code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.... | 226 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate... | 226 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def a ( _UpperCAmelCase : str ):... | 226 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import ... | 226 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCAmelCase__ :
'''simple docstring'''
UpperCamelCase = None
def snake_case__ ( self : List[str] ):
'''sim... | 226 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_... | 226 | 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
class ... | 226 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
... | 226 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
),
}
class ... | 226 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCAmelCase__ :
'''simple docstring'''
UpperCamelCase = None
def snake_case__ ( self : List[str] ):
'''sim... | 226 | 1 |
def a ( _UpperCAmelCase : int = 2_00 ):
'''simple docstring'''
__UpperCAmelCase : int = [1, 2, 5, 10, 20, 50, 1_00, 2_00]
__UpperCAmelCase : Union[str, Any] = [0] * (pence + 1)
__UpperCAmelCase : Tup... | 226 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 226 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A ={
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
... | 226 |
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 --user <user> --hos... | 226 | 1 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__A ="2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise ImportW... | 226 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__A =""
__A =""
__A =""
__A =1 # (0 is vertical, 1 is horizontal)
def a ( ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmel... | 226 | 1 |
from collections.abc import Callable
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[int] , a_ : Callable | None = None ):
'''simple docstring'''
__UpperCAmelCase : list = []
... | 226 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
P... | 226 | 1 |
def a ( _UpperCAmelCase : int = 1_00_00_00 ):
'''simple docstring'''
__UpperCAmelCase : Tuple = set(range(3 , _UpperCAmelCase , 2 ) )
primes.add(2 )
for p in range(3 , _UpperCAmelCase , 2 ):
i... | 226 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def a ( _UpperCAmelCase : Any , _UpperCAmelCase : Any=None ):
'''simple docstring'''
... | 226 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import l... | 226 |
import qiskit
def a ( _UpperCAmelCase : int , _UpperCAmelCase : int ):
'''simple docstring'''
__UpperCAmelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
__UpperCAmelCase : Optional[A... | 226 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class UpperC... | 226 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 226 | 1 |
import sys
import turtle
def a ( _UpperCAmelCase : tuple[float, float] , _UpperCAmelCase : tuple[float, float] ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def a ( _UpperCAmelCase ... | 226 |
import numpy as np
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = (0, 0)
__UpperCAmelCase : List[str] = None
... | 226 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, US... | 226 |
def a ( _UpperCAmelCase : Any ):
'''simple docstring'''
__UpperCAmelCase : Any = 0
__UpperCAmelCase : str = len(_UpperCAmelCase )
for i in range(n - 1 ):
for j in range(i + 1 , _UpperCAme... | 226 | 1 |
import argparse
import os
# New Code #
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 acce... | 226 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
'''simple do... | 226 | 1 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_... | 226 |
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 ... | 226 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
__A ="https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__A =BASE_URL + "/user"
# https://github.com/settings/tokens
__A =o... | 226 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, T... | 226 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def a ( _UpperCAmelCase : str , _UpperCAmelCase : List[str]=False ):
'''simple docstring'''
__UpperCAmelCase : ... | 226 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distr... | 226 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A ={"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxCo... | 226 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
class UpperCAmelCase__ ( __UpperCamelCase ):
'''simple docstring'''
... | 226 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
... | 226 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCAmelCase__ ( uni... | 226 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__A ="\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei an... | 226 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 226 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__A =logging.get_logger(__name__)
__A ={
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class UpperCAmelCas... | 226 |
from __future__ import annotations
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[Any] , a_ : Optional[int]=None ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = data
... | 226 | 1 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 226 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate... | 226 | 1 |
from __future__ import annotations
__A =[
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a ( _UpperCAmelCase : list[list[int]] , _UpperCAmelCase : list[int] , _UpperCAmelCase : list[int] , _UpperCAmelCas... | 226 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import ... | 226 | 1 |
def a ( _UpperCAmelCase : int = 1_00_00_00 ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = 1
__UpperCAmelCase : Optional[Any] = 1
__UpperCAmelCase : Optional[int] = {1: 1}
... | 226 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_... | 226 | 1 |
import re
def a ( _UpperCAmelCase : str ):
'''simple docstring'''
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def a ( _UpperCAmelCase : str ):
'''simple d... | 226 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
... | 226 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaT... | 226 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCAmelCase__ :
'''simple docstring'''
UpperCamelCase = None
def snake_case__ ( self : List[str] ):
'''sim... | 226 | 1 |
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 ... | 226 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 226 | 1 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 226 |
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 --user <user> --hos... | 226 | 1 |
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 ={
"microsoft/beit-base-patch16-224-p... | 226 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__A =""
__A =""
__A =""
__A =1 # (0 is vertical, 1 is horizontal)
def a ( ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmel... | 226 | 1 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def a ( _UpperCAmelCase : Union[str, Any] ):
'''simple docstring'''
... | 226 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
P... | 226 | 1 |
from math import isqrt, loga
def a ( _UpperCAmelCase : int ):
'''simple docstring'''
__UpperCAmelCase : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for ... | 226 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def a ( _UpperCAmelCase : Any , _UpperCAmelCase : Any=None ):
'''simple docstring'''
... | 226 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def a ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray ):
'''simple docstring'''
return math.sqrt(sum(pow(a - b , 2 ... | 226 |
import qiskit
