




'''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 (
    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


A									=logging.get_logger(__name__)


if is_vision_available():
   import PIL


class     _a						(			__a				):
       __a       :  Optional[int]            =      ["""pixel_values"""]
       def __init__(			self		:			Union[str, Any]     ,       lowercase		:			bool = True     ,       lowercase		:			Dict[str, int] = None     ,       lowercase		:			PILImageResampling = PILImageResampling.BICUBIC     ,       lowercase		:			bool = True     ,       lowercase		:			Dict[str, int] = None     ,       lowercase		:			bool = True     ,       lowercase		:			Union[int, float] = 1 / 255     ,       lowercase		:			bool = True     ,       lowercase		:			Optional[Union[float, List[float]]] = None     ,       lowercase		:			Optional[Union[float, List[float]]] = None     ,       lowercase		:			bool = True     ,       **lowercase		:			Optional[int]     ,       ):



           '''simple docstring'''

           super().__init__(**lowercase       )
           UpperCAmelCase     =	size if size is not None else {'''shortest_edge''': 224}
           UpperCAmelCase     =	get_size_dict(lowercase     ,       default_to_square=lowercase       )
           UpperCAmelCase     =	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           UpperCAmelCase     =	get_size_dict(lowercase     ,       default_to_square=lowercase     ,       param_name='''crop_size'''       )

           UpperCAmelCase     =	do_resize
           UpperCAmelCase     =	size
           UpperCAmelCase     =	resample
           UpperCAmelCase     =	do_center_crop
           UpperCAmelCase     =	crop_size
           UpperCAmelCase     =	do_rescale
           UpperCAmelCase     =	rescale_factor
           UpperCAmelCase     =	do_normalize
           UpperCAmelCase     =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           UpperCAmelCase     =	image_std if image_std is not None else OPENAI_CLIP_STD
           UpperCAmelCase     =	do_convert_rgb
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			np.ndarray     ,       lowercase		:			Dict[str, int]     ,       lowercase		:			PILImageResampling = PILImageResampling.BICUBIC     ,       lowercase		:			Optional[Union[str, ChannelDimension]] = None     ,       **lowercase		:			Dict     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	get_size_dict(lowercase     ,       default_to_square=lowercase       )
           if "shortest_edge" not in size:
               raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"       )
           UpperCAmelCase     =	get_resize_output_image_size(lowercase     ,       size=size['''shortest_edge''']     ,       default_to_square=lowercase       )
           return resize(lowercase     ,       size=lowercase     ,       resample=lowercase     ,       data_format=lowercase     ,       **lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			np.ndarray     ,       lowercase		:			Dict[str, int]     ,       lowercase		:			Optional[Union[str, ChannelDimension]] = None     ,       **lowercase		:			Union[str, Any]     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	get_size_dict(lowercase       )
           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(lowercase     ,       size=(size['''height'''], size['''width'''])     ,       data_format=lowercase     ,       **lowercase       )
       def 					A   (			self		:			str     ,       lowercase		:			np.ndarray     ,       lowercase		:			Union[int, float]     ,       lowercase		:			Optional[Union[str, ChannelDimension]] = None     ,       **lowercase		:			Optional[int]     ,       ):



           '''simple docstring'''

           return rescale(lowercase     ,       scale=lowercase     ,       data_format=lowercase     ,       **lowercase       )
       def 					A   (			self		:			List[Any]     ,       lowercase		:			np.ndarray     ,       lowercase		:			Union[float, List[float]]     ,       lowercase		:			Union[float, List[float]]     ,       lowercase		:			Optional[Union[str, ChannelDimension]] = None     ,       **lowercase		:			str     ,       ):



           '''simple docstring'''

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







       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			ImageInput     ,       lowercase		:			bool = None     ,       lowercase		:			Dict[str, int] = None     ,       lowercase		:			PILImageResampling = None     ,       lowercase		:			bool = None     ,       lowercase		:			int = None     ,       lowercase		:			bool = None     ,       lowercase		:			float = None     ,       lowercase		:			bool = None     ,       lowercase		:			Optional[Union[float, List[float]]] = None     ,       lowercase		:			Optional[Union[float, List[float]]] = None     ,       lowercase		:			bool = None     ,       lowercase		:			Optional[Union[str, TensorType]] = None     ,       lowercase		:			Optional[ChannelDimension] = ChannelDimension.FIRST     ,       **lowercase		:			List[str]     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	do_resize if do_resize is not None else self.do_resize
           UpperCAmelCase     =	size if size is not None else self.size
           UpperCAmelCase     =	get_size_dict(lowercase     ,       param_name='''size'''     ,       default_to_square=lowercase       )
           UpperCAmelCase     =	resample if resample is not None else self.resample
           UpperCAmelCase     =	do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCAmelCase     =	crop_size if crop_size is not None else self.crop_size
           UpperCAmelCase     =	get_size_dict(lowercase     ,       param_name='''crop_size'''     ,       default_to_square=lowercase       )
           UpperCAmelCase     =	do_rescale if do_rescale is not None else self.do_rescale
           UpperCAmelCase     =	rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCAmelCase     =	do_normalize if do_normalize is not None else self.do_normalize
           UpperCAmelCase     =	image_mean if image_mean is not None else self.image_mean
           UpperCAmelCase     =	image_std if image_std is not None else self.image_std
           UpperCAmelCase     =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           UpperCAmelCase     =	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:
               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     =	[convert_to_rgb(lowercase       ) for image in images]

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

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

           if do_center_crop:
               UpperCAmelCase     =	[self.center_crop(image=lowercase     ,       size=lowercase       ) for image in images]

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

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

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

           UpperCAmelCase     =	{'''pixel_values''': images}
           return BatchFeature(data=lowercase     ,       tensor_type=lowercase       )










'''simple docstring'''







from __future__ import annotations





def 		snake_case_							(_a			:							str					,     _a			:							list[str] | None = None  ):
    UpperCAmelCase     =	word_bank or []
    # create a table
    UpperCAmelCase     =	len(_a  ) + 1

    UpperCAmelCase     =	[]
    for _ in range(_a  ):
        table.append([]  )
    # seed value
    UpperCAmelCase     =	[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(_a  ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(_a  )] == word:
                    UpperCAmelCase     =	[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(_a  )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(_a  )]:
        combination.reverse()

    return table[len(_a  )]


if __name__ == "__main__":
   print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
   print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
   print(
       all_construct(
           'hexagonosaurus',
           ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
       )
   )




