






"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):
							snake_case_       =							['image_processor', 'tokenizer']
							snake_case_       =							'AutoImageProcessor'
							snake_case_       =							'AutoTokenizer'


							def __init__(     self :  List[Any]     ,      snake_case :  str=None     ,      snake_case :  Dict=None     ,      **snake_case :  List[Any]							):
													'''simple docstring'''





													A__  :					Dict											=					None
													if "feature_extractor" in kwargs:
																			warnings.warn(
																			    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																			    """ instead."""     ,      snake_case     ,      )
																			A__  :					List[Any]											=					kwargs.pop("""feature_extractor"""							)

													A__  :					List[str]											=					image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																			raise ValueError("""You need to specify an `image_processor`."""							)
													if tokenizer is None:
																			raise ValueError("""You need to specify a `tokenizer`."""							)

													super().__init__(snake_case     ,      snake_case							)
													A__  :					Optional[Any]											=					self.image_processor
													A__  :					int											=					False


							def __call__(     self :  Optional[Any]     ,      *snake_case :  str     ,      **snake_case :  Dict							):
													'''simple docstring'''





													if self._in_target_context_manager:
																			return self.current_processor(*snake_case     ,      **snake_case							)

													A__  :					List[str]											=					kwargs.pop("""images"""     ,      snake_case							)
													A__  :					Any											=					kwargs.pop("""text"""     ,      snake_case							)
													if len(snake_case							) > 0:
																			A__  :					Tuple											=					args[0]
																			A__  :					Dict											=					args[1:]

													if images is None and text is None:
																			raise ValueError("""You need to specify either an `images` or `text` input to process."""							)

													if images is not None:
																			A__  :					Dict											=					self.image_processor(snake_case     ,      *snake_case     ,      **snake_case							)
													if text is not None:
																			A__  :					Optional[int]											=					self.tokenizer(snake_case     ,      **snake_case							)

													if text is None:
																			return inputs
													elif images is None:
																			return encodings
													else:
																			A__  :					Optional[Any]											=					encodings["""input_ids"""]
																			return inputs


							def   _UpperCamelCase    (     self :  List[str]     ,      *snake_case :  List[Any]     ,      **snake_case :  Dict							):
													'''simple docstring'''





													return self.tokenizer.batch_decode(*snake_case     ,      **snake_case							)


							def   _UpperCamelCase    (     self :  Optional[Any]     ,      *snake_case :  Optional[int]     ,      **snake_case :  List[Any]							):
													'''simple docstring'''





													return self.tokenizer.decode(*snake_case     ,      **snake_case							)


							@contextmanager
							def   _UpperCamelCase    (     self :  Tuple							):
													'''simple docstring'''





													warnings.warn(
													    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
													    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
													    """your images inputs, or in a separate call."""							)
													A__  :					str											=					True
													A__  :					List[str]											=					self.tokenizer
													yield
													A__  :					List[Any]											=					self.image_processor
													A__  :					List[Any]											=					False


							def   _UpperCamelCase    (     self :  Tuple     ,      snake_case :  Optional[int]     ,      snake_case :  Dict=False     ,      snake_case :  Optional[Any]=None							):
													'''simple docstring'''





													if added_vocab is None:
																			A__  :					str											=					self.tokenizer.get_added_vocab()

													A__  :					Optional[int]											=					{}

													while tokens:
																			A__  :					List[Any]											=					re.search(r"""<s_(.*?)>"""     ,      snake_case     ,      re.IGNORECASE							)
																			if start_token is None:
																									break
																			A__  :					Tuple											=					start_token.group(1							)
																			A__  :					Dict											=					re.search(rF'</s_{key}>'     ,      snake_case     ,      re.IGNORECASE							)
																			A__  :					str											=					start_token.group()
																			if end_token is None:
																									A__  :					Union[str, Any]											=					tokens.replace(snake_case     ,      """"""							)
																			else:
																									A__  :					Dict											=					end_token.group()
																									A__  :					List[str]											=					re.escape(snake_case							)
																									A__  :					str											=					re.escape(snake_case							)
																									A__  :					Dict											=					re.search(F'{start_token_escaped}(.*?){end_token_escaped}'     ,      snake_case     ,      re.IGNORECASE							)
																									if content is not None:
																															A__  :					str											=					content.group(1							).strip()
																															if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																					A__  :					Optional[int]											=					self.tokenajson(snake_case     ,      is_inner_value=snake_case     ,      added_vocab=snake_case							)
																																					if value:
																																											if len(snake_case							) == 1:
																																																	A__  :					Optional[Any]											=					value[0]
																																											A__  :					str											=					value
																															else:  # leaf nodes
																																					A__  :					Optional[Any]											=					[]
																																					for leaf in content.split(r"""<sep/>"""							):
																																											A__  :					Optional[int]											=					leaf.strip()
																																											if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																	A__  :					Any											=					leaf[1:-2]  # for categorical special tokens
																																											output[key].append(snake_case							)
																																					if len(output[key]							) == 1:
																																											A__  :					Any											=					output[key][0]

