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"""
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_ : Optional[Any] = 'EncodecFeatureExtractor'
A_ ... | 320 |
'''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... | 27 | 0 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--d... | 107 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
__lowerCAmelCase = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__lowerCAmelCase = BASE_... | 107 | 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_t... | 162 |
'''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 _lowerCamelCase ( lowercase : Union[str, Any] , lowercase : int , lowerc... | 63 | 0 |
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 is_onnx_available():
import onnxruntime a... | 354 |
import random
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :list , SCREAMING_SNAKE_CASE :Dict ) -> tuple:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : List[str] = [], [], []
for element in data:
if element < pivot:
less.append(SCREAMING_SNA... | 232 | 0 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 312 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _a ( snake_case_ , snake_case_ ):
"""simple docstring"""
@register_t... | 312 | 1 |
'''simple docstring'''
def A_( A : Optional[int]):
UpperCamelCase = [0] * len(A)
UpperCamelCase = []
UpperCamelCase = [1] * len(A)
for values in graph.values():
for i in values:
indegree[i] += 1
for i ... | 251 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_... | 251 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass... | 325 |
import math
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int ) -> bool:
assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are prime... | 325 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_A = '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def __UpperCamelCase ( _A = "mumbai" ):
lowerCAmelCase_ = BeautifulSoup(requests.get... | 167 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_A = logging.get_l... | 167 | 1 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accele... | 242 |
"""simple docstring"""
from typing import Any
import numpy as np
def lowercase_ ( __UpperCAmelCase ) -> bool:
return np.array_equal(__UpperCAmelCase , matrix.conjugate().T )
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> Any:
... | 242 | 1 |
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 import logging
... | 333 | # Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def lowercase_ ( _lowerCamelCase : List[str]):
return 1 / (1 + np.exp(-z))
def lowercase_ ... | 333 | 1 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowercase_ ( a__ , a... | 80 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _UpperCamelCase ... | 80 | 1 |
"""simple docstring"""
import argparse
import os
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_task_guides.py
__A = 'src/transformers'
__A = 'docs/source/en/task... | 368 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( a__ ):
def __init__( self : Option... | 64 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
# See all CANINE models at https://huggingface.co/models?... | 179 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
a_ = 50000
a_ = 5000
a_ , a_ = os.path.split(__file__)
a_ = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
... | 179 | 1 |
"""simple docstring"""
import copy
import re
class __magic_name__ :
"""simple docstring"""
__UpperCamelCase = '''hp'''
__UpperCamelCase = {}
__UpperCamelCase = None
@classmethod
def SCREAMING_SNAKE_CASE ( ... | 363 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Any = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
'''ClapTe... | 70 | 0 |
"""simple docstring"""
from itertools import permutations
def _lowerCAmelCase ( lowercase_ ):
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
... | 78 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowercase ( A_ , A_ , A_ = False )-> list[float]:
'''simple docstring'''
if radian_mode:
... | 40 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
a_ : Union[str, Any] = [
os.path.join(os.path.dirname(__file__), dirname... | 104 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list[int] , lowerCAmelCase__ :list[int] ) -> None:
'''simple docstring'''
_a = len(lowerCAmelCase__ )
print('The following activities are selected:' )
# The first a... | 104 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] ) -> Tuple:
... | 90 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase ( nn.Module ):
UpperCAmelCase__ = 42
UpperCAmelCase__ = jnp.floataa
def A_ ( self : Any ) -> Any:
lowerCamelCase__ : str = nn.Conv(
self.out_ch... | 50 | 0 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a : Union[str, Any] ="""\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul... | 355 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_b... | 113 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from .... | 152 |
'''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.ut... | 152 | 1 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = torch.lo... | 355 |
from __future__ import annotations
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = []
create_all_state(1 , __magic_name__ , __magic_name__ , [] , __magic_name__ )
return result
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __... | 201 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase__ ( nn.Module ):
lowerCAmelCase_ = 42
lowerCAmelCase_ = jnp.floataa
def _snake_case ( self ):
"""simpl... | 93 |
'''simple docstring'''
_lowercase : int = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def snake_case_ ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
lowercase_ : Optional[int] = ... | 93 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
... | 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https://huggingfac... | 167 | 0 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
... | 170 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[int] =logging.get_logger(__name__)
_lowercase : Tuple ={
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-25... | 170 | 1 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_snake_case : Optional[int] = argparse.ArgumentParser()