def a ( _UpperCAmelCase : int , _UpperCAmelCase : int ):
'''simple docstring'''
__UpperCAmelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
__UpperCAmelCase : Optional[A... | 226 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tok... | 226 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 226 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def a ( _UpperCAmelCase : str , _UpperCAmelCase : str ):
'''simple docstring'''
__UpperCAmelCase : str = list(_Up... | 226 |
import numpy as np
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = (0, 0)
__UpperCAmelCase : List[str] = None
... | 226 | 1 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def a ( _UpperCAmelCase : str = "" ):
'''simple docstring'''
__UpperCAmelCase : Dict = url or '''https://www.imdb.com/chart/top/?... | 226 |
def a ( _UpperCAmelCase : Any ):
'''simple docstring'''
__UpperCAmelCase : Any = 0
__UpperCAmelCase : str = len(_UpperCAmelCase )
for i in range(n - 1 ):
for j in range(i + 1 , _UpperCAme... | 226 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 226 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
'''simple do... | 226 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def a ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple=1 ):
'''simple docstring'''
if n_shave_prefix_segm... | 226 |
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 ... | 226 | 1 |
# 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 having m... | 226 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, T... | 226 | 1 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__A =list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__A =[file for file in filepaths if file != file.lower()]
i... | 226 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distr... | 226 | 1 |
from __future__ import annotations
def a ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError(... | 226 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
class UpperCAmelCase__ ( __UpperCamelCase ):
'''simple docstring'''
... | 226 | 1 |
import numpy as np
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = (0, 0)
__UpperCAmelCase : List[str] = None
... | 226 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCAmelCase__ ( uni... | 226 | 1 |
from scipy.stats import pearsonr
import datasets
__A ="\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is ... | 226 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 226 | 1 |
def a ( _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : int ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = len(_UpperCAmelCase )
__UpperCAmelCase : str ... | 226 |
from __future__ import annotations
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[Any] , a_ : Optional[int]=None ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = data
... | 226 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQ... | 226 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate... | 226 | 1 |
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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitCon... | 226 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import ... | 226 | 1 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
... | 226 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_... | 226 | 1 |
from math import isqrt
def a ( _UpperCAmelCase : int ):
'''simple docstring'''
__UpperCAmelCase : str = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in ran... | 226 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
... | 226 | 1 |
from torch import nn
class UpperCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , a_ : Optional[Any] , a_ : List[Any] ):
'''simple docstring'''
super().__init__()
__Upp... | 226 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCAmelCase__ :
'''simple docstring'''
UpperCamelCase = None
def snake_case__ ( self : List[str] ):
'''sim... | 226 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 226 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 226 | 1 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
P... | 226 |
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 --user <user> --hos... | 226 | 1 |
import math
import random
def a ( _UpperCAmelCase : float , _UpperCAmelCase : bool = False ):
'''simple docstring'''
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__A =... | 226 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__A =""
__A =""
__A =""
__A =1 # (0 is vertical, 1 is horizontal)
def a ( ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmel... | 226 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import ... | 226 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
P... | 226 | 1 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__A =argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
" Distillation"
... | 226 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def a ( _UpperCAmelCase : Any , _UpperCAmelCase : Any=None ):
'''simple docstring'''
... | 226 | 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 transfo... | 226 |
import qiskit
def a ( _UpperCAmelCase : int , _UpperCAmelCase : int ):
'''simple docstring'''
__UpperCAmelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
__UpperCAmelCase : Optional[A... | 226 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__A =logging.get_logger(__name__)
__A ={"vocab_f... | 226 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 226 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
'''simple do... | 226 |
import numpy as np
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = (0, 0)
__UpperCAmelCase : List[str] = None
... | 226 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https:... | 226 |
def a ( _UpperCAmelCase : Any ):
'''simple docstring'''
__UpperCAmelCase : Any = 0
__UpperCAmelCase : str = len(_UpperCAmelCase )
for i in range(n - 1 ):
for j in range(i + 1 , _UpperCAme... | 226 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A ={"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNo... | 226 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
'''simple do... | 226 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_... | 0 |
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 ... | 226 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ = str(bin(snake_cas... | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, T... | 226 | 0 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _SCREAMING_SNAKE_CASE (A ) -> str:
"""simple docstring"""
return "".join(sorted(A ) )
def _SCREAMING_SNAKE_CASE (A ) ... | 2 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distr... | 226 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A ( metaclass=__snake_case ):
__magic_name__ = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *SCREAMING_SNAKE_CASE , **SCREAMING_S... | 3 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
class UpperCAmelCase__ ( __UpperCamelCase ):
'''simple docstring'''
... | 226 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case ={"""configuration_plbart""": ["""PLBART_PRETRAINE... | 4 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCAmelCase__ ( uni... | 226 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def UpperCAmelCase_ ( __snake_case ) -> typing.Counter[int]:
"""simple docstring"""
_lowercase =Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in ... | 5 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 226 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Union[str, Any] = {
'xlm-roberta-base': 'https://huggingface.co/xlm... | 6 |
from __future__ import annotations
