


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,
    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


__UpperCAmelCase : Optional[int] 			=				logging.get_logger(__name__)


if is_vision_available():
  import PIL



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Dict							=					["pixel_values"]






       def __init__(     self ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = 1 / 255 ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = True ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		size if size is not None else {'''shortest_edge''': 224}
              UpperCamelCase    :	Dict   	=		get_size_dict(__SCREAMING_SNAKE_CASE ,					default_to_square=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Any   	=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
              UpperCamelCase    :	Dict   	=		get_size_dict(__SCREAMING_SNAKE_CASE ,					default_to_square=__SCREAMING_SNAKE_CASE ,					param_name='''crop_size'''						)

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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		get_size_dict(__SCREAMING_SNAKE_CASE ,					default_to_square=__SCREAMING_SNAKE_CASE						)
              if "shortest_edge" not in size:
                     raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
              UpperCamelCase    :	Optional[int]   	=		get_resize_output_image_size(__SCREAMING_SNAKE_CASE ,					size=size['''shortest_edge'''] ,					default_to_square=__SCREAMING_SNAKE_CASE						)
              return resize(__SCREAMING_SNAKE_CASE ,					size=__SCREAMING_SNAKE_CASE ,					resample=__SCREAMING_SNAKE_CASE ,					data_format=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		get_size_dict(__SCREAMING_SNAKE_CASE						)
              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(__SCREAMING_SNAKE_CASE ,					size=(size['''height'''], size['''width''']) ,					data_format=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




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

              UpperCamelCase    :	Any   	=		make_list_of_images(__SCREAMING_SNAKE_CASE						)

              if not valid_images(__SCREAMING_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:
                     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.'''						)

              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:
                     UpperCamelCase    :	Any   	=		[convert_to_rgb(__SCREAMING_SNAKE_CASE						) for image in images]

              # All transformations expect numpy arrays.
              UpperCamelCase    :	Tuple   	=		[to_numpy_array(__SCREAMING_SNAKE_CASE						) for image in images]

              if do_resize:
                     UpperCamelCase    :	Union[str, Any]   	=		[self.resize(image=__SCREAMING_SNAKE_CASE ,					size=__SCREAMING_SNAKE_CASE ,					resample=__SCREAMING_SNAKE_CASE						) for image in images]

              if do_center_crop:
                     UpperCamelCase    :	str   	=		[self.center_crop(image=__SCREAMING_SNAKE_CASE ,					size=__SCREAMING_SNAKE_CASE						) for image in images]

              if do_rescale:
                     UpperCamelCase    :	str   	=		[self.rescale(image=__SCREAMING_SNAKE_CASE ,					scale=__SCREAMING_SNAKE_CASE						) for image in images]

              if do_normalize:
                     UpperCamelCase    :	Optional[int]   	=		[self.normalize(image=__SCREAMING_SNAKE_CASE ,					mean=__SCREAMING_SNAKE_CASE ,					std=__SCREAMING_SNAKE_CASE						) for image in images]

              UpperCamelCase    :	Any   	=		[to_channel_dimension_format(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						) for image in images]

              UpperCamelCase    :	List[str]   	=		{'''pixel_values''': images}
              return BatchFeature(data=__SCREAMING_SNAKE_CASE ,					tensor_type=__SCREAMING_SNAKE_CASE						)





from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState


def      a	(							SCREAMING_SNAKE_CASE_      :		bool = True							,			*SCREAMING_SNAKE_CASE_      :		List[str]							,			**SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





       if not is_tqdm_available():
              raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.'''       )
       UpperCamelCase    :	int   	=		False
       if main_process_only:
              UpperCamelCase    :	int   	=		PartialState().local_process_index == 0
       return _tqdm(*SCREAMING_SNAKE_CASE_							,			**SCREAMING_SNAKE_CASE_							,			disable=SCREAMING_SNAKE_CASE_       )

