code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from tra... | 235 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : List[Any] = {'configuration_xlnet': ['XL... | 51 | 0 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.mode... | 710 |
"""simple docstring"""
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_dimensio... | 20 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCam... | 387 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,... | 387 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _A ( snake_case__ : Callable , snake_case__ : float , snake_case__ : float , snake_case__ : float , snake_case__ : float ):
snake_case__ : Dict = int(np.ceil((x_end - xa) / step_size... | 702 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 694 | 0 |
from PIL import Image
def snake_case__ ( lowercase ):
lowerCAmelCase_: Union[str, Any] = image.size
lowerCAmelCase_: Union[str, Any] = 0
lowerCAmelCase_: int = image.load()
for i in range(lowercase ):
for j in range(lowercase ):
lowerCAmelCas... | 613 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 504 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...te... | 149 |
def snake_case_ ( lowercase__ : int ):
'''simple docstring'''
_lowerCAmelCase =[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =0, 0, 0
_lowerCAmelCase =ugly_nums[ia] * 2
_lowerCAmelCase =ugly_nums[ia] * 3
_lowerCAme... | 149 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = '▁'
SCREAMI... | 300 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE_ ... | 300 | 1 |
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase ... | 717 |
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase ... | 24 | 0 |
'''simple docstring'''
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
_lowerCAmelCase :Optional[Any] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path)... | 251 | '''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/LICE... | 251 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ = {
'''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}... | 713 | '''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
lowerca... | 58 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin... | 622 |
from __future__ import annotations
def _lowerCAmelCase ( A__ , A__ = None ):
lowercase__ = word_bank or []
# create a table
lowercase__ = len(A__ ) + 1
lowercase__ = []
for _ in range(A__ ):
table.append([] )
# seed value
lowercas... | 622 | 1 |
UpperCamelCase_ = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
UpperCamelCase_ = frozenset(["prompt", "negative_prompt"])
... | 712 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def A ( ) -> Optional[int]:
'''simple docstring'''
raise RuntimeError('''CUDA out of memory.''' )
... | 561 | 0 |
"""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,
)
... | 449 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a... | 25 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilB... | 715 |
__magic_name__ : List[Any] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def lowercase__ ( _UpperCamelCase) -> int:
"""simple docstring"""
UpperCamelCase = 0
while number:
# Increased Speed Sligh... | 410 | 0 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class a ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self , snake_case_ , snake_case_ ) -> Dict:
super().__init__()
self.register_modules(un... | 426 |
import unittest
from knapsack import greedy_knapsack as kp
class _A ( unittest.TestCase ):
def __a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase : Dict = [10, 20, 30, 40, ... | 217 | 0 |
'''simple docstring'''
a_ : str = range(2, 20 + 1)
a_ : Any = [10**k for k in range(ks[-1] + 1)]
a_ : dict[int, dict[int, list[list[int]]]] = {}
def __snake_case ( UpperCAmelCase_ : Any , UpperCAmelCase_ : int ... | 445 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ : List[Any] = ... | 445 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class _snake_case ( lowercase_ ):
lowerCAmelCase_ : str
lowerCAmelCase_ : int
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
if not isinstance(... | 400 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Tuple ... | 400 | 1 |
"""simple docstring"""
import os
__lowerCamelCase = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 1_00, "D": 5_00, "M": 10_00}
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
A__ = 0
A__ = 0
... | 536 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_avai... | 536 | 1 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__lowerCamelCase = """src/diffusers"""
# Matches is_xxx_available()
__lowerCamelCase = re.... | 608 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : ... | 218 | 0 |
import os
from datetime import datetime as dt
from github import Github
UpperCamelCase_ = [
"good first issue",
"feature request",
"wip",
]
def _UpperCAmelCase ( ):
"""simple docstring"""
__lowerCAmelCase = Github(os.environ["GITHUB_TOKEN"] )
__lowerCAmelCase ... | 712 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class a ( unittest.TestCase ):
def UpperCAmelCa... | 376 | 0 |
"""simple docstring"""
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.c... | 516 | """simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
A : int = get_logger(__name__)
A : Dict = r'\n Args:\n input_ids (`jnp.... | 516 | 1 |
class _a :
def __init__( self: Tuple , UpperCamelCase_: Any = "" , UpperCamelCase_: Dict = False ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = {}
# A node will be a leaf if the tree contains it... | 716 |
import requests
from bsa import BeautifulSoup
def _a ( SCREAMING_SNAKE_CASE = "AAPL" ):
"""simple docstring"""
lowercase__ = f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
lowercase__ = BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE ).text , '''html.par... | 429 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv... | 459 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTP... | 459 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 718 |
"""simple docstring"""
from math import sqrt
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = 0
for i in range(1 , int(sqrt(UpperCamelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(UpperCamelCase_ ):
total += i + n // i
... | 248 | 0 |
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transfo... | 52 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A = logging.get_logger(__name__)
A ... | 52 | 1 |
