315 | 0 |
lowerCAmelCase__ : Tuple =frozenset(
[
'''prompt''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
lowerCAmelCase__... | 257 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : List[str] = '''Speech2TextFeatureExtractor'''
UpperCamelCase__ : List[str] = ... | 257 | 1 |
'''simple docstring'''
import os
def __lowerCAmelCase ():
with open(os.path.dirname(__lowerCAmelCase ) + "/grid.txt" ) as f:
_UpperCAmelCase : List[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(__lowerCAmelCase ) for x in f.readline().... | 322 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
... | 322 | 1 |
from typing import Dict
from .base import GenericTensor, Pipeline
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCa... | 48 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : List[Any] = {'processing_layoutxlm'... | 48 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class _snake_case ( unittest.TestCase ):
... | 64 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
class _snake_case ( a__ ):
snake_case__ = "bert-generation"
def __init__( self : Optional[int] , UpperCAmelCase : Dict=50358 , UpperCAmelCase : int=1024 , U... | 64 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def lowerCAmelCase_ ( _lowerCamelCase: list[float] ):
return np.maximum(0 , _lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5] | 112 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
) | 112 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils imp... | 363 | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler... | 192 | 0 |
"""simple docstring"""
from manim import *
class __snake_case ( _lowercase):
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = Rectangle(height=0.5 , width=0... | 72 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https:/... | 315 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] = [
"""encoder.version""",
... | 364 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _lowerca... | 56 | 0 |
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEA... | 322 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 322 | 1 |
# 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
#
# Unless required by applicabl... | 63 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class _UpperC... | 63 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diff... | 64 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if ... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
'feature_extraction_mctct': ['MCTCTFeatureE... | 371 |
"""simple docstring"""
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, ... | 40 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 315 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
A_ : List[str] = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value network
'scale_grad_by_std... | 192 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_a... | 369 |
__UpperCAmelCase = [
(10_00, "M"),
(9_00, "CM"),
(5_00, "D"),
(4_00, "CD"),
(1_00, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = {'''... | 257 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _lowerCamelCase ):
'''simple docstring'''
__A : int = (UnCLIPScheduler,)
def _snake_case ( self , **__A ):
... | 283 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
a : Dict = (720, 1280) # Height, Width
a : Tuple = (0.4, 0.6) # if height or width lower than this scale, drop it.
a : Dict = ... | 56 | 0 |
from collections.abc import Sequence
def lowerCAmelCase_ (lowerCAmelCase__: Sequence[float] , lowerCAmelCase__: bool = False ):
"""simple docstring"""
if not arr:
return 0
UpperCAmelCase_: Optional[Any] = 0 if allow_... | 370 |
from collections import defaultdict
class _a :
def __init__(self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[str]:
UpperCAmelCase_: Optional[int] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
... | 82 | 0 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ : Dict = ... | 63 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase_ : int = {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT... | 63 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 352 |
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError('''input must be a negative integer''' )
snake_case_ : List[str] = len(bin(_UpperCamelCase )[3:] )
snake_case_ : str ... | 279 | 0 |
UpperCamelCase = [0, 2, 4, 6, 8]
UpperCamelCase = [1, 3, 5, 7, 9]
def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[int] , _lowerCamelCase : int):
if remaining_length == 0:
if dig... | 87 |
"""simple docstring"""
from itertools import permutations
def lowercase ( A_ )-> bool:
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False... | 40 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transforme... | 195 |
"""simple docstring"""
from datetime import datetime
import requests
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
__SCREAMING_SNAKE_CASE ... | 195 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : str = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
''... | 21 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, p... | 257 | 0 |
def snake_case_ ( lowerCAmelCase_ : list , lowerCAmelCase_ : list , lowerCAmelCase_ : int ):
__lowercase : int = len(lowerCAmelCase_ )
__lowercase : Optional[int] = [[0] * n for i in range(lowerCAmelCase_ )]
for i in ran... | 306 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logg... | 306 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_co... | 346 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase ... | 82 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner impo... | 351 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax... | 93 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
... | 105 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common impor... | 279 | 0 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowercase__ : Dict = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a co... | 289 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase__ : Optional[int] = logging.get_logger(__name__)
... | 289 | 1 |
import math
import random
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
UpperCAmelCase = 0.02
def UpperCAmelCase_ ( __SCREAMIN... | 195 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCAmelCase_ ( ):
lowercase = ArgumentParser(
description=(
'PyTorch TPU distributed training launch '
... | 195 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowercase: Tuple = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionE... | 370 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : Dict , _UpperCamelCase : str , _UpperCamelCase : Optional[int] , _UpperCamelCase : str ) -> Dict: # noqa: E741
'''simple docstri... | 31 | 0 |
def __lowerCamelCase ( snake_case__ ) -> bool:
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 306 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rando... | 306 | 1 |