parser.add_argu... | 352 |
'''simple docstring'''
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_modelin... | 179 | 0 |
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
_a : Optional[Any] ... | 294 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(UpperCamelCase__ ):
prin... | 294 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils impor... | 161 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __lowerCamelCase ( __UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : int = rgb[:, :, 0], rgb[:,... | 161 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_se... | 74 |
"""simple docstring"""
from __future__ import annotations
import requests
def _snake_case ( snake_case__ : str ):
A = F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(snake_case__ ).json()
def _snake_case ( snake_... | 74 | 1 |
from math import isclose, sqrt
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> tuple[float, float, float]:
lowerCAmelCase__ : Any = point_y / 4 / point_x
lowerCAmelCase__ : str = 2 * n... | 363 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,... | 307 | 0 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __snake_case ( ):
raise RuntimeError("CUDA out of memory." )
class... | 55 | """simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a)
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : str = field(default="language-modeling" , metad... | 77 | 0 |
"""simple docstring"""
def _snake_case ( lowerCamelCase__ : int ) -> int:
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ), F"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
... | 209 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _snake_case ( lowerCamelCase__ : Any ) -> ... | 209 | 1 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class _lowerCamelCase:
lowercase_ : Optional[Union[str, Path]] = None
lowercase_ : bool = False
lowercase_ : bool = False
lowercase_ : bool ... | 21 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : list[int | float] , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int | float:
if len(_lowerCAmelCase ) == 0:
raise ValueE... | 23 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 360 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin impor... | 41 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int | str] ) -> None:
create_state_space_tree(snake_case_ , [] , 0 , [0 for i in range(len(snake_case_ ) )] )
def lowerCamelCa... | 24 | '''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__lowercase = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_py... | 272 | 0 |
'''simple docstring'''
from math import factorial, pi
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 30 ) -> float:
'''simple docstring'''
if not isinstance(__A, (int, float) ):
raise ValueError('''maclaurin_sin() requires either an int or f... | 357 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@datacl... | 72 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 22 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_a )
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = field(default='a... | 155 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( _a , _a , _a , ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stres... | 211 |
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-roberta-xl''': '''https://h... | 211 | 1 |
import unittest
from transformers import BigBirdConfig, 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():
import jax
from transformers.models.big_bird.mod... | 99 |
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_available(check_device=Fa... | 99 | 1 |
"""simple docstring"""
def __A ( a_ :list) -> list:
if len(a_) <= 1:
return lst
__a : Tuple = 1
while i < len(a_):
if lst[i - 1] <= lst[i]:
i += 1
else:
__a , __a : Any = l... | 188 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 188 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_a = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
"p... | 209 |
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_available():
from t... | 209 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 352 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Tuple = logging.get_logger(__name__)
A_ : Dict = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models at https://huggi... | 292 | 0 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
... | 165 |
"""simple docstring"""
def A ( snake_case__ = 50 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in r... | 165 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
'tokenization_lxme... | 75 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: List[Any] , *__A: Union[str, An... | 75 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = SwinConfig(image_size=192 ... | 57 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __a , __... | 57 | 1 |
def A ( _lowercase , _lowercase ):
def get_matched_characters(_lowercase , _lowercase ) -> str:
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Optional[Any] = min(len(_stra ) , len(_stra ) ... | 258 | from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__UpperCamelCase : Union[str, Any] = datasets.load_iris()
__UpperCamelCase : Any = np.array(data['data'])
__UpperCamelCase : Dict =... | 258 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> "list[int]":
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
__lowerCamelCase : Tuple = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__lowerCamelCase : Tuple ... | 73 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase__ ( _A , _A ):
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative... | 297 | 0 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCAmelCase_ : Union[str, Any] = '''\
@misc{chen2021evaluating,
title={Eval... | 62 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE... | 62 | 1 |
import requests
from bsa import BeautifulSoup
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(snake_case_ , params=snake_case_ ).content , "html.parser" )
_UpperCAm... | 133 |
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
lowercase_ : Optional[i... | 133 | 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 log... | 369 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_a = logging.getLogger()