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
import jax.numpy as jnp
from flax.jax_utils imp... | 711 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCAmelCase__ ( metaclass=UpperCAmelCase_ ):
lowercase__ : Union[str, Any] = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *UpperCamelCase__ , **UpperCamelC... | 261 | 0 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__a = {
'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_g... | 494 | '''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: str ):
return [ord(a_ ) - 96 for elem in plain]
def __UpperCAmelCase ( a_: list[int] ):
return "".join(chr(elem + 96 ) for elem in encoded )
def __UpperCAmelCase ... | 494 | 1 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
A = logging.get_logger(__name__)
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self : Tuple , *_lowercase : List[str] , **_... | 277 |
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... | 277 | 1 |
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_pytorch_model
from tra... | 43 | """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,
EulerAn... | 159 | 0 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class __snake_case ( nn.Module ):... | 706 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available... | 604 | 0 |
def __lowerCamelCase ( _lowercase ) -> list:
UpperCamelCase = len(_lowercase )
for _ in range(_lowercase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
UpperCamelCase , UpperCa... | 282 |
# 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... | 282 | 1 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import ... | 716 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 620 | 0 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = "x" , SCREAMING_SNAKE_CASE_ = 10**-10 , SCREAMING_SNAKE_CASE_ = 1 , ) ->... | 591 |
'''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class _a :
"""simple docstring"""
def __init__( self ) -> str:
_SCREAMING_SNAKE_CASE = psutil.Process()
_SCREAMING_SNAKE_CASE = False
... | 591 | 1 |
from math import factorial
def __UpperCAmelCase ( __A = 2_0 ) -> int:
'''simple docstring'''
UpperCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase__ ... | 703 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self : Dict , _lowercase : int , ... | 277 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( _lowercase : Union[str, Any] ) -> Optional[int]:
__UpperCAmelCase: Dict = len(_lowercase )
for i in range(length - 1 ):
__UpperCAmelCase: str = i
for k in range(i + 1 , _lowercase ):
if collect... | 523 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-sp... | 523 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A__ = (3, 9, -11, 0, 7, 5, 1, -1)
A__ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __UpperCamelCase :
_lowercase : int
_lowercase : Node | None
class __U... | 184 |
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_CHECKING:
... | 184 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""shi-labs/dinat-... | 178 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __lowerCAmelCase ( lowercase : Optional[int] ... | 178 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channe... | 415 | '''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __A ( UpperCamelCase__ ):
a__ : Optional[int]... | 415 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__lowerCamelCase = logging.get_logger(__name__)
class A__ ( _snake_case ):
def __init__( self , *UpperCamelCase__ , **UpperCamelC... | 288 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__lowerCamelCase = ''''''
__lowerCamelCase = ''''''
__lowerCamelCase = ''''''
__lowerCamelCase = ''''''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> None:
# ... | 288 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__lowerCamelCase = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbel... | 455 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and ... | 455 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
... | 564 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeIma... | 255 | 0 |
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : int = len(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = len(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = (
first_str_length ... | 711 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ ={
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
... | 381 | 0 |
from __future__ import annotations
def lowercase ( a ):
'''simple docstring'''
if len(a ) == 0:
return []
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :List[str] = min(a ), max(a )
SCREAMING_SNAKE_CASE_ :Union[str, Any] = int(max_value - min_value )... | 631 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPEN... | 631 | 1 |
'''simple docstring'''
import math
class _lowercase :
'''simple docstring'''
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> int:
'''simple docstring'''
UpperCAmelCase__ : Tuple = 0.0
UpperCAmelCase__ ... | 701 | '''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : str... | 496 | 0 |
# 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.apache.org/licenses/LICENSE-2.0
#
# ... | 367 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
lowerCamelCase : str = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformable_detr/cuda/ms_de... | 367 | 1 |
"""simple docstring"""
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowercase_ ( ) -> int:
'''simple docstring'''
__lowerCamelCase : Any = 9
__lowerCamelCase : Dict = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],... | 366 | """simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow h... | 366 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowerCAmelCase_ ( _lowerCamelCase: str , _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : Any = list(_lowerCamelCase )
__SCREAMING_SNAKE_... | 578 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _UpperCamel... | 578 | 1 |
'''simple docstring'''
def a_ ( __UpperCAmelCase ) -> list[list[int]]:
"""simple docstring"""
snake_case: Optional[Any] =[]
if len(__UpperCAmelCase ) == 1:
return [nums.copy()]
for _ in range(len(__UpperCAmelCase ) ):
... | 703 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 347 | 0 |
import math
import unittest
from transformers import BioGptConfig, 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_comm... | 176 | # 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 import (
