




from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

a													=			logging.get_logger(__name__)




@add_end_docstrings(__SCREAMING_SNAKE_CASE     )
class       _A				(		__SCREAMING_SNAKE_CASE     ):






		def __init__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								super().__init__(*_a						,		**_a			)
								requires_backends(self						,		"""vision"""			)
								self.check_model_type(_a			)






		def __call__(    self						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								return super().__call__(_a						,		**_a			)






		def 				UpperCAmelCase (    self						,		**_SCREAMING_SNAKE_CASE			):
								return {}, {}, {}






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			load_image(_a			)
								_UpperCAmelCase      =			image.size
								_UpperCAmelCase      =			self.image_processor(images=_a						,		return_tensors=self.framework			)
								return model_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.model(**_a			)
								return model_outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			model_outputs.predicted_depth
								_UpperCAmelCase      =			torch.nn.functional.interpolate(
								    predicted_depth.unsqueeze(1			)						,		size=self.image_size[::-1]						,		mode="""bicubic"""						,		align_corners=_a			)
								_UpperCAmelCase      =			prediction.squeeze().cpu().numpy()
								_UpperCAmelCase      =			(output * 255 / np.max(_a			)).astype("""uint8"""			)
								_UpperCAmelCase      =			Image.fromarray(_a			)
								_UpperCAmelCase      =			{}
								_UpperCAmelCase      =			predicted_depth
								_UpperCAmelCase      =			depth
								return output_dict





def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				bool:
						_UpperCAmelCase      =			len(snake_case       ) + 1
						_UpperCAmelCase      =			len(snake_case       ) + 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.
						_UpperCAmelCase      =			[[0 for i in range(snake_case       )] for j in range(snake_case       )]

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

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

						# since string of zero length will match with pattern where there
						# is at least one * alternatively
						for j in range(1			,     snake_case       ):
												_UpperCAmelCase      =			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			,     snake_case       ):
												for j in range(1			,     snake_case       ):
																		if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																								_UpperCAmelCase      =			dp[i - 1][j - 1]

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

				a													=			"aab"
				a													=			"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}')