

'''simple docstring'''

from __future__ import annotations

from collections import deque




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Any					,      lowercase_			:					list[str]	)						->     Optional[Any]:
      UpperCAmelCase	:     list[dict]						 =			[]
      self.adlist.append(
          {'value': '', 'next_states': [], 'fail_state': 0, 'output': []}	)

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


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					int					,      lowercase_			:					str	)						->     str:
      for state in self.adlist[current_state]["next_states"]:
       if char == self.adlist[state]["value"]:
        return state
      return None


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					str	)						->     str:
      UpperCAmelCase	:     str						 =			0
      for character in keyword:
       UpperCAmelCase	:     Optional[Any]						 =			self.find_next_state(lowercase_					,      lowercase_	)
       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	)
        UpperCAmelCase	:     str						 =			len(self.adlist	) - 1
       else:
        UpperCAmelCase	:     Optional[int]						 =			next_state
      self.adlist[current_state]["output"].append(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Dict	)						->     Optional[Any]:
      UpperCAmelCase	:     deque						 =			deque()
      for node in self.adlist[0]["next_states"]:
       q.append(lowercase_	)
       UpperCAmelCase	:     Optional[Any]						 =			0
      while q:
       UpperCAmelCase	:     Optional[int]						 =			q.popleft()
       for child in self.adlist[r]["next_states"]:
        q.append(lowercase_	)
        UpperCAmelCase	:     Tuple						 =			self.adlist[r]["""fail_state"""]
        while (
            self.find_next_state(lowercase_					,      self.adlist[child]['value']	) is None
            and state != 0
        ):
         UpperCAmelCase	:     str						 =			self.adlist[state]["""fail_state"""]
        UpperCAmelCase	:     Tuple						 =			self.find_next_state(
            lowercase_					,      self.adlist[child]['value']	)
        if self.adlist[child]["fail_state"] is None:
         UpperCAmelCase	:     List[Any]						 =			0
        UpperCAmelCase	:     Union[str, Any]						 =			(
            self.adlist[child]["""output"""]
            + self.adlist[self.adlist[child]["""fail_state"""]]["""output"""]
        )




     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					str	)						->     List[Any]:
      UpperCAmelCase	:     dict						 =			{}  # returns a dict with keywords and list of its occurrences
      UpperCAmelCase	:     Any						 =			0
      for i in range(len(lowercase_	)	):
       while (
           self.find_next_state(lowercase_					,      string[i]	) is None
           and current_state != 0
       ):
        UpperCAmelCase	:     int						 =			self.adlist[current_state]["""fail_state"""]
       UpperCAmelCase	:     Optional[Any]						 =			self.find_next_state(lowercase_					,      string[i]	)
       if next_state is None:
        UpperCAmelCase	:     Any						 =			0
       else:
        UpperCAmelCase	:     Optional[int]						 =			next_state
        for key in self.adlist[current_state]["output"]:
         if key not in result:
          UpperCAmelCase	:     Optional[Any]						 =			[]
         result[key].append(i - len(lowercase_	) + 1	)
      return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()






'''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


lowercase__         =      logging.get_logger(__name__)


if is_vision_available():
 import PIL




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  ["""pixel_values"""]


     def __init__(		self			:					Dict					,      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_			:					Any					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Dict						 =			size if size is not None else {'shortest_edge': 224}
      UpperCAmelCase	:     Any						 =			get_size_dict(lowercase_					,      default_to_square=lowercase_	)
      UpperCAmelCase	:     List[str]						 =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
      UpperCAmelCase	:     Any						 =			get_size_dict(lowercase_					,      default_to_square=lowercase_					,      param_name='crop_size'	)

      UpperCAmelCase	:     Optional[int]						 =			do_resize
      UpperCAmelCase	:     str						 =			size
      UpperCAmelCase	:     str						 =			resample
      UpperCAmelCase	:     Union[str, Any]						 =			do_center_crop
      UpperCAmelCase	:     Any						 =			crop_size
      UpperCAmelCase	:     Optional[Any]						 =			do_rescale
      UpperCAmelCase	:     List[Any]						 =			rescale_factor
      UpperCAmelCase	:     Any						 =			do_normalize
      UpperCAmelCase	:     int						 =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      UpperCAmelCase	:     str						 =			image_std if image_std is not None else OPENAI_CLIP_STD
      UpperCAmelCase	:     Union[str, Any]						 =			do_convert_rgb


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BICUBIC					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					int					,      )						->     np.ndarray:
      UpperCAmelCase	:     Optional[int]						 =			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	:     str						 =			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        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Tuple					,      )						->     np.ndarray:
      UpperCAmelCase	:     List[Any]						 =			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        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Union[int, float]					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     Union[str, Any]:
      return rescale(lowercase_					,      scale=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Union[float, List[float]]					,      lowercase_			:					Union[float, List[float]]					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Optional[int]					,      )						->     np.ndarray:
      return normalize(lowercase_					,      mean=lowercase_					,      std=lowercase_					,      data_format=lowercase_					,      **lowercase_	)




     def        UpperCAmelCase_	(		self			:					Union[str, 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[Any]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Any						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     List[str]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_					,      param_name='size'					,      default_to_square=lowercase_	)
      UpperCAmelCase	:     int						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     List[Any]						 =			do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCAmelCase	:     List[Any]						 =			crop_size if crop_size is not None else self.crop_size
      UpperCAmelCase	:     Union[str, Any]						 =			get_size_dict(lowercase_					,      param_name='crop_size'					,      default_to_square=lowercase_	)
      UpperCAmelCase	:     Union[str, Any]						 =			do_rescale if do_rescale is not None else self.do_rescale
      UpperCAmelCase	:     Optional[int]						 =			rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCAmelCase	:     Tuple						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			image_mean if image_mean is not None else self.image_mean
      UpperCAmelCase	:     Optional[Any]						 =			image_std if image_std is not None else self.image_std
      UpperCAmelCase	:     List[Any]						 =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      UpperCAmelCase	:     Union[str, Any]						 =			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	:     int						 =			[convert_to_rgb(lowercase_	) for image in images]

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     List[Any]						 =			[to_numpy_array(lowercase_	) for image in images]

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

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

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

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

      UpperCAmelCase	:     Optional[int]						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

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