from __future__ import annotations
class A :
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str ) -> Tuple:
"""simple docstring"""
lowercase__ , ... | 146 |
from functools import lru_cache
def UpperCamelCase ( __magic_name__ : int ) -> set:
"""simple docstring"""
lowercase__ = 2
lowercase__ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.... | 146 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
... | 65 |
'''simple docstring'''
from math import isqrt, loga
def snake_case_ ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
lowercase_ : Any = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + ... | 93 | 0 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_ava... | 357 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A : str = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'P... | 33 | 0 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a ( lowerCAmelCase_ ):
_snake_case : Dict = (KDPMaDiscreteScheduler,)
_snake_case : Tupl... | 289 | """simple docstring"""
import warnings
warnings.warn(
"""memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
"""`from accelerate import find_executable_batch_size` to avoid this warning.""",
FutureWarning,
)
| 289 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
f... | 366 |
"""simple docstring"""
from typing import Any
class UpperCAmelCase_ :
def __init__( self : Optional[Any] , snake_case_ : Any ) -> List[str]:
'''simple docstring'''
A__ = data
A__ = None
def __repr__( self : Optional[int] ... | 230 | 0 |
import argparse
from collections import defaultdict
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> Dict:
'''simple docstring'''
lowercase : Optional[Any] = F"""{file}_{class_name}... | 308 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__SCREAMING_SNAKE_CASE : Optional[int] = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCH... | 31 | 0 |
"""simple docstring"""
import random
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def __SCREAMING_SNAKE_CASE ( __a : str ) -> tuple[list[int], list[int]]:
_UpperCamelCase : List[Any] = [ord(__a ) for i in text]
_... | 310 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"facebook/xlm-rob... | 310 | 1 |
import argparse
from collections import defaultdict
import yaml
__UpperCamelCase : List[Any] = "docs/source/en/_toctree.yml"
def _a ( SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
UpperCamelCase__ : str = defaultdict(SCREAMING_SNAKE_CASE )
UpperCa... | 146 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import c... | 146 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxCo... | 365 |
"""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/l... | 302 | 0 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ = {'''UserAgent''': UserAgent().random}
def _A ( A__ ):
"""simple docstring"""
__lowercase = s... | 104 |
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0 ):
lowercase_ : str = 0
lowercase_ : List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
... | 33 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : Any = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBer... | 371 |
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
lowercase : Optional[Any] = '''src/diffusers'''
# Matches is_xxx_available()
lowercase : Any ... | 36 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a : int = logging.getLogger(__name__)
if __name__ ... | 265 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching b... | 230 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _lowerCamelCase ( lowerCamelCase_ : str ):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : int = ... | 274 | '''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelC... | 274 | 1 |
from __future__ import annotations
from math import pi
def _A ( _lowercase , _lowercase , _lowercase ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('One and only ... | 310 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import ... | 310 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merge... | 367 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 88 | 0 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
snake_case : int = logging.getLogger(__name__)
def lowerCAmelCase_ ( ) -> List[Any]:
'''simple docstring'''
__magic_name__ : List[Any] = arg... | 281 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/r... | 302 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
__SCREAMING_SNAKE_CASE :List[Any] = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy... | 350 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : list ) -> list:
'''simple docstring'''
_UpperCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
_UpperCAmelCase = True
... | 156 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, Tensor... | 109 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
_snake_case = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def A ( _lowerCamel... | 36 | 0 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
_snake_case = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
_snake_case = requests.get(... | 199 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
fr... | 199 | 1 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class A (datasets.BuilderConfig ):
'''simple docstring'''
__lowerCamelCase : O... | 274 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A : Optional[Any] = logging... | 274 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from tran... | 362 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : List[str] = {
'''configuration_electra''': ['''ELE... | 227 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dim... | 125 |
import re
import string
import numpy as np
import datasets
__lowerCAmelCase : Optional[int] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
__lowerCAmelCase : Optional... | 88 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cac... | 370 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE :Un... | 156 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if i... | 156 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__lowerCAmelCase : Optional[Any] = {"UserAgent": UserAgent().random}
def UpperCAmelCase_ ( __lowerCAmelCase ) -> dict:
__lowercase : ... | 156 | 1 |
"""simple docstring"""
import math
import random
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
SCREAMING_SNAKE_CASE = 0.02
def _SCREAMIN... | 366 |
"""simple docstring"""
from typing import Any
class UpperCAmelCase_ :
def __init__( self : Optional[Any] , snake_case_ : Any ) -> List[str]:
'''simple docstring'''
A__ = data
A__ = None
def __repr__( self : Optional[int] ... | 230 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
... | 199 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ : List[Any] =[('size', ctypes.c_int), ('visible', ... | 199 | 1 |
from __future__ import annotations
UpperCamelCase__ : List[Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
UpperCamelCase__ : str = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) ->... | 330 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. A... | 330 | 1 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
# TODO Update this
UpperCAmelCase : Optional[Any] = {
... | 95 |
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... | 227 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowerCAmelCase_... | 354 | """simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase = 5_0 ) -> int:
lowercase__: str = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_leng... | 2 | 0 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class A__ :
'''simple docstring'''
def __init__( self: List[str] , _SCREAMING_SNAKE_CASE: List[Any]) -> Any:
"""simple ... | 269 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : Any = '''EncodecFeatureExtractor'''
A__ : ... | 156 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogit... | 370 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> List[str]:
# vision encoder
if "img_encoder.pos_embed" in name:
lowercase : ... | 285 | 0 |
'''simple docstring'''
import baseaa
def __lowerCamelCase ( _lowercase ) -> bytes:
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def __lowerCamelCase ( _lowercase ) -> str:
return baseaa.aaadecode(__lowerCAmelCase ).decode("""utf-8""" )
if ... | 265 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching b... | 230 | 0 |
from __future__ import annotations
import time
a = list[tuple[int, int]]
a = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
... | 367 |
"""simple docstring"""
from collections import deque
class lowercase_ :
'''simple docstring'''
def __init__( self : int , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = process_name # process name
_A = ... | 271 | 0 |
import numpy as np
a_ = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class __lowe... | 330 |
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 330 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tra... | 330 |
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> Union[str, Any]:
"""simple docstring"""
stooge(snake_case_, 0, len(snake_case_ ) - 1 )
return arr
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_, snake_case_ ) -> ... | 330 | 1 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _A ( unittest.TestCase ):
@property
d... | 49 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCamelCase : List[Any] = logging.getLogger(__name__)
class __lowerCAmelCase (lowercase_ ):
... | 2 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: List[Any] , __A: list[tuple[float, float]] ) -> Optional[Any]:
_A = list_of_po... | 75 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase )... | 75 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( _snake_case : Dict ) -> int:
'''simple docstring'''
def is_in_circle(_snake_case : Any , _snake_case : List[str] ) -> ... | 281 |
def __lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_UpperCAmelCase : Union[str, Any] = generate_large_matrix()
_UpperCAmelCase : Tuple = (
[[4,... | 285 | 0 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Acce... | 309 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_av... | 309 | 1 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Tuple = logging.get_logger(__name__)
lowercase__ : str = {
"""snap-research/efficientformer-l1-300""": (
... | 224 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow,... | 271 | 0 |
def __lowerCamelCase (UpperCAmelCase__ : Tuple ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = set({"(", "[", "{"} )
SCREAMING_SNAKE_CASE = set({")", "]", "}"} )
SCREAMING_SNAKE_CASE = {"{": "}", "[": "]", "(... | 352 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required... | 206 | 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... | 330 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""enc... | 330 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_availabl... | 88 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
Args:
... | 88 | 1 |
'''simple docstring'''
from __future__ import annotations
a_ : Tuple = list[list[int]]
# assigning initial values to the grid
a_ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, ... | 75 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Any = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingf... | 75 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTM... | 365 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
... | 163 | 0 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a_ (_a ):
def __init__( self , snake_... | 309 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers im... | 309 | 1 |
def SCREAMING_SNAKE_CASE ( ):
return 1
def SCREAMING_SNAKE_CASE ( snake_case_ : int ):
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def SCREAMING_SNAKE_CASE ( snake_case_ : int ):
return 0 if x < 0 else five_pence(x - 5 ... | 286 |
import sys
__lowerCamelCase : List[str] = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""... | 286 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : str ={
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
... | 41 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=10_24 , lowerCamelCase__=10_24... | 206 | 0 |
"""simple docstring"""
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
A_ = TypeVar('''T''')
class __SCREAMING_SNAKE_CASE ( Generic[T] ... | 296 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test... | 296 | 1 |
import os
__lowerCAmelCase : int = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = 0
__magic_name__ = 0
while index < len(A_ ) - 1:
__... | 88 |
def a__ ( A_ ):
'''simple docstring'''
if not isinstance(A_, A_ ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(A_ ) == 0:
raise ValueError("""Input list must be a non empty list""" )
if len(A_ ) ==... | 88 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
SCREAMING_SNAKE_CASE : Any = """https://api.github.com"""
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
SCREAMING_SNAKE_CASE : Tuple = ... | 24 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=__snake_case ):
'''simple docstring'''
lowerCamelCase__ =['transformers', 'torch', 'note_seq']
def __init__(self , *a_ , **a_ ):
'''simple docstring'... | 24 | 1 |
def a_ ( lowerCAmelCase_ : int ):
if n_term == "":
return []
__lowerCAmelCase = []
for temp in range(int(UpperCamelCase__ ) ):
series.append(F"""1/{temp + 1}""" if series else '1' )
return series
if __name__ == "__main__":
_snake_case : Dict = input(... | 284 |
'''simple docstring'''
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__A =namedtuple(
'_TestCommandArgs',
[... | 163 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer"... | 352 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int ) -> int:
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be posi... | 112 | 0 |
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