




# 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__       (						A__       :					Dict	):





       '''simple docstring'''



       __lowerCamelCase   =						_uppercase_uppercase_re.sub(R"""\1_\2"""  ,			A__	)
       __lowerCamelCase   =						_lowercase_uppercase_re.sub(R"""\1_\2"""  ,			A__	)
       return name.lower()




def 					lowerCamelCase__       (						A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						_single_underscore_re.split(A__	)
       __lowerCamelCase   =						[_multiple_underscores_re.split(A__	) for n in name]
       return "".join(n.capitalize() for n in itertools.chain.from_iterable(A__	) if n != """"""	)




def 					lowerCamelCase__       (						A__       :					Optional[Any]	):





       '''simple docstring'''



       if os.path.basename(A__	) != name:
              raise ValueError(f'Should be a dataset name, not a path: {name}'	)
       return camelcase_to_snakecase(A__	)




def 					lowerCamelCase__       (						A__       :					Dict  ,			A__       :					Dict	):





       '''simple docstring'''



       if os.path.basename(A__	) != name:
              raise ValueError(f'Should be a dataset name, not a path: {name}'	)
       if not re.match(_split_re  ,			A__	):
              raise ValueError(f'Split name should match \'{_split_re}\'\' but got \'{split}\'.'	)
       return f'{filename_prefix_for_name(A__	)}-{split}'




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					Tuple  ,			A__       :					Optional[Any]  ,			A__       :					str=None	):





       '''simple docstring'''



       __lowerCamelCase   =						filename_prefix_for_split(A__  ,			A__	)
       if filetype_suffix:
              prefix += f'.{filetype_suffix}'
       __lowerCamelCase   =						os.path.join(A__  ,			A__	)
       return f'{filepath}*'




def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					List[Any]  ,			A__       :					int  ,			A__       :					int=None  ,			A__       :					Any=None	):





       '''simple docstring'''



       __lowerCamelCase   =						filename_prefix_for_split(A__  ,			A__	)
       __lowerCamelCase   =						os.path.join(A__  ,			A__	)

       if shard_lengths:
              __lowerCamelCase   =						len(A__	)
              __lowerCamelCase   =						[f'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(A__	)]
              if filetype_suffix:
                     __lowerCamelCase   =						[filename + f'.{filetype_suffix}' for filename in filenames]
              return filenames
       else:
              __lowerCamelCase   =						prefix
              if filetype_suffix:
                     filename += f'.{filetype_suffix}'
              return [filename]







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   ['pixel_values']
      def __init__(				self: Any    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: int = 8    ,       **UpperCamelCase_: Tuple    ,       ):
             super().__init__(**UpperCamelCase_   )

             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						do_pad
             __lowerCamelCase   =						pad_size
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: float    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Tuple   ):
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None   ):
             __lowerCamelCase,   __lowerCamelCase   =						get_image_size(UpperCamelCase_   )
             __lowerCamelCase   =						(old_height // size + 1) * size - old_height
             __lowerCamelCase   =						(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase_    ,       ((0, pad_height), (0, pad_width))    ,       mode="""symmetric"""    ,       data_format=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[float] = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCamelCase_: Any    ,       ):
             __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_pad if do_pad is not None else self.do_pad
             __lowerCamelCase   =						pad_size if pad_size is not None else self.pad_size

             __lowerCamelCase   =						make_list_of_images(UpperCamelCase_   )

             if not valid_images(UpperCamelCase_   ):
                    raise ValueError(
                        """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                        """torch.Tensor, tf.Tensor or jax.ndarray."""   )

             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.
             __lowerCamelCase   =						[to_numpy_array(UpperCamelCase_   ) for image in images]

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

             if do_pad:
                    __lowerCamelCase   =						[self.pad(UpperCamelCase_    ,       size=UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						[to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						{"""pixel_values""": images}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )

