



from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


_lowerCAmelCase	:   Tuple    =   logging.get_logger(__name__)


class 	__snake_case   ( __snake_case ):
		SCREAMING_SNAKE_CASE__								=	['pixel_values']
		def __init__(							self    ,a_ = True    ,a_ = None    ,a_ = PILImageResampling.BILINEAR    ,a_ = True    ,a_ = None    ,a_ = True    ,a_ = 1 / 255    ,a_ = True    ,a_ = None    ,a_ = None    ,**a_    ,):







						"""simple docstring"""
						super().__init__(**a_						)
						lowerCAmelCase__		  =  size if size is not None else {"shortest_edge": 256}
						lowerCAmelCase__		  =  get_size_dict(a_    ,default_to_square=a_						)
						lowerCAmelCase__		  =  crop_size if crop_size is not None else {"height": 224, "width": 224}
						lowerCAmelCase__		  =  get_size_dict(a_    ,param_name='crop_size'						)
						lowerCAmelCase__		  =  do_resize
						lowerCAmelCase__		  =  size
						lowerCAmelCase__		  =  resample
						lowerCAmelCase__		  =  do_center_crop
						lowerCAmelCase__		  =  crop_size
						lowerCAmelCase__		  =  do_rescale
						lowerCAmelCase__		  =  rescale_factor
						lowerCAmelCase__		  =  do_normalize
						lowerCAmelCase__		  =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__		  =  image_std if image_std is not None else IMAGENET_STANDARD_STD
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_ = PILImageResampling.BICUBIC    ,a_ = None    ,**a_    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  get_size_dict(a_    ,default_to_square=a_						)
						if "shortest_edge" not in size:
										raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'						)
						lowerCAmelCase__		  =  get_resize_output_image_size(a_    ,size=size['shortest_edge']    ,default_to_square=a_						)
						return resize(a_    ,size=a_    ,resample=a_    ,data_format=a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_ = None    ,**a_    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  get_size_dict(a_						)
						if "height" not in size or "width" not in size:
										raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'						)
						return center_crop(a_    ,size=(size['height'], size['width'])    ,data_format=a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_ = None    ,**a_						):







						"""simple docstring"""
						return rescale(a_    ,scale=a_    ,data_format=a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_ = None    ,**a_    ,):







						"""simple docstring"""
						return normalize(a_    ,mean=a_    ,std=a_    ,data_format=a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = ChannelDimension.FIRST    ,**a_    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__		  =  size if size is not None else self.size
						lowerCAmelCase__		  =  get_size_dict(a_    ,default_to_square=a_						)
						lowerCAmelCase__		  =  resample if resample is not None else self.resample
						lowerCAmelCase__		  =  do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__		  =  crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__		  =  get_size_dict(a_    ,param_name='crop_size'						)
						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__		  =  make_list_of_images(a_						)

						if not valid_images(a_						):
										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.'						)

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

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

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

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

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

						lowerCAmelCase__		  =  [to_channel_dimension_format(a_    ,a_						) for image in images]

						lowerCAmelCase__		  =  {"pixel_values": images}
						return BatchFeature(data=a_    ,tensor_type=a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = None						):







						"""simple docstring"""
						lowerCAmelCase__		  =  outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
										if len(a_						) != len(a_						):
														raise ValueError(
														    'Make sure that you pass in as many target sizes as the batch dimension of the logits'						)

										if is_torch_tensor(a_						):
														lowerCAmelCase__		  =  target_sizes.numpy()

										lowerCAmelCase__		  =  []

										for idx in range(len(a_						)						):
														lowerCAmelCase__		  =  torch.nn.functional.interpolate(
														    logits[idx].unsqueeze(dim=0						)    ,size=target_sizes[idx]    ,mode='bilinear'    ,align_corners=a_						)
														lowerCAmelCase__		  =  resized_logits[0].argmax(dim=0						)
														semantic_segmentation.append(a_						)
						else:
										lowerCAmelCase__		  =  logits.argmax(dim=1						)
										lowerCAmelCase__		  =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

						return semantic_segmentation


import functools
from typing import Any



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str]  )   ->			bool:






						'''simple docstring'''

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or len(_UpperCAmelCase  ) == 0:
												raise ValueError("the string should be not empty string"  )

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or not all(
						    isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) and len(_UpperCAmelCase  ) > 0 for item in words  ):
												raise ValueError("the words should be a list of non-empty strings"  )

						# Build trie
						_lowercase       :	dict[str, Any]           =   {}
						_lowercase       :	List[Any]           =   "WORD_KEEPER"

						for word in words:
												_lowercase       :	Optional[Any]           =   trie
												for c in word:
																		if c not in trie_node:
																								_lowercase       :	List[Any]           =   {}

																		_lowercase       :	Optional[int]           =   trie_node[c]

												_lowercase       :	str           =   True

						_lowercase       :	int           =   len(_UpperCAmelCase  )

						# Dynamic programming method
						@functools.cache
						def is_breakable(_UpperCAmelCase  :   int  ) -> bool:
												if index == len_string:
																		return True

												_lowercase       :	Union[str, Any]           =   trie
												for i in range(_UpperCAmelCase ,			_UpperCAmelCase  ):
																		_lowercase       :	List[str]           =   trie_node.get(string[i] ,			_UpperCAmelCase  )

																		if trie_node is None:
																								return False

																		if trie_node.get(_UpperCAmelCase ,			_UpperCAmelCase  ) and is_breakable(i + 1  ):
																								return True

												return False

						return is_breakable(0  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