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[Any] , a_ : Optional[int]=None ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = data
... | 226 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json... | 7 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate... | 226 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenize... | 8 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import ... | 226 | 0 |
from __future__ import annotations
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : list[list[int]] = []
create_all_state(1 , lowercase__ , lowercase__ , [] , lowercase__ )
return res... | 9 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_... | 226 | 0 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDic... | 10 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
... | 226 | 0 |
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self) -> Optional[Any]:
_A : Union[str, Any] = {}
def _lowerCamelCase ( self) -> None:
print(self.vertex)
for i in self.vertex:
... | 11 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCAmelCase__ :
'''simple docstring'''
UpperCamelCase = None
def snake_case__ ( self : List[str] ):
'''sim... | 226 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = 'https://openai... | 12 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 226 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxFo... | 13 |
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 --user <user> --hos... | 226 | 0 |
from __future__ import annotations
_lowerCamelCase : Optional[Any] = 1.60_21E-19 # units = C
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , ) -> tuple[str, float]:
"""simple docstring"""
if (conductivity, electron_conc, mobilit... | 14 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__A =""
__A =""
__A =""
__A =1 # (0 is vertical, 1 is horizontal)
def a ( ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmel... | 226 | 0 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def UpperCAmelCase ( a_ ) -> l... | 15 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
P... | 226 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils impor... | 16 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def a ( _UpperCAmelCase : Any , _UpperCAmelCase : Any=None ):
'''simple docstring'''
... | 226 | 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_torch_a... | 17 |
import qiskit
def a ( _UpperCAmelCase : int , _UpperCAmelCase : int ):
'''simple docstring'''
__UpperCAmelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
__UpperCAmelCase : Optional[A... | 226 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
... | 18 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 226 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 19 |
import numpy as np
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = (0, 0)
__UpperCAmelCase : List[str] = None
... | 226 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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, random_attention_mask
from ... | 20 |
def a ( _UpperCAmelCase : Any ):
'''simple docstring'''
__UpperCAmelCase : Any = 0
__UpperCAmelCase : str = len(_UpperCAmelCase )
for i in range(n - 1 ):
for j in range(i + 1 , _UpperCAme... | 226 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , ) -> list[float]:
_lowercase , _lowercase : Un... | 21 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
'''simple do... | 226 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__SCREAMING_SNAKE_CASE :Union[str, Any] = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_A... | 22 |
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 ... | 226 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__: ... | 23 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, T... | 226 | 0 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowerCamelCase__ ( ) -> Dict:
raise RuntimeError('''CUDA out of memory.''' )
... | 24 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distr... | 226 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.m... | 25 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
class UpperCAmelCase__ ( __UpperCamelCase ):
'''simple docstring'''
... | 226 | 0 |
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, ... | 26 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCAmelCase__ ( uni... | 226 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowercase : int = logging.get_logger(__name__)
__lowercase : List[str] = {'vocab_file': 'vocab.json'}
__lowercase : L... | 27 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 226 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_lowerCamelCase : Union[str, Any] = {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/con... | 28 |
from __future__ import annotations
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[Any] , a_ : Optional[int]=None ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = data
... | 226 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__UpperCAmelCase = logging.get_logger(__name__)... | 29 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate... | 226 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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 (
... | 30 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import ... | 226 | 0 |
'''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
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
__SCREAM... | 31 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_... | 226 | 0 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def SCREAMING_SNAKE_CASE_ ( *__A : str , __A : Optional[Union[Dict, Any]] = None , __A : Tuple=True , __A : int=2 ) -> Optional[Any]:
"""si... | 32 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
... | 226 | 0 |
"""simple docstring"""
from typing import Any
class _UpperCAmelCase :
def __init__( self : Optional[Any] , A : Any ) -> Tuple:
lowercase_ : List[Any] = data
lowercase_ : Union[str, Any] ... | 33 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCAmelCase__ :
'''simple docstring'''
UpperCamelCase = None
def snake_case__ ( self : List[str] ):
'''sim... | 226 | 0 |
'''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 ={
'google/mobilenet_v1_1.0_2... | 34 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 226 | 0 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .uti... | 35 |
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 --user <user> --hos... | 226 | 0 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
c... | 36 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__A =""
__A =""
__A =""
__A =1 # (0 is vertical, 1 is horizontal)
def a ( ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmel... | 226 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : int = """"""
for word_or_phrase in separated:
if not isinstance(UpperCamelCase , UpperCamelCase ):
... | 37 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
P... | 226 | 0 |
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
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)... | 38 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def a ( _UpperCAmelCase : Any , _UpperCAmelCase : Any=None ):
'''simple docstring'''
... | 226 | 0 |
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
... | 39 |
import qiskit
def a ( _UpperCAmelCase : int , _UpperCAmelCase : int ):
'''simple docstring'''
__UpperCAmelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
__UpperCAmelCase : Optional[A... | 226 | 0 |
"""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... | 40 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 226 | 0 |
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