
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


__A							=logging.get_logger(__name__)




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					['pixel_values']



	def __init__(       self		,    lowercase = True		,    lowercase = None		,    lowercase = PILImageResampling.BICUBIC		,    lowercase = True		,    lowercase = 1 / 255		,    lowercase = True		,    lowercase = None		,    lowercase = None		,    lowercase = True		,    **lowercase		,    )							->			None:
					super().__init__(**lowercase					)
					lowerCamelCase_         =     size if size is not None else {"height": 384, "width": 384}
					lowerCamelCase_         =     get_size_dict(lowercase		,    default_to_square=lowercase					)

					lowerCamelCase_         =     do_resize
					lowerCamelCase_         =     size
					lowerCamelCase_         =     resample
					lowerCamelCase_         =     do_rescale
					lowerCamelCase_         =     rescale_factor
					lowerCamelCase_         =     do_normalize
					lowerCamelCase_         =     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					lowerCamelCase_         =     image_std if image_std is not None else OPENAI_CLIP_STD
					lowerCamelCase_         =     do_convert_rgb



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase = PILImageResampling.BICUBIC		,    lowercase = None		,    **lowercase		,    )							->			np.ndarray:
					lowerCamelCase_         =     get_size_dict(lowercase		,    default_to_square=lowercase					)
					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_         =     (size["height"], size["width"])
					return resize(lowercase		,    size=lowercase		,    resample=lowercase		,    data_format=lowercase		,    **lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase = None		,    **lowercase		,    )							->			Union[str, Any]:
					return rescale(lowercase		,    scale=lowercase		,    data_format=lowercase		,    **lowercase					)



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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = ChannelDimension.FIRST		,    **lowercase		,    )							->			PIL.Image.Image:
					lowerCamelCase_         =     do_resize if do_resize is not None else self.do_resize
					lowerCamelCase_         =     resample if resample is not None else self.resample
					lowerCamelCase_         =     do_rescale if do_rescale is not None else self.do_rescale
					lowerCamelCase_         =     rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCamelCase_         =     do_normalize if do_normalize is not None else self.do_normalize
					lowerCamelCase_         =     image_mean if image_mean is not None else self.image_mean
					lowerCamelCase_         =     image_std if image_std is not None else self.image_std
					lowerCamelCase_         =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					lowerCamelCase_         =     size if size is not None else self.size
					lowerCamelCase_         =     get_size_dict(lowercase		,    default_to_square=lowercase					)

					lowerCamelCase_         =     make_list_of_images(lowercase					)

					if not valid_images(lowercase					):
									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_         =     [convert_to_rgb(lowercase					) for image in images]

					# All transformations expect numpy arrays.
					lowerCamelCase_         =     [to_numpy_array(lowercase					) for image in images]

					if do_resize:
									lowerCamelCase_         =     [self.resize(image=lowercase		,    size=lowercase		,    resample=lowercase					) for image in images]

					if do_rescale:
									lowerCamelCase_         =     [self.rescale(image=lowercase		,    scale=lowercase					) for image in images]

					if do_normalize:
									lowerCamelCase_         =     [self.normalize(image=lowercase		,    mean=lowercase		,    std=lowercase					) for image in images]

					lowerCamelCase_         =     [to_channel_dimension_format(lowercase		,    lowercase					) for image in images]

					lowerCamelCase_         =     BatchFeature(data={"pixel_values": images}		,    tensor_type=lowercase					)

					return encoded_outputs




import re





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]"      ,					str_       )]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     split_input(str_       )
				return "".join(
				    ["".join([char.capitalize() for char in sub_str]       ) for sub_str in string_split]       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     split_input(lowerCamelCase__       )
								if upper:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.upper() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								else:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.lower() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								return res_str
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return to_simple_case(lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     to_simple_case(lowerCamelCase__       )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"_"       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"-"       )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


