





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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_  							=				logging.get_logger(__name__)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	["pixel_values"]




   def __init__(					self,	__magic_name__ = True,	__magic_name__ = None,	__magic_name__ = 0.9,	__magic_name__ = PILImageResampling.BICUBIC,	__magic_name__ = True,	__magic_name__ = None,	__magic_name__ = 1 / 255,	__magic_name__ = True,	__magic_name__ = True,	__magic_name__ = None,	__magic_name__ = None,	**__magic_name__,	)				->   None:






       """simple docstring"""
       super().__init__(**__magic_name__							)
       UpperCamelCase__							:       Optional[Any]      =   size if size is not None else {'''shortest_edge''': 224}
       UpperCamelCase__							:       Dict      =   get_size_dict(__magic_name__,	default_to_square=__magic_name__							)
       UpperCamelCase__							:       Any      =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       UpperCamelCase__							:       str      =   get_size_dict(__magic_name__,	param_name='''crop_size'''							)

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = PILImageResampling.BICUBIC,	__magic_name__ = None,	**__magic_name__,	)				->   np.ndarray:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   get_size_dict(__magic_name__,	default_to_square=__magic_name__							)
       if "shortest_edge" not in size and ("height" not in size or "width" not in size):
           raise ValueError(f"size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"							)
       if crop_pct is not None:
           if "shortest_edge" in size:
               UpperCamelCase__							:       Union[str, Any]      =   int(size['''shortest_edge'''] / crop_pct							)
           elif "height" in size and "width" in size:
               if size["height"] == size["width"]:
                   UpperCamelCase__							:       Optional[int]      =   int(size['''height'''] / crop_pct							)
               else:
                   UpperCamelCase__							:       Any      =   (int(size['''height'''] / crop_pct							), int(size['''width'''] / crop_pct							))
           else:
               raise ValueError('''Invalid size for resize: {}'''.format(__magic_name__							)							)

           UpperCamelCase__							:       List[Any]      =   get_resize_output_image_size(__magic_name__,	size=__magic_name__,	default_to_square=__magic_name__							)
       else:
           if "shortest_edge" in size:
               UpperCamelCase__							:       Union[str, Any]      =   get_resize_output_image_size(__magic_name__,	size=size['''shortest_edge'''],	default_to_square=__magic_name__							)
           elif "height" in size and "width" in size:
               UpperCamelCase__							:       List[str]      =   (size['''height'''], size['''width'''])
           else:
               raise ValueError('''Invalid size for resize: {}'''.format(__magic_name__							)							)

       return resize(__magic_name__,	size=__magic_name__,	resample=__magic_name__,	data_format=__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	**__magic_name__,	)				->   np.ndarray:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   get_size_dict(__magic_name__							)
       if "height" not in size or "width" not in size:
           raise ValueError(f"size must contain 'height' and 'width' as keys. Got {size.keys()}"							)
       return center_crop(__magic_name__,	size=(size['''height'''], size['''width''']),	data_format=__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	**__magic_name__,	)				->   Optional[int]:






       """simple docstring"""
       return rescale(__magic_name__,	scale=__magic_name__,	data_format=__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	**__magic_name__,	)				->   np.ndarray:






       """simple docstring"""
       return normalize(__magic_name__,	mean=__magic_name__,	std=__magic_name__,	data_format=__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = ChannelDimension.FIRST,	**__magic_name__,	)				->   PIL.Image.Image:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:       Dict      =   crop_pct if crop_pct is not None else self.crop_pct
       UpperCamelCase__							:       Any      =   resample if resample is not None else self.resample
       UpperCamelCase__							:       Dict      =   do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase__							:       Union[str, Any]      =   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__							:       Dict      =   do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase__							:       Dict      =   image_mean if image_mean is not None else self.image_mean
       UpperCamelCase__							:       Union[str, Any]      =   image_std if image_std is not None else self.image_std

       UpperCamelCase__							:       str      =   size if size is not None else self.size
       UpperCamelCase__							:       List[Any]      =   get_size_dict(__magic_name__,	default_to_square=__magic_name__							)
       UpperCamelCase__							:       Union[str, Any]      =   crop_size if crop_size is not None else self.crop_size
       UpperCamelCase__							:       Any      =   get_size_dict(__magic_name__,	param_name='''crop_size'''							)

