





'''simple docstring'''



from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
	import PIL

# soft dependency
if is_pytesseract_available():
	import pytesseract

_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					Any						,    lowerCamelCase_     :					Optional[int]			):
			return [
			    int(1_0_0_0 * (box[0] / width)			),
			    int(1_0_0_0 * (box[1] / height)			),
			    int(1_0_0_0 * (box[2] / width)			),
			    int(1_0_0_0 * (box[3] / height)			),
			]




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					int						,    lowerCamelCase_     :					int			):
			__lowercase			  =							to_pil_image(lowerCamelCase_			)
			__lowercase  ,						__lowercase			  =							pil_image.size
			__lowercase			  =							pytesseract.image_to_data(lowerCamelCase_						,    lang=lowerCamelCase_						,    output_type='''dict'''						,    config=lowerCamelCase_			)
			__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase			  =							data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

			# filter empty words and corresponding coordinates
			__lowercase			  =							[idx for idx, word in enumerate(lowerCamelCase_			) if not word.strip()]
			__lowercase			  =							[word for idx, word in enumerate(lowerCamelCase_			) if idx not in irrelevant_indices]
			__lowercase			  =							[coord for idx, coord in enumerate(lowerCamelCase_			) if idx not in irrelevant_indices]
			__lowercase			  =							[coord for idx, coord in enumerate(lowerCamelCase_			) if idx not in irrelevant_indices]
			__lowercase			  =							[coord for idx, coord in enumerate(lowerCamelCase_			) if idx not in irrelevant_indices]
			__lowercase			  =							[coord for idx, coord in enumerate(lowerCamelCase_			) if idx not in irrelevant_indices]

			# turn coordinates into (left, top, left+width, top+height) format
			__lowercase			  =							[]
			for x, y, w, h in zip(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			):
						__lowercase			  =							[x, y, x + w, y + h]
						actual_boxes.append(lowerCamelCase_			)

			# finally, normalize the bounding boxes
			__lowercase			  =							[]
			for box in actual_boxes:
						normalized_boxes.append(normalize_box(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)			)

			assert len(lowerCamelCase_			) == len(lowerCamelCase_			), "Not as many words as there are bounding boxes"

			return words, normalized_boxes






class 							__lowercase    ( __a  ):







	'''simple docstring'''

	a      :			int     =    ["""pixel_values"""]


	def __init__(self       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = PILImageResampling.BILINEAR       ,_lowerCamelCase = True       ,_lowerCamelCase = 1 / 255       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = ""       ,**_lowerCamelCase       ,)							->							List[str]:







				'''simple docstring'''





				super().__init__(**a__	)
				__lowercase			  =							size if size is not None else {'''height''': 224, '''width''': 224}
				__lowercase			  =							get_size_dict(a__	)

				__lowercase			  =							do_resize
				__lowercase			  =							size
				__lowercase			  =							resample
				__lowercase			  =							do_rescale
				__lowercase			  =							rescale_value
				__lowercase			  =							do_normalize
				__lowercase			  =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				__lowercase			  =							image_std if image_std is not None else IMAGENET_STANDARD_STD
				__lowercase			  =							apply_ocr
				__lowercase			  =							ocr_lang
				__lowercase			  =							tesseract_config


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = PILImageResampling.BILINEAR       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							Optional[Any]:







				'''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()}"	)
				__lowercase			  =							(size['''height'''], size['''width'''])
				return resize(a__       ,size=a__       ,resample=a__       ,data_format=a__       ,**a__	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							Optional[int]:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							List[Any]:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase=None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = ChannelDimension.FIRST       ,**_lowerCamelCase       ,)							->							int:







				'''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(a__	)
				__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_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			  =							apply_ocr if apply_ocr is not None else self.apply_ocr
				__lowercase			  =							ocr_lang if ocr_lang is not None else self.ocr_lang
				__lowercase			  =							tesseract_config if tesseract_config is not None else self.tesseract_config

				__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_normalize and (image_mean is None or image_std is None):
							raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''	)

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

				# Tesseract OCR to get words + normalized bounding boxes
				if apply_ocr:
							requires_backends(self       ,'''pytesseract'''	)
							__lowercase			  =							[]
							__lowercase			  =							[]
							for image in images:
										__lowercase  ,						__lowercase			  =							apply_tesseract(a__       ,a__       ,a__	)
										words_batch.append(a__	)
										boxes_batch.append(a__	)

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

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

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

				__lowercase			  =							[to_channel_dimension_format(a__       ,a__	) for image in images]

				__lowercase			  =							BatchFeature(data={'''pixel_values''': images}       ,tensor_type=a__	)

				if apply_ocr:
							__lowercase			  =							words_batch
							__lowercase			  =							boxes_batch
				return data










'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			__lowercase			  =							len(lowerCamelCase_			) + 1
			__lowercase			  =							len(lowerCamelCase_			) + 1

			# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
			# length i of input_string matches with prefix string of length j of
			# given pattern.
			# "dp" stands for dynamic programming.
			__lowercase			  =							[[0 for i in range(lowerCamelCase_			)] for j in range(lowerCamelCase_			)]

			# since string of zero length match pattern of zero length
			__lowercase			  =							1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1						,    lowerCamelCase_			):
						__lowercase			  =							0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1						,    lowerCamelCase_			):
						__lowercase			  =							dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

			# now using bottom-up approach to find for all remaining lengths
			for i in range(1						,    lowerCamelCase_			):
						for j in range(1						,    lowerCamelCase_			):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												__lowercase			  =							dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															__lowercase			  =							1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															__lowercase			  =							dp[i - 1][j]
												else:
															__lowercase			  =							0
									else:
												__lowercase			  =							0

			return bool(dp[-1][-1]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# inputing the strings
	# input_string = input("input a string :")
	# pattern = input("input a pattern :")

	_SCREAMING_SNAKE_CASE   =      '''aab'''
	_SCREAMING_SNAKE_CASE   =      '''c*a*b'''

	# using function to check whether given string matches the given pattern
	if match_pattern(input_string, pattern):
		print(f'''{input_string} matches the given pattern {pattern}''')
	else:
		print(f'''{input_string} does not match with the given pattern {pattern}''')