																									A__  :					List[str]											=					tokens[tokens.find(snake_case							) + len(snake_case							) :].strip()
																									if tokens[:6] == r"<sep/>":  # non-leaf nodes
																															return [output] + self.tokenajson(tokens[6:]     ,      is_inner_value=snake_case     ,      added_vocab=snake_case							)

													if len(snake_case							):
																			return [output] if is_inner_value else output
													else:
																			return [] if is_inner_value else {"text_sequence": tokens}


							@property
							def   _UpperCamelCase    (     self :  int							):
													'''simple docstring'''





													warnings.warn(
													    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,      snake_case     ,      )
													return self.image_processor_class


							@property
							def   _UpperCamelCase    (     self :  List[Any]							):
													'''simple docstring'''





													warnings.warn(
													    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,      snake_case     ,      )
													return self.image_processor














"""simple docstring"""



from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt




def        _lowerCAmelCase						(							UpperCAmelCase__    :   Sequence[float],  UpperCAmelCase__    :   int,  UpperCAmelCase__    :   int )						->tuple[int | None, int | None, float]:
						if not arr:
												return None, None, 0
						if low == high:
												return low, high, arr[low]

						A__  :					Optional[int]											=					(low + high) // 2
						A__					,					A__					,					A__  :					List[Any]											=					max_subarray(UpperCAmelCase__,  UpperCAmelCase__,  UpperCAmelCase__ )
						A__					,					A__					,					A__  :					Union[str, Any]											=					max_subarray(UpperCAmelCase__,  mid + 1,  UpperCAmelCase__ )
						A__					,					A__					,					A__  :					Union[str, Any]											=					max_cross_sum(UpperCAmelCase__,  UpperCAmelCase__,  UpperCAmelCase__,  UpperCAmelCase__ )
						if left_sum >= right_sum and left_sum >= cross_sum:
												return left_low, left_high, left_sum
						elif right_sum >= left_sum and right_sum >= cross_sum:
												return right_low, right_high, right_sum
						return cross_left, cross_right, cross_sum




def        _lowerCAmelCase						(							UpperCAmelCase__    :   Sequence[float],  UpperCAmelCase__    :   int,  UpperCAmelCase__    :   int,  UpperCAmelCase__    :   int )						->tuple[int, int, float]:
						A__					,					A__  :					Dict											=					float("""-inf""" ), -1
						A__					,					A__  :					Optional[Any]											=					float("""-inf""" ), -1

						A__  :					int | float											=					0
						for i in range(UpperCAmelCase__,  low - 1,  -1 ):
												summ += arr[i]
												if summ > left_sum:
																		A__  :					Optional[int]											=					summ
																		A__  :					Union[str, Any]											=					i

						A__  :					Optional[Any]											=					0
						for i in range(mid + 1,  high + 1 ):
												summ += arr[i]
												if summ > right_sum:
																		A__  :					int											=					summ
																		A__  :					Union[str, Any]											=					i

						return max_left, max_right, (left_sum + right_sum)




def        _lowerCAmelCase						(							UpperCAmelCase__    :   int )						->float:
						A__  :					Union[str, Any]											=					[randint(1,  UpperCAmelCase__ ) for _ in range(UpperCAmelCase__ )]
						A__  :					Any											=					time.time()
						max_subarray(UpperCAmelCase__,  0,  input_size - 1 )
						A__  :					List[Any]											=					time.time()
						return end - start




def        _lowerCAmelCase						(							)						->None:
						A__  :					List[Any]											=					[1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
						A__  :					Any											=					[time_max_subarray(UpperCAmelCase__ ) for input_size in input_sizes]
						print("""No of Inputs\t\tTime Taken""" )
						for input_size, runtime in zip(UpperCAmelCase__,  UpperCAmelCase__ ):
												print(UpperCAmelCase__,  """\t\t""",  UpperCAmelCase__ )
						plt.plot(UpperCAmelCase__,  UpperCAmelCase__ )
						plt.xlabel("""Number of Inputs""" )
						plt.ylabel("""Time taken in seconds""" )
						plt.show()


if __name__ == "__main__":
						from doctest import testmod

						testmod()






