
'''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 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_vision_available, logging


if is_vision_available():
							import PIL


_lowercase :  str    							=       logging.get_logger(__name__)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Tuple      ,      __SCREAMING_SNAKE_CASE					: List[Any]							):



						"""simple docstring"""





						lowercase_	:     int													=       b.T
						lowercase_	:     List[str]													=       np.sum(np.square(__SCREAMING_SNAKE_CASE							)      ,      axis=1							)
						lowercase_	:     str													=       np.sum(np.square(__SCREAMING_SNAKE_CASE							)      ,      axis=0							)
						lowercase_	:     Union[str, Any]													=       np.matmul(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
						lowercase_	:     Optional[int]													=       aa[:, None] - 2 * ab + ba[None, :]
						return d
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: Dict							):



						"""simple docstring"""





						lowercase_	:     Any													=       x.reshape(-1      ,      3							)
						lowercase_	:     List[Any]													=       squared_euclidean_distance(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
						return np.argmin(__SCREAMING_SNAKE_CASE      ,      axis=1							)






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     ['''pixel_values''']






					def __init__(   self				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = True				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       size if size is not None else {'''height''': 2_56, '''width''': 2_56}
											lowercase_	:     Any													=       get_size_dict(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       np.array(__SCREAMING_SNAKE_CASE		) if clusters is not None else None
											lowercase_	:     Union[str, Any]													=       do_resize
											lowercase_	:     int													=       size
											lowercase_	:     Optional[int]													=       resample
											lowercase_	:     List[str]													=       do_normalize
											lowercase_	:     List[Any]													=       do_color_quantize






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       get_size_dict(__SCREAMING_SNAKE_CASE		)
											if "height" not in size or "width" not in size:
																	raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}'''		)
											return resize(
											    __SCREAMING_SNAKE_CASE				,							size=(size['''height'''], size['''width'''])				,							resample=__SCREAMING_SNAKE_CASE				,							data_format=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       rescale(image=__SCREAMING_SNAKE_CASE				,							scale=1 / 127.5				,							data_format=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       image - 1
											return image






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       do_resize if do_resize is not None else self.do_resize
											lowercase_	:     Union[str, Any]													=       size if size is not None else self.size
											lowercase_	:     Union[str, Any]													=       get_size_dict(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       resample if resample is not None else self.resample
											lowercase_	:     Optional[int]													=       do_normalize if do_normalize is not None else self.do_normalize
											lowercase_	:     Optional[Any]													=       do_color_quantize if do_color_quantize is not None else self.do_color_quantize
											lowercase_	:     Tuple													=       clusters if clusters is not None else self.clusters
											lowercase_	:     Dict													=       np.array(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Tuple													=       make_list_of_images(__SCREAMING_SNAKE_CASE		)

											if not valid_images(__SCREAMING_SNAKE_CASE		):
																	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 or resample is None:
																	raise ValueError('''Size and resample must be specified if do_resize is True.'''		)

											if do_color_quantize and clusters is None:
																	raise ValueError('''Clusters must be specified if do_color_quantize is True.'''		)

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

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

											if do_normalize:
																	lowercase_	:     Optional[Any]													=       [self.normalize(image=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_color_quantize:
																	lowercase_	:     str													=       [to_channel_dimension_format(__SCREAMING_SNAKE_CASE				,							ChannelDimension.LAST		) for image in images]
																	# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																	lowercase_	:     Dict													=       np.array(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     int													=       color_quantize(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		).reshape(images.shape[:-1]		)

																	# flatten to (batch_size, height*width)
																	lowercase_	:     Optional[Any]													=       images.shape[0]
																	lowercase_	:     Optional[Any]													=       images.reshape(__SCREAMING_SNAKE_CASE				,							-1		)

																	# We need to convert back to a list of images to keep consistent behaviour across processors.
																	lowercase_	:     str													=       list(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_	:     Optional[Any]													=       [to_channel_dimension_format(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) for image in images]

											lowercase_	:     str													=       {'''input_ids''': images}
											return BatchFeature(data=__SCREAMING_SNAKE_CASE				,							tensor_type=__SCREAMING_SNAKE_CASE		)





'''simple docstring'''







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						lowercase_	:     List[str]													=       len(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Optional[int]													=       []
						for i in range(len(__SCREAMING_SNAKE_CASE							) - pat_len + 1							):
												lowercase_	:     Tuple													=       True
												for j in range(__SCREAMING_SNAKE_CASE							):
																		if s[i + j] != pattern[j]:
																								lowercase_	:     List[str]													=       False
																								break
												if match_found:
																		position.append(__SCREAMING_SNAKE_CASE							)
						return position


if __name__ == "__main__":
							assert naive_pattern_search("ABCDEFG", "DE") == [3]
							print(naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC"))