       UpperCamelCase__							:       Any      =   make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           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_pct is None:
           raise ValueError('''Crop_pct 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.
       UpperCamelCase__							:       List[Any]      =   [to_numpy_array(__magic_name__							) for image in images]

       if do_resize:
           UpperCamelCase__							:       Optional[int]      =   [self.resize(image=__magic_name__,	size=__magic_name__,	crop_pct=__magic_name__,	resample=__magic_name__							) for image in images]

       if do_center_crop:
           UpperCamelCase__							:       Any      =   [self.center_crop(image=__magic_name__,	size=__magic_name__							) for image in images]

       if do_rescale:
           UpperCamelCase__							:       Optional[int]      =   [self.rescale(image=__magic_name__,	scale=__magic_name__							) for image in images]

       if do_normalize:
           UpperCamelCase__							:       Union[str, Any]      =   [self.normalize(image=__magic_name__,	mean=__magic_name__,	std=__magic_name__							) for image in images]

       UpperCamelCase__							:       Any      =   [to_channel_dimension_format(__magic_name__,	__magic_name__							) for image in images]

       UpperCamelCase__							:       str      =   {'''pixel_values''': images}
       return BatchFeature(data=__magic_name__,	tensor_type=__magic_name__							)








# Lint as: python3
import itertools
import os
import re


UpperCAmelCase_  							=				re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCAmelCase_  							=				re.compile(R'([a-z\d])([A-Z])')

UpperCAmelCase_  							=				re.compile(R'(?<!_)_(?!_)')
UpperCAmelCase_  							=				re.compile(R'(_{2,})')

UpperCAmelCase_  							=				R'^\w+(\.\w+)*$'

UpperCAmelCase_  							=				R'<>:/\|?*'




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]   )   ->			List[Any]:
    UpperCamelCase__							:       int      =   _uppercase_uppercase_re.sub(r'''\1_\2'''	,     __UpperCAmelCase   )
    UpperCamelCase__							:       Tuple      =   _lowercase_uppercase_re.sub(r'''\1_\2'''	,     __UpperCAmelCase   )
    return name.lower()




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any   )   ->			Any:
    UpperCamelCase__							:       Optional[int]      =   _single_underscore_re.split(__UpperCAmelCase   )
    UpperCamelCase__							:       Dict      =   [_multiple_underscores_re.split(__UpperCAmelCase   ) for n in name]
    return "".join(n.capitalize() for n in itertools.chain.from_iterable(__UpperCAmelCase   ) if n != ''''''   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int   )   ->			Union[str, Any]:
    if os.path.basename(__UpperCAmelCase   ) != name:
        raise ValueError(f"Should be a dataset name, not a path: {name}"   )
    return camelcase_to_snakecase(__UpperCAmelCase   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Optional[int]   )   ->			Optional[Any]:
    if os.path.basename(__UpperCAmelCase   ) != name:
        raise ValueError(f"Should be a dataset name, not a path: {name}"   )
    if not re.match(_split_re	,     __UpperCAmelCase   ):
        raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."   )
    return f"{filename_prefix_for_name(__UpperCAmelCase   )}-{split}"




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    List[Any]=None   )   ->			Union[str, Any]:
    UpperCamelCase__							:       Optional[Any]      =   filename_prefix_for_split(__UpperCAmelCase	,     __UpperCAmelCase   )
    if filetype_suffix:
        prefix += f".{filetype_suffix}"
    UpperCamelCase__							:       Dict      =   os.path.join(__UpperCAmelCase	,     __UpperCAmelCase   )
    return f"{filepath}*"





def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    str=None	,     __UpperCAmelCase:    Tuple=None   )   ->			Tuple:
    UpperCamelCase__							:       str      =   filename_prefix_for_split(__UpperCAmelCase	,     __UpperCAmelCase   )
    UpperCamelCase__							:       Tuple      =   os.path.join(__UpperCAmelCase	,     __UpperCAmelCase   )

    if shard_lengths:
        UpperCamelCase__							:       Optional[Any]      =   len(__UpperCAmelCase   )
        UpperCamelCase__							:       List[str]      =   [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(__UpperCAmelCase   )]
        if filetype_suffix:
            UpperCamelCase__							:       Union[str, Any]      =   [filename + f".{filetype_suffix}" for filename in filenames]
        return filenames
    else:
        UpperCamelCase__							:       Tuple      =   prefix
        if filetype_suffix:
            filename += f".{filetype_suffix}"
        return [filename]

