



"""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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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       UpperCamelCase__   (	A_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     ["pixel_values"]



      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      Dict[str, int] = None    ,			SCREAMING_SNAKE_CASE_				:      PILImageResampling = PILImageResampling.BICUBIC    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      Union[int, float] = 1 / 2_5_5    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[float, List[float]]] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[float, List[float]]] = None    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			**SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			):
        super().__init__(**_snake_case       )
        lowerCAmelCase_   :				List[str]								= size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
        lowerCAmelCase_   :				Optional[Any]								= get_size_dict(_snake_case    ,			default_to_square=_snake_case       )

        lowerCAmelCase_   :				Optional[int]								= do_resize
        lowerCAmelCase_   :				str								= size
        lowerCAmelCase_   :				Optional[Any]								= resample
        lowerCAmelCase_   :				Optional[Any]								= do_rescale
        lowerCAmelCase_   :				Union[str, Any]								= rescale_factor
        lowerCAmelCase_   :				Dict								= do_normalize
        lowerCAmelCase_   :				List[Any]								= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowerCAmelCase_   :				List[Any]								= image_std if image_std is not None else OPENAI_CLIP_STD
        lowerCAmelCase_   :				Optional[Any]								= do_convert_rgb



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      np.ndarray    ,			SCREAMING_SNAKE_CASE_				:      Dict[str, int]    ,			SCREAMING_SNAKE_CASE_				:      PILImageResampling = PILImageResampling.BICUBIC    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[str, ChannelDimension]] = None    ,			**SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			):
        lowerCAmelCase_   :				List[str]								= get_size_dict(_snake_case    ,			default_to_square=_snake_case       )
        if "height" not in size or "width" not in size:
          raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"       )
        lowerCAmelCase_   :				Tuple								= (size['''height'''], size['''width'''])
        return resize(_snake_case    ,			size=_snake_case    ,			resample=_snake_case    ,			data_format=_snake_case    ,			**_snake_case       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      np.ndarray    ,			SCREAMING_SNAKE_CASE_				:      Union[int, float]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[str, ChannelDimension]] = None    ,			**SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			):
        return rescale(_snake_case    ,			scale=_snake_case    ,			data_format=_snake_case    ,			**_snake_case       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      np.ndarray    ,			SCREAMING_SNAKE_CASE_				:      Union[float, List[float]]    ,			SCREAMING_SNAKE_CASE_				:      Union[float, List[float]]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[str, ChannelDimension]] = None    ,			**SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			):
        return normalize(_snake_case    ,			mean=_snake_case    ,			std=_snake_case    ,			data_format=_snake_case    ,			**_snake_case       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      ImageInput    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Dict[str, int]] = None    ,			SCREAMING_SNAKE_CASE_				:      PILImageResampling = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[float] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[float, List[float]]] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[float, List[float]]] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[str, TensorType]] = None    ,			SCREAMING_SNAKE_CASE_				:      bool = None    ,			SCREAMING_SNAKE_CASE_				:      ChannelDimension = ChannelDimension.FIRST    ,			**SCREAMING_SNAKE_CASE_				:      Dict    ,			):
        lowerCAmelCase_   :				List[str]								= do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase_   :				Tuple								= resample if resample is not None else self.resample
        lowerCAmelCase_   :				int								= do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase_   :				List[str]								= rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase_   :				Dict								= do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase_   :				Tuple								= image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase_   :				str								= image_std if image_std is not None else self.image_std
        lowerCAmelCase_   :				Optional[int]								= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowerCAmelCase_   :				List[Any]								= size if size is not None else self.size
        lowerCAmelCase_   :				str								= get_size_dict(_snake_case    ,			default_to_square=_snake_case       )

        lowerCAmelCase_   :				Union[str, Any]								= make_list_of_images(_snake_case       )

        if not valid_images(_snake_case       ):
          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_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.'       )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
          lowerCAmelCase_   :				List[str]								= [convert_to_rgb(_snake_case       ) for image in images]

        # All transformations expect numpy arrays.
        lowerCAmelCase_   :				List[Any]								= [to_numpy_array(_snake_case       ) for image in images]

        if do_resize:
          lowerCAmelCase_   :				Optional[Any]								= [self.resize(image=_snake_case    ,			size=_snake_case    ,			resample=_snake_case       ) for image in images]

        if do_rescale:
          lowerCAmelCase_   :				Tuple								= [self.rescale(image=_snake_case    ,			scale=_snake_case       ) for image in images]

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

        lowerCAmelCase_   :				List[str]								= [to_channel_dimension_format(_snake_case    ,			_snake_case       ) for image in images]

        lowerCAmelCase_   :				Optional[Any]								= BatchFeature(data={'pixel_values': images}    ,			tensor_type=_snake_case       )

        return encoded_outputs










"""simple docstring"""







import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				debug_launcher(test_script.main						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Tuple:






				"""simple docstring"""



				debug_launcher(test_ops.main						)