@unittest.skip("""Temporarily disable the doc test... | 23 | 0 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = '''T5Con... | 38 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKIN... | 38 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional imp... | 356 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAv... | 57 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
snake_case : Optional[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
... | 94 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDepen... | 29 | 0 |
"""simple docstring"""
import sys
_A = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896648950... | 166 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__(self , _lowerCamelCase , _lowerCamelCase , _lower... | 166 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_si... | 105 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
... | 330 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __snake_case : list[int] , __snake_case : int ) -> list[list[int]]:
__A : list[list[int]] = []
__A : list[int] = []
__A :... | 190 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __snake_case : list[int | str] ) -> None:
create_state_space_tree(__snake_case , [] , 0 , [0 for i in range(len(__snake_case ) )] )
... | 190 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sent... | 62 |
_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 ( SCREAMING_SNAKE_CASE__ : dict[int, list[int]] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[bool] ... | 62 | 1 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __lowerCamelCase ( snake_case__ ) -> List[Any]:
"""simple docstri... | 125 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageProcessingSavi... | 125 | 1 |
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
lowercase_ = logging.get_logger(__name__)
l... | 303 |
import math
import os
import sys
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = ''''''
try:
with open(snake_case , '''rb''' ) as binary_file:
__SCREAMING_SNAKE_CASE : int = binary_file.read()
for dat in data:
... | 303 | 1 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import Base... | 359 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_lowercase : Any = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_... | 272 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Dict = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neo... | 31 | '''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 | 1 |
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 : Optional[... | 351 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowercase : List[Any] ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
... | 266 | 0 |
def lowerCAmelCase_ ( __A, __A ) -> None:
'''simple docstring'''
UpperCAmelCase__ = len(__A )
print("The following activities are selected:" )
# The first activity is always selected
UpperCAmelCase__ = 0
print... | 65 | 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 A ( unittest.TestCase ):
def lo... | 65 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
E... | 104 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :int ) -> float:
'''simple docstring'''
_a = x
_a ... | 104 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class a__ ( A__ ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON seri... | 18 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
A_ = logging.get_logger... | 64 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase__ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 360 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _UpperCamelCase... | 83 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Tuple:
assert x is not None
assert y is not None
A_ = len(__A )
A_ = len(__A )
# declaring the array for storing the dp values
A_ = [[0] * (... | 162 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.s... | 51 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def UpperCamelCase_( snake_case : Any , snake_case : Dict , snake_case : Optional[int] , snake_case : List[str] , ... | 366 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCamelCase_( snake_case : str , snake_case : complex , snake_case : str = "x" , snake_case : float = 1_0**-1_0 , snake_cas... | 92 | 0 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytor... | 104 | """simple docstring"""
from typing import List
import numpy as np
def lowercase ( a__ : dict ) -> int:
_UpperCamelCase = {key: len(a__ ) for key, value in gen_kwargs.items() if isinstance(a__ , a__ )}
if len(set(lists_lengths.values() ) ) > 1:
... | 256 | 0 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCAmelCase = re.compile(r'\b(a|an|the)\b', re.UNICODE)
UpperCAmelCase = None
def _snake_case ( ) -> int:
... | 187 |
'''simple docstring'''
class __snake_case:
'''simple docstring'''
def __init__( self ) -> None:
lowerCAmelCase = {} # Mapping from char to TrieNode
lowerCAmelCase = False
def __snake_case ( self , A_ ) -> None:
... | 187 | 1 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorStat... | 127 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils... | 128 | 0 |
'''simple docstring'''
from itertools import permutations
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : tuple ):
'''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
UpperCAmelCase__ ... | 61 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
return int(input_a == input_a == 0 )
def _UpperCamelCase ( ):
'''simple docstring'''
print("""Truth Table of NOR Gate:""" )
print("... | 61 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 292 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to... | 292 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 30 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ["""... | 30 | 1 |
"""simple docstring"""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_we... | 347 | """simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWit... | 221 | 0 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(snake_case_ ) )
def SCREAMING_SNAKE_CASE__ ( snake_case_, ... | 361 |