Co... | 64 | 0 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCamelCase__:
def __init__( self : Union[str, Any] , lowerCAmelCase : list[tuple[float, float]] )-> Any:
"""simple docstring"""
UpperCAme... | 50 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__( metaclass=lowerCAmelCase ):
__magic_name__ : List[str] = ["note_seq"]
def __init__( self : Any , *lowerCAmelCase : List[str] , **lowerCAmelCase : int... | 50 | 1 |
from typing import Dict, Optional
import numpy as np
import datasets
a = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segme... | 687 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"""bert-base-uncased""": """https://huggingface.co/be... | 687 | 1 |
'''simple docstring'''
import math
def __lowercase (_lowercase ) -> list:
"""simple docstring"""
__lowerCamelCase : Any = [True] * n
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : List[str] = False
... | 483 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
... | 483 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _snake_case ( UpperCAmelCase_ ):
def lowercase__ ( self):
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2... | 12 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
"processing_vision_text_dual_encoder... | 393 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
lowerCamelCase__ = 637_8137.0
lowerCamelCase__ = 635_6752.31_4245
lowerCamelCase__ = 637_8137
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : f... | 547 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, D... | 547 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None )-> Dict:
if start is None:
_SCREAMING_SNAKE_CASE : str = 0
if end is None:
... | 338 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transform... | 29 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin... | 236 | from __future__ import annotations
from collections.abc import Callable
def a__ ( a , a , a , a = 1_0_0 , ) -> float:
A_ : Any = x_start
A_ : int = fnc(a )
A_ : int = 0.0
for _ ... | 236 | 1 |
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class lowerCAmelCase_ ( __A ):
UpperCAmelCase__ ... | 40 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __UpperCamelCase ( *lowercase_ : Union[str, Any] ):
"""simple docstring"""
if not isinstance(lowercase_ , lowercas... | 536 | 0 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from... | 701 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Tuple = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
... | 235 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: list[float] , UpperCamelCase__: list[float] ):
SCREAMING_SNAKE_CASE__ = sorted(numsa + numsa )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = divmod(len(UpperC... | 6 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
for param in module.parameters():
lowercase__ = False
def _a ( ):
"""simple docstring"""
lowercase__ = '''cuda''... | 43 | 0 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def snake_case ( A__ ,A__ ,A__ ,A__ ):
UpperCAmelCase_ : Tuple = sorted(zip(__UpperCamelCase ,__UpperCamelCase ) ,key=lambda A__ : x[0] / x[1] ,reverse=__UpperCamelCase )
UpperCAmel... | 711 |
"""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
lowerCamelCase_ = logging.getLogger()
@unittest.skip('''Temporaril... | 463 | 0 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logge... | 562 |
def _lowerCamelCase ( __A : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(__A , (list, tuple) ) or not all(
isinstance(__A , __A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of intege... | 485 | 0 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_avail... | 716 |
'''simple docstring'''
lowercase__ = 65_521
def __UpperCamelCase ( __lowerCamelCase : str ) -> int:
'''simple docstring'''
_a = 1
_a = 0
for plain_chr in plain_text:
_a = (a + ord(__lowerCamelCase )) % MOD_ADLER
... | 276 | 0 |
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... | 392 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __a :
def __lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstri... | 228 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import ... | 357 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : str = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': [''... | 357 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase : int = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:
if not is_torc... | 367 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...... | 367 | 1 |
"""simple docstring"""
import os
def snake_case ():
'''simple docstring'''
a : str = os.path.join(os.path.dirname(A_ ) , 'num.txt' )
with open(A_ ) as file_hand:
return str(sum(int(A_ ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
... | 118 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def snake_case (A_ :int = 8 ):
'''simple docstring'''
a : Tuple = ascii_letters + digits + punctuation
return "".... | 118 | 1 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def __magic_name__ ( __a : str ):
... | 513 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xope... | 585 | 0 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokeniz... | 634 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import T... | 634 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "http... | 604 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_SCREAMING_SNAKE_CASE :... | 400 | 0 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(UpperCAmelCase , (list, tuple) ) or not all(
isinstance(UpperCAmelCase , UpperCAmelCase ) for number in numbers ):
... | 210 |
"""simple docstring"""
from math import sqrt
def UpperCamelCase ( UpperCAmelCase = 1_000_000 ) ->int:
"""simple docstring"""
a_ = 0
a_ = 0
a_ = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 *... | 210 | 1 |
def _UpperCAmelCase ( a : int , a : int ):
while second != 0:
snake_case__ = first & second
first ^= second
snake_case__ = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmo... | 654 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,... | 49 | 0 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
i... | 269 |
from ..utils import DummyObject, requires_backends
class __A ( metaclass=a ):
__A = ["""torch"""]
def __init__( self , *UpperCAmelCase_ , **UpperCAmelCase_ ):
requires_backends(self , ["""torch"""] )
@classmethod
def _snake_case ( cl... | 269 | 1 |
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
lowercase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstrin... | 15 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=_lowerCamelCase ):
