


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	:		Optional[int]    =       logging.get_logger(__name__)


if is_vision_available():
	import PIL



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			['''pixel_values''']

					def __init__(self   :						Dict				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Dict[str, int] = None				, _UpperCAmelCase   :						PILImageResampling = PILImageResampling.BICUBIC				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Dict[str, int] = None				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Union[int, float] = 1 / 255				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Optional[Union[float, List[float]]] = None				, _UpperCAmelCase   :						Optional[Union[float, List[float]]] = None				, _UpperCAmelCase   :						bool = True				, **_UpperCAmelCase   :						int				, )						->       None:




							"""simple docstring"""


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

							lowercase__					      =		do_resize
							lowercase__					      =		size
							lowercase__					      =		resample
							lowercase__					      =		do_center_crop
							lowercase__					      =		crop_size
							lowercase__					      =		do_rescale
							lowercase__					      =		rescale_factor
							lowercase__					      =		do_normalize
							lowercase__					      =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							lowercase__					      =		image_std if image_std is not None else OPENAI_CLIP_STD
							lowercase__					      =		do_convert_rgb

					def   lowerCamelCase__					(self   :						Tuple				, _UpperCAmelCase   :						np.ndarray				, _UpperCAmelCase   :						Dict[str, int]				, _UpperCAmelCase   :						PILImageResampling = PILImageResampling.BICUBIC				, _UpperCAmelCase   :						Optional[Union[str, ChannelDimension]] = None				, **_UpperCAmelCase   :						str				, )						->       np.ndarray:




							"""simple docstring"""


							lowercase__					      =		get_size_dict(_UpperCAmelCase				, default_to_square=_UpperCAmelCase		)
							if "shortest_edge" not in size:
									raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
							lowercase__					      =		get_resize_output_image_size(_UpperCAmelCase				, size=size["""shortest_edge"""]				, default_to_square=_UpperCAmelCase		)
							return resize(_UpperCAmelCase				, size=_UpperCAmelCase				, resample=_UpperCAmelCase				, data_format=_UpperCAmelCase				, **_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[int]				, _UpperCAmelCase   :						np.ndarray				, _UpperCAmelCase   :						Dict[str, int]				, _UpperCAmelCase   :						Optional[Union[str, ChannelDimension]] = None				, **_UpperCAmelCase   :						Tuple				, )						->       np.ndarray:




							"""simple docstring"""


							lowercase__					      =		get_size_dict(_UpperCAmelCase		)
							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(_UpperCAmelCase				, size=(size["""height"""], size["""width"""])				, data_format=_UpperCAmelCase				, **_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						np.ndarray				, _UpperCAmelCase   :						Union[int, float]				, _UpperCAmelCase   :						Optional[Union[str, ChannelDimension]] = None				, **_UpperCAmelCase   :						Dict				, )						->       Optional[int]:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						np.ndarray				, _UpperCAmelCase   :						Union[float, List[float]]				, _UpperCAmelCase   :						Union[float, List[float]]				, _UpperCAmelCase   :						Optional[Union[str, ChannelDimension]] = None				, **_UpperCAmelCase   :						Union[str, Any]				, )						->       np.ndarray:




							"""simple docstring"""


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


					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						ImageInput				, _UpperCAmelCase   :						bool = None				, _UpperCAmelCase   :						Dict[str, int] = None				, _UpperCAmelCase   :						PILImageResampling = None				, _UpperCAmelCase   :						bool = None				, _UpperCAmelCase   :						int = None				, _UpperCAmelCase   :						bool = None				, _UpperCAmelCase   :						float = None				, _UpperCAmelCase   :						bool = None				, _UpperCAmelCase   :						Optional[Union[float, List[float]]] = None				, _UpperCAmelCase   :						Optional[Union[float, List[float]]] = None				, _UpperCAmelCase   :						bool = None				, _UpperCAmelCase   :						Optional[Union[str, TensorType]] = None				, _UpperCAmelCase   :						Optional[ChannelDimension] = ChannelDimension.FIRST				, **_UpperCAmelCase   :						str				, )						->       PIL.Image.Image:




							"""simple docstring"""


							lowercase__					      =		do_resize if do_resize is not None else self.do_resize
							lowercase__					      =		size if size is not None else self.size
							lowercase__					      =		get_size_dict(_UpperCAmelCase				, param_name="""size"""				, default_to_square=_UpperCAmelCase		)
							lowercase__					      =		resample if resample is not None else self.resample
							lowercase__					      =		do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase__					      =		crop_size if crop_size is not None else self.crop_size
							lowercase__					      =		get_size_dict(_UpperCAmelCase				, param_name="""crop_size"""				, default_to_square=_UpperCAmelCase		)
							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_normalize if do_normalize is not None else self.do_normalize
							lowercase__					      =		image_mean if image_mean is not None else self.image_mean
							lowercase__					      =		image_std if image_std is not None else self.image_std
							lowercase__					      =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							lowercase__					      =		make_list_of_images(_UpperCAmelCase		)

							if not valid_images(_UpperCAmelCase		):
									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:
									lowercase__					      =		[convert_to_rgb(_UpperCAmelCase		) for image in images]

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

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

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

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

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

							lowercase__					      =		[to_channel_dimension_format(_UpperCAmelCase				, _UpperCAmelCase		) for image in images]

							lowercase__					      =		{"""pixel_values""": images}
							return BatchFeature(data=_UpperCAmelCase				, tensor_type=_UpperCAmelCase		)





from __future__ import annotations

from collections import deque



class 							A   :
					'''simple docstring'''






					def __init__(self   :						Any				, _UpperCAmelCase   :						list[str]		)						->       Optional[int]:




							"""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   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						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   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						str		)						->       None:




							"""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   lowerCamelCase__					(self   :						Any		)						->       None:




							"""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   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						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(_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()

