


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_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__			:      List[Any]								=logging.get_logger(__name__)







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""pixel_values"""]






							def __init__(      self			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = PILImageResampling.BICUBIC			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = 1 / 2_5_5			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
										SCREAMING_SNAKE_CASE_		:     Any					=					get_size_dict(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__			,						param_name='crop_size'       )

										SCREAMING_SNAKE_CASE_		:     int					=					do_resize
										SCREAMING_SNAKE_CASE_		:     Any					=					do_rescale
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					do_normalize
										SCREAMING_SNAKE_CASE_		:     str					=					do_center_crop
										SCREAMING_SNAKE_CASE_		:     List[str]					=					crop_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					size
										SCREAMING_SNAKE_CASE_		:     str					=					resample
										SCREAMING_SNAKE_CASE_		:     Tuple					=					rescale_factor
										SCREAMING_SNAKE_CASE_		:     Any					=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										SCREAMING_SNAKE_CASE_		:     Any					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = PILImageResampling.BILINEAR			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					get_size_dict(lowerCAmelCase__       )

										if "shortest_edge" in size:
													SCREAMING_SNAKE_CASE_		:     int					=					get_resize_output_image_size(lowerCAmelCase__			,						size=size['shortest_edge']			,						default_to_square=lowerCAmelCase__       )
													# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
										elif "height" in size and "width" in size:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					(size['height'], size['width'])
										else:
													raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''       )
										return resize(lowerCAmelCase__			,						size=lowerCAmelCase__			,						resample=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					get_size_dict(lowerCAmelCase__       )
										if "height" not in size or "width" not in size:
													raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''       )
										return center_crop(lowerCAmelCase__			,						size=(size['height'], size['width'])			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return rescale(lowerCAmelCase__			,						scale=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										return normalize(lowerCAmelCase__			,						mean=lowerCAmelCase__			,						std=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = ChannelDimension.FIRST			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					do_resize if do_resize is not None else self.do_resize
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					do_rescale if do_rescale is not None else self.do_rescale
										SCREAMING_SNAKE_CASE_		:     List[str]					=					do_normalize if do_normalize is not None else self.do_normalize
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					do_center_crop if do_center_crop is not None else self.do_center_crop
										SCREAMING_SNAKE_CASE_		:     int					=					crop_size if crop_size is not None else self.crop_size
										SCREAMING_SNAKE_CASE_		:     Any					=					get_size_dict(lowerCAmelCase__			,						param_name='crop_size'			,						default_to_square=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					resample if resample is not None else self.resample
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					rescale_factor if rescale_factor is not None else self.rescale_factor
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image_mean if image_mean is not None else self.image_mean
										SCREAMING_SNAKE_CASE_		:     str					=					image_std if image_std is not None else self.image_std

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					size if size is not None else self.size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					get_size_dict(lowerCAmelCase__       )

										if not is_batched(lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     str					=					[images]

										if not valid_images(lowerCAmelCase__       ):
													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:
													raise ValueError('Size 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.'       )

										# All transformations expect numpy arrays.
										SCREAMING_SNAKE_CASE_		:     List[str]					=					[to_numpy_array(lowerCAmelCase__       ) for image in images]

										if do_resize:
													SCREAMING_SNAKE_CASE_		:     str					=					[self.resize(image=lowerCAmelCase__			,						size=lowerCAmelCase__			,						resample=lowerCAmelCase__       ) for image in images]

										if do_center_crop:
													SCREAMING_SNAKE_CASE_		:     int					=					[self.center_crop(image=lowerCAmelCase__			,						size=lowerCAmelCase__       ) for image in images]

										if do_rescale:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					[self.rescale(image=lowerCAmelCase__			,						scale=lowerCAmelCase__       ) for image in images]

										if do_normalize:
													SCREAMING_SNAKE_CASE_		:     str					=					[self.normalize(image=lowerCAmelCase__			,						mean=lowerCAmelCase__			,						std=lowerCAmelCase__       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     int					=					[to_channel_dimension_format(lowerCAmelCase__			,						lowerCAmelCase__       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     Dict					=					{'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase__			,						tensor_type=lowerCAmelCase__       )








"""simple docstring"""


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def   _A					(	*_a	:						int				):



      """simple docstring"""






      if not isinstance(_a     ,   _a				):
            A				  =		list(_a				)
      for i in range(len(_a				)				):
            A				  =		None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects



def   _A					(	_a	:						Exception				):



      """simple docstring"""






      A				  =		[
          """CUDA out of memory.""",  # CUDA OOM
          """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
          """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
      ]
      if isinstance(_a     ,   _a				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False





def   _A					(	_a	:						callable = None     ,   _a	:						int = 1_2_8				):



      """simple docstring"""






      if function is None:
            return functools.partial(_a     ,   starting_batch_size=_a				)

      A				  =		starting_batch_size

      def decorator(*_a	:						Union[str, Any]     ,   **_a	:						List[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            A				  =		list(inspect.signature(_a				).parameters.keys()				)
            # Guard against user error
            if len(_a				) < (len(_a				) + 1):
                  A				  =		""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]     ,   args[1:]				)]				)
                  raise TypeError(
                      f'Batch size was passed into `{function.__name__}` as the first argument when called.'
                      f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError("""No executable batch size found, reached zero."""				)
                  try:
                        return function(_a     ,   *_a     ,   **_a				)
                  except Exception as e:
                        if should_reduce_batch_size(_a				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator




