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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


A_      =      logging.get_logger(__name__)






class      __lowercase					(    _A    ):
						lowercase					 =     ['''pixel_values''']

						def __init__(	self				:      Tuple							,							__lowerCamelCase				:      Any = True							,							__lowerCamelCase				:      Tuple = None							,							__lowerCamelCase				:      str = PILImageResampling.BILINEAR							,							__lowerCamelCase				:      str = True							,							__lowerCamelCase				:      Any = 1 / 2_55							,							__lowerCamelCase				:      List[Any] = True							,							__lowerCamelCase				:      str = None							,							__lowerCamelCase				:      int = True							,							**__lowerCamelCase				:      Union[str, Any]							,							):


									'''simple docstring'''

									super().__init__(**A__   )
									lowercase							=		size if size is not None else {"""shortest_edge""": 2_24}
									lowercase							=		get_size_dict(A__							,							default_to_square=A__   )
									lowercase							=		crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
									lowercase							=		get_size_dict(A__							,							param_name='''crop_size'''   )

									lowercase							=		do_resize
									lowercase							=		size
									lowercase							=		resample
									lowercase							=		do_rescale
									lowercase							=		rescale_factor
									lowercase							=		do_center_crop
									lowercase							=		crop_size
									lowercase							=		do_flip_channel_order

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[Any] = PIL.Image.BILINEAR							,							__lowerCamelCase				:      Optional[Any] = None							,							**__lowerCamelCase				:      Tuple							,							):


									'''simple docstring'''

									lowercase							=		get_size_dict(A__							,							default_to_square=A__   )
									if "shortest_edge" not in size:
												raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'   )
									lowercase							=		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        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Dict = None							,							**__lowerCamelCase				:      List[str]							,							):


									'''simple docstring'''

									lowercase							=		get_size_dict(A__   )
									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()}'   )
									return center_crop(A__							,							size=(size['''height'''], size['''width'''])							,							data_format=A__							,							**A__   )

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Any = None							,							**__lowerCamelCase				:      List[str]							,							):


									'''simple docstring'''

									return rescale(A__							,							scale=A__							,							data_format=A__							,							**A__   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      List[str] = None   ):


									'''simple docstring'''

									return flip_channel_order(A__							,							data_format=A__   )

						def        __a		(	self				:      int							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Tuple = None							,							__lowerCamelCase				:      Optional[int] = None							,							__lowerCamelCase				:      int = None							,							__lowerCamelCase				:      Tuple = None							,							__lowerCamelCase				:      Optional[Any] = None							,							__lowerCamelCase				:      Union[str, Any] = None							,							__lowerCamelCase				:      Union[str, Any] = None							,							__lowerCamelCase				:      List[Any] = None							,							__lowerCamelCase				:      Optional[Any] = None							,							__lowerCamelCase				:      List[Any] = ChannelDimension.FIRST							,							**__lowerCamelCase				:      Dict							,							):


									'''simple docstring'''

									lowercase							=		do_resize if do_resize is not None else self.do_resize
									lowercase							=		resample if resample is not None else self.resample
									lowercase							=		do_rescale if do_rescale is not None else self.do_rescale
									lowercase							=		rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase							=		do_center_crop if do_center_crop is not None else self.do_center_crop
									lowercase							=		(
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									lowercase							=		size if size is not None else self.size
									lowercase							=		get_size_dict(A__							,							default_to_square=A__   )
									lowercase							=		crop_size if crop_size is not None else self.crop_size
									lowercase							=		get_size_dict(A__							,							param_name='''crop_size'''   )

									lowercase							=		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_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

									if do_center_crop and crop_size is None:
												raise ValueError('''Crop size must be specified if do_center_crop is True.'''   )

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

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

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

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

									# the pretrained checkpoints assume images are BGR, not RGB
									if do_flip_channel_order:
												lowercase							=		[self.flip_channel_order(image=A__   ) for image in images]

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

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



						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Optional[Any] = None   ):


									'''simple docstring'''

									lowercase							=		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__   ):
															lowercase							=		target_sizes.numpy()

												lowercase							=		[]

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

									return semantic_segmentation






from __future__ import annotations

from collections import deque






class      __lowercase					:

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      list[str]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		[]
									self.adlist.append(
									    {'''value''': '''''', '''next_states''': [], '''fail_state''': 0, '''output''': []}   )

									for keyword in keywords:
												self.add_keyword(__lowerCamelCase   )
									self.set_fail_transitions()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      str   )							->		int | None:


									'''simple docstring'''

									for state in self.adlist[current_state]["next_states"]:
												if char == self.adlist[state]["value"]:
															return state
									return None

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      str   )							->		None:


									'''simple docstring'''

									lowercase							=		0
									for character in keyword:
												lowercase							=		self.find_next_state(__lowerCamelCase							,							__lowerCamelCase   )
												if next_state is None:
															self.adlist.append(
															    {
															        '''value''': character,
															        '''next_states''': [],
															        '''fail_state''': 0,
															        '''output''': [],
															    }   )
															self.adlist[current_state]["next_states"].append(len(self.adlist   ) - 1   )
															lowercase							=		len(self.adlist   ) - 1
												else:
															lowercase							=		next_state
									self.adlist[current_state]["output"].append(__lowerCamelCase   )

						def        __a		(	self				:      int   )							->		None:


									'''simple docstring'''

									lowercase							=		deque()
									for node in self.adlist[0]["next_states"]:
												q.append(__lowerCamelCase   )
												lowercase							=		0
									while q:
												lowercase							=		q.popleft()
												for child in self.adlist[r]["next_states"]:
															q.append(__lowerCamelCase   )
															lowercase							=		self.adlist[r]['''fail_state''']
															while (
															    self.find_next_state(__lowerCamelCase							,							self.adlist[child]['''value''']   ) is None
															    and state != 0
															):
																		lowercase							=		self.adlist[state]['''fail_state''']
															lowercase							=		self.find_next_state(
															    __lowerCamelCase							,							self.adlist[child]['''value''']   )
															if self.adlist[child]["fail_state"] is None:
																		lowercase							=		0
															lowercase							=		(
															    self.adlist[child]['''output''']
															    + self.adlist[self.adlist[child]['''fail_state''']]['''output''']
															)



						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      str   )							->		dict[str, list[int]]:


									'''simple docstring'''

									lowercase							=		{}  # returns a dict with keywords and list of its occurrences
									lowercase							=		0
									for i in range(len(__lowerCamelCase   )   ):
												while (
												    self.find_next_state(__lowerCamelCase							,							string[i]   ) is None
												    and current_state != 0
												):
															lowercase							=		self.adlist[current_state]['''fail_state''']
												lowercase							=		self.find_next_state(__lowerCamelCase							,							string[i]   )
												if next_state is None:
															lowercase							=		0
												else:
															lowercase							=		next_state
															for key in self.adlist[current_state]["output"]:
																		if key not in result:
																					lowercase							=		[]
																		result[key].append(i - len(__lowerCamelCase   ) + 1   )
									return result


if __name__ == "__main__":
						import doctest

						doctest.testmod()