# 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 | 0 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowercase (snake_case__ : int ) -> float:
'''simple docstring'''
return np.dot(a_ , a_ )
class S... | 155 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Tuple = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
... | 71 | 0 |
def lowerCamelCase__ ( a ) -> Dict:
if not isinstance(a , a ):
raise TypeError('''Input value must be an \'int\' type''' )
_A: List[str] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
do... | 358 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : List[Any] = {
'vo... | 301 | 0 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTeste... | 218 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipel... | 218 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
Segfor... | 178 |
import argparse
from collections import defaultdict
import yaml
lowerCamelCase_ = '''docs/source/en/_toctree.yml'''
def __magic_name__ ( __a : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ = defaultdict(__a )
for doc in model_doc:... | 178 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoMode... | 4 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.uti... | 259 | 0 |
"""simple docstring"""
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Optional[Any] = 0
_lowercase : Optional[int] = len(__UpperCAmelCase ) - 1
while i < j:
... | 361 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : str = F"""{file}_{class_name}_{test_n... | 336 | 0 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_spa... | 306 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 55 | 0 |
'''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, MaskFormerImageProce... | 220 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 220 | 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_torch_avai... | 66 |
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run t... | 66 | 1 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import r... | 355 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _snake_case ( lowerCamelCase__ : Any ) -> ... | 209 | 0 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def __lowerCamelCase ( __UpperCamelCase ) -> int:
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_che... | 241 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float ) ->float:
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(100, 0.25) = }''')
... | 105 | 0 |
from __future__ import annotations
_lowerCamelCase = 1.60_21e-19 # units = C
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : float , ) -> tuple[str, float]:
if (conductivity... | 177 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : np.array ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 177 | 1 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def __UpperCamelCase ( lowercase__ : int ):
'''simple docstring'''
if not isinstance(lowercase__, lowercase__ ):
raise TypeError('Undefined for non-integers' ... | 141 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_im... | 141 | 1 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu... | 148 |
def _A ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1_000 ):
"""simple docstring"""
a__ : Any =1
a__ : Any =0
for divide_by_number in range(SCREAMING_SNAKE_CASE , digit + 1 ):
a__ : list[int] =[]
a_... | 148 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
__UpperCamelCase = 42
__UpperCamelCase = None
__UpperCamelCase = None... | 91 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def UpperCamelCase ( __lowerCamelCase : Dataset , __lowerCamelCase : Dict[str, str] ):
... | 59 | 0 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
Mo... | 353 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_UpperCamelCase: str = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer ... | 53 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCAmelCase ( a__ ):
def __init__( self , Uppe... | 203 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase : List[str] = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if not is_torch... | 359 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Union[str, Any] = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig'... | 151 | 0 |
import math
import unittest
def UpperCamelCase ( __lowerCamelCase : int ):
assert isinstance(__lowerCamelCase , __lowerCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes... | 59 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --u... | 181 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class UpperCamelCase_... | 231 |
from __future__ import annotations
import queue
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase__ : Dict) ->Any:
'''simple docstring'''
A__ = data
A__ = None
... | 231 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import tran... | 290 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 1 |
'''simple docstring'''
from math import factorial
__a = {str(digit): factorial(digit) for digit in range(10)}
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Parameter number must be in... | 369 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
"""simple docstring"""
def lowerCamelCase ( self : Optional[Any] , snake_case_ : Optional... | 43 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __UpperCamelCase :
def __init__( self, lowerCAmelCase=2, lowerCAmelCase=3, lowerCAmelCase=64, lowerCAmelCase=None ):
... | 75 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ =''''''
lowerCamelCase_ ... | 75 | 1 |
import math
from collections.abc import Iterator
from itertools import takewhile
def A(__a: int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
retu... | 22 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSeriesTransformerConfig''... | 22 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _A :
"""simple docstring"""
UpperCAmelCase : int
... | 40 | """simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTok... | 135 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
snake_case_ = 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_copies ... | 356 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB 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.a... | 181 | 0 |
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