




'''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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase      :   Dict      =       logging.get_logger(__name__)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Tuple          =					["""pixel_values"""]







     def __init__(				self							,__a = True							,__a = None							,__a = PIL.Image.BICUBIC							,__a = True							,__a = None							,__a = 1 / 255							,__a = True							,__a = True							,__a = None							,__a = None							,**__a							,)      ->				None:
          super().__init__(**__a							)
          snake_case			:      Tuple        =			size if size is not None else {"""height""": 256, """width""": 256}
          snake_case			:      List[Any]        =			get_size_dict(__a							)
          snake_case			:      Any        =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          snake_case			:      List[Any]        =			get_size_dict(__a							,param_name="""crop_size"""							)

          snake_case			:      int        =			do_resize
          snake_case			:      Tuple        =			size
          snake_case			:      Union[str, Any]        =			resample
          snake_case			:      Optional[int]        =			do_center_crop
          snake_case			:      List[Any]        =			crop_size
          snake_case			:      Dict        =			do_rescale
          snake_case			:      Tuple        =			rescale_factor
          snake_case			:      Dict        =			do_normalize
          snake_case			:      List[Any]        =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          snake_case			:      Dict        =			image_std if image_std is not None else IMAGENET_STANDARD_STD







     def 			snake_case_      (				self							,__a							,__a							,__a = PIL.Image.BICUBIC							,__a = None							,**__a							,)      ->				np.ndarray:
          snake_case			:      Optional[int]        =			get_size_dict(__a							)
          if "height" not in size or "width" not in size:
               raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''							)
          return resize(
              __a							,size=(size["""height"""], size["""width"""])							,resample=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          snake_case			:      Tuple        =			get_size_dict(__a							)
          if "height" not in size or "width" not in size:
               raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''							)
          return center_crop(__a							,size=(size["""height"""], size["""width"""])							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,**__a							,)      ->				str:
          return rescale(__a							,scale=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return normalize(__a							,mean=__a							,std=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a = None							,__a = None							,__a=None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = ChannelDimension.FIRST							,**__a							,)      ->				PIL.Image.Image:
          snake_case			:      Tuple        =			do_resize if do_resize is not None else self.do_resize
          snake_case			:      List[Any]        =			resample if resample is not None else self.resample
          snake_case			:      Dict        =			do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case			:      str        =			do_rescale if do_rescale is not None else self.do_rescale
          snake_case			:      int        =			rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case			:      str        =			do_normalize if do_normalize is not None else self.do_normalize
          snake_case			:      Any        =			image_mean if image_mean is not None else self.image_mean
          snake_case			:      str        =			image_std if image_std is not None else self.image_std

          snake_case			:      Optional[Any]        =			size if size is not None else self.size
          snake_case			:      List[Any]        =			get_size_dict(__a							)
          snake_case			:      str        =			crop_size if crop_size is not None else self.crop_size
          snake_case			:      Optional[Any]        =			get_size_dict(__a							,param_name="""crop_size"""							)

          snake_case			:      List[str]        =			make_list_of_images(__a							)

          if not valid_images(__a							):
               raise ValueError(
                   """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                   """torch.Tensor, tf.Tensor or jax.ndarray."""							)

          if do_resize and size is None or resample is None:
               raise ValueError("""Size and resample must be specified if do_resize is True."""							)

          if do_center_crop and crop_size is None:
               raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

          if do_rescale and rescale_factor is None:
               raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

          if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

          # All transformations expect numpy arrays.
          snake_case			:      int        =			[to_numpy_array(__a							) for image in images]

          if do_resize:
               snake_case			:      List[str]        =			[self.resize(image=__a							,size=__a							,resample=__a							) for image in images]

          if do_center_crop:
               snake_case			:      Tuple        =			[self.center_crop(image=__a							,size=__a							) for image in images]

          if do_rescale:
               snake_case			:      Optional[Any]        =			[self.rescale(image=__a							,scale=__a							) for image in images]

          if do_normalize:
               snake_case			:      Dict        =			[self.normalize(image=__a							,mean=__a							,std=__a							) for image in images]

          snake_case			:      Dict        =			[to_channel_dimension_format(__a							,__a							) for image in images]

          snake_case			:      str        =			{"""pixel_values""": images}
          return BatchFeature(data=__a							,tensor_type=__a							)









'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      List[Any]        =			FileLock(str(tmpdir / """foo.lock"""				)				)
     snake_case			:      str        =			FileLock(str(tmpdir / """foo.lock"""				)				)
     snake_case			:      Any        =			0.01
     with locka.acquire():
          with pytest.raises(__lowercase				):
               snake_case			:      Union[str, Any]        =			time.time()
               locka.acquire(__lowercase				)
               assert time.time() - _start > timeout







def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      List[Any]        =			"""a""" * 1_000 + """.lock"""
     snake_case			:      List[str]        =			FileLock(str(tmpdir / filename				)				)
     assert locka._lock_file.endswith(""".lock"""				)
     assert not locka._lock_file.endswith(__lowercase				)
     assert len(os.path.basename(locka._lock_file				)				) <= 255
     snake_case			:      Any        =			FileLock(tmpdir / filename				)
     with locka.acquire():
          with pytest.raises(__lowercase				):
               locka.acquire(0				)