__lowercase = ["""note_seq"""]
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
... | 542 |
'''simple docstring'''
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 SCREAMING_SNAKE_CASE__ ( __A , __A ) -> int:
_snake_case = ar... | 542 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 160 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
snake_case__ : int = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTCon... | 408 | 0 |
'''simple docstring'''
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
if len(__UpperCAmelCase ) <= 1:
return lst
lowerCamelCase_ : Dict = 1
while i < len(__UpperCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
lowerCamelCase_ , ... | 418 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __snake_case (__UpperCAmelCase ):
... | 418 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .toke... | 79 |
'''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
... | 288 | 0 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : List[Any] ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
i... | 721 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSa... | 401 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils i... | 76 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = CustomTokenizer
pass
| 565 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
Up... | 565 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusion... | 347 |
'''simple docstring'''
from ... import PretrainedConfig
lowerCAmelCase__ : Dict = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
... | 347 | 1 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of... | 703 |
"""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 trans... | 183 | 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
_lowercase: Dict = '''\
@misc{chen2021evaluating,
title={Evaluating Large Language Models... | 192 | def _lowerCamelCase ( snake_case = 10 ):
if not isinstance(snake_case , snake_case ) or n < 0:
raise ValueError('Invalid input' )
_lowerCAmelCase = 10**n
_lowerCAmelCase = 28_433 * (pow(2 , 7_830_457 , snake_case )) + 1
return str(numbe... | 192 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase ( _a ,_a ,_a ,_a ) -> Optional[int]:
# Initialise PyTorch mod... | 713 |
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_available():
import t... | 306 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 97 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def SCREAMING_SNA... | 418 | 0 |
from __future__ import annotations
from collections import Counter
from random import random
class lowerCamelCase :
def __init__( self ) -> str:
"""simple docstring"""
_snake_case : List[str] = {}
def UpperCAmelCase_ ( self ... | 708 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase (unittest.TestCase ):
def UpperCAmelCase_ ( self ) ... | 47 | 0 |
'''simple docstring'''
from collections import defaultdict
def UpperCAmelCase__ ( UpperCAmelCase_ : int ) -> int:
__lowerCamelCase : Any = 1
__lowerCamelCase : Dict = True
for v in tree[start]:
if v not in ... | 13 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_available():
raise OptionalDe... | 477 | 0 |
from __future__ import annotations
SCREAMING_SNAKE_CASE__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
... | 52 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def _a ( self : Any , _snake_case : str=None , _snake_case : Dict=None , _snake_case... | 52 | 1 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(... | 231 |
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():
import jax.numpy as j... | 254 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __snake_case( __A ):
_A = ['''image_processor''', '''feature_extractor''']
_A = '''TvltImageProcessor'''
_A = '''TvltFeatureExtractor'''
def __init__( ... | 718 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
if (
(cp >= 0x4E00 and cp <= 0x... | 168 | 0 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = 'T5Config'
class snake_case_ ( a ):... | 625 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_config... | 625 | 1 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterM... | 45 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 45 | 1 |
__A : Dict = "Alexander Joslin"
import operator as op
from .stack import Stack
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
_A = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
... | 27 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 193 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( __UpperCAmelCase: int = 4 ) -> list[list[int]]:
UpperCamelCase__ : Any = abs(__UpperCAmelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCAmelCase )] for y in range(__UpperCA... | 369 |
from __future__ import annotations
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: str ) -> bool:
UpperCamelCase__ : List[str] = get_failure_array(__UpperCAmelCase )
# 2) Step through text searching for pattern
UpperCam... | 369 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCAmelCase_ ( __A ) -> tuple:
... | 486 | def lowerCAmelCase_ ( __A ) -> list:
'''simple docstring'''
for i in range(len(__A ) - 1, 0, -1 ):
UpperCAmelCase__ = False
for j in range(__A, 0, -1 ):
if unsorted[j] < unsorted[j - 1]:
... | 486 | 1 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclas... | 717 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_ti... | 526 | 0 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def a__ ( _SC... | 71 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Any =["speech"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["speech"] )
class _s... | 71 | 1 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version impo... | 558 | """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():
import ... | 558 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : str = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
# See... | 257 |
import numpy as np
def UpperCamelCase ( _a , _a , _a , _a , _a ) -> List[Any]:
'''simple docstring'''
lowercase_ :Optional[Any] = int(np.ceil((x_end - xa) / h ) )
lowercase_ :List[str] = n... | 257 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class lowercase__ ( _UpperCAmelCase ):
a_ =field(default="""language-modeling""", metadata={"""includ... | 721 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from .... | 115 | 0 |
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