




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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


UpperCamelCase__ :   Tuple						=							logging.get_logger(__name__)





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  )       ->  List[List[ImageInput]]:







			"""simple docstring"""





			if isinstance(lowerCamelCase_  ,							(list, tuple)  ) and isinstance(videos[0]  ,							(list, tuple)  ) and is_valid_image(videos[0][0]  ):
						return videos

			elif isinstance(lowerCamelCase_  ,							(list, tuple)  ) and is_valid_image(videos[0]  ):
						return [videos]

			elif is_valid_image(lowerCamelCase_  ):
						return [[videos]]

			raise ValueError(F'Could not make batched video from {videos}'  )







class 				lowerCAmelCase_       (					lowercase_							):
				__a     :       int						=     ["""pixel_values"""]






				def __init__(			self      ,snake_case__ = True      ,snake_case__ = None      ,snake_case__ = PILImageResampling.BILINEAR      ,snake_case__ = True      ,snake_case__ = None      ,snake_case__ = True      ,snake_case__ = 1 / 255      ,snake_case__ = True      ,snake_case__ = None      ,snake_case__ = None      ,**snake_case__      ,):
							super().__init__(**snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      size if size is not None else {'shortest_edge': 224}
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      get_size_dict(snake_case__      ,default_to_square=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      crop_size if crop_size is not None else {'height': 224, 'width': 224}
							SCREAMING_SNAKE_CASE_   : Tuple					  =      get_size_dict(snake_case__      ,param_name='crop_size' )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      do_resize
							SCREAMING_SNAKE_CASE_   : Dict					  =      size
							SCREAMING_SNAKE_CASE_   : List[str]					  =      do_center_crop
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      crop_size
							SCREAMING_SNAKE_CASE_   : Dict					  =      resample
							SCREAMING_SNAKE_CASE_   : Any					  =      do_rescale
							SCREAMING_SNAKE_CASE_   : Tuple					  =      rescale_factor
							SCREAMING_SNAKE_CASE_   : int					  =      do_normalize
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							SCREAMING_SNAKE_CASE_   : Tuple					  =      image_std if image_std is not None else IMAGENET_STANDARD_STD






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ = PILImageResampling.BILINEAR      ,snake_case__ = None      ,**snake_case__      ,):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      get_size_dict(snake_case__      ,default_to_square=snake_case__ )
							if "shortest_edge" in size:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      get_resize_output_image_size(snake_case__      ,size['shortest_edge']      ,default_to_square=snake_case__ )
							elif "height" in size and "width" in size:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      (size['height'], size['width'])
							else:
										raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
							return resize(snake_case__      ,size=snake_case__      ,resample=snake_case__      ,data_format=snake_case__      ,**snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ = None      ,**snake_case__      ,):
							SCREAMING_SNAKE_CASE_   : Any					  =      get_size_dict(snake_case__ )
							if "height" not in size or "width" not in size:
										raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}' )
							return center_crop(snake_case__      ,size=(size['height'], size['width'])      ,data_format=snake_case__      ,**snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ = None      ,**snake_case__      ,):
							return rescale(snake_case__      ,scale=snake_case__      ,data_format=snake_case__      ,**snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ = None      ,**snake_case__      ,):
							return normalize(snake_case__      ,mean=snake_case__      ,std=snake_case__      ,data_format=snake_case__      ,**snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = ChannelDimension.FIRST      ,):







							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_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.
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      to_numpy_array(snake_case__ )

							if do_resize:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.resize(image=snake_case__      ,size=snake_case__      ,resample=snake_case__ )

							if do_center_crop:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.center_crop(snake_case__      ,size=snake_case__ )

							if do_rescale:
										SCREAMING_SNAKE_CASE_   : Any					  =      self.rescale(image=snake_case__      ,scale=snake_case__ )

							if do_normalize:
										SCREAMING_SNAKE_CASE_   : Dict					  =      self.normalize(image=snake_case__      ,mean=snake_case__      ,std=snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      to_channel_dimension_format(snake_case__      ,snake_case__ )
							return image






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = ChannelDimension.FIRST      ,**snake_case__      ,):
							SCREAMING_SNAKE_CASE_   : Any					  =      do_resize if do_resize is not None else self.do_resize
							SCREAMING_SNAKE_CASE_   : List[str]					  =      resample if resample is not None else self.resample
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      do_center_crop if do_center_crop is not None else self.do_center_crop
							SCREAMING_SNAKE_CASE_   : int					  =      do_rescale if do_rescale is not None else self.do_rescale
							SCREAMING_SNAKE_CASE_   : List[str]					  =      rescale_factor if rescale_factor is not None else self.rescale_factor
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      do_normalize if do_normalize is not None else self.do_normalize
							SCREAMING_SNAKE_CASE_   : str					  =      image_mean if image_mean is not None else self.image_mean
							SCREAMING_SNAKE_CASE_   : Tuple					  =      image_std if image_std is not None else self.image_std

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

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

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      make_batched(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      [
							    [
							        self._preprocess_image(
							            image=snake_case__      ,do_resize=snake_case__      ,size=snake_case__      ,resample=snake_case__      ,do_center_crop=snake_case__      ,crop_size=snake_case__      ,do_rescale=snake_case__      ,rescale_factor=snake_case__      ,do_normalize=snake_case__      ,image_mean=snake_case__      ,image_std=snake_case__      ,data_format=snake_case__      ,)
							        for img in video
							    ]
							    for video in videos
							]

							SCREAMING_SNAKE_CASE_   : Any					  =      {'pixel_values': videos}
							return BatchFeature(data=snake_case__      ,tensor_type=snake_case__ )







'''simple docstring'''

from __future__ import annotations

from collections import deque
class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       str  ,		UpperCamelCase       :       list[str]					):

             '''simple docstring'''







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

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



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Optional[Any]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					0
             for character in keyword:
                    lowercase__      =					self.find_next_state(UpperCamelCase  ,		UpperCamelCase					)
                    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(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					deque()
             for node in self.adlist[0]["next_states"]:
                    q.append(UpperCamelCase					)
                    lowercase__      =					0
             while q:
                    lowercase__      =					q.popleft()
                    for child in self.adlist[r]["next_states"]:
                           q.append(UpperCamelCase					)
                           lowercase__      =					self.adlist[r]['''fail_state''']
                           while (
                               self.find_next_state(UpperCamelCase  ,		self.adlist[child]['''value''']					) is None
                               and state != 0
                           ):
                                  lowercase__      =					self.adlist[state]['''fail_state''']
                           lowercase__      =					self.find_next_state(
                               UpperCamelCase  ,		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  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					{}  # returns a dict with keywords and list of its occurrences
             lowercase__      =					0
             for i in range(len(UpperCamelCase					)					):
                    while (
                        self.find_next_state(UpperCamelCase  ,		string[i]					) is None
                        and current_state != 0
                    ):
                           lowercase__      =					self.adlist[current_state]['''fail_state''']
                    lowercase__      =					self.find_next_state(UpperCamelCase  ,		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(UpperCamelCase					) + 1					)
             return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()




