


"""simple docstring"""


from dataclasses import dataclass

a_											=			"0.1"
a_											=			"Lucia Harcekova"
@dataclass
class snake_case					:
		__UpperCamelCase							=	42
		__UpperCamelCase							=	42
		__UpperCamelCase							=	42







		def __repr__(    self					:				Union[str, Any]						)				->  str:





						'''simple docstring'''







						return F"""({self.offset}, {self.length}, {self.indicator})"""





class snake_case					:







		def __init__(    self					:				str			,      a__					:				int = 13			,      a__					:				int = 6						)				->  None:





						'''simple docstring'''







						_A         =	window_size
						_A         =	lookahead_buffer_size
						_A         =	self.window_size - self.lookahead_buffer_size







		def        a_       (    self					:				Optional[Any]			,      a__					:				str						)				->  list[Token]:





						'''simple docstring'''







						_A         =	[]
						_A         =	""

						# while there are still characters in text to compress
						while text:
										# find the next encoding phrase
										# - triplet with offset, length, indicator (the next encoding character)
										_A         =	self._find_encoding_token(a__			,      a__						)

										# update the search buffer:
										# - add new characters from text into it
										# - check if size exceed the max search buffer size, if so, drop the
										#   oldest elements
										search_buffer += text[: token.length + 1]
										if len(a__						) > self.search_buffer_size:
														_A         =	search_buffer[-self.search_buffer_size :]

										# update the text
										_A         =	text[token.length + 1 :]

										# append the token to output
										output.append(a__						)

						return output







		def        a_       (    self					:				int			,      a__					:				list[Token]						)				->  str:





						'''simple docstring'''







						_A         =	""

						for token in tokens:
										for _ in range(token.length						):
														output += output[-token.offset]
										output += token.indicator

						return output







		def        a_       (    self					:				Dict			,      a__					:				str			,      a__					:				str						)				->  Token:





						'''simple docstring'''







						if not text:
										raise ValueError("We need some text to work with."						)

						# Initialise result parameters to default values
						_A     ,						_A         =	0, 0

						if not search_buffer:
										return Token(a__			,      a__			,      text[length]						)

						for i, character in enumerate(a__						):
										_A         =	len(a__						) - i
										if character == text[0]:
														_A         =	self._match_length_from_index(a__			,      a__			,      0			,      a__						)
														# if the found length is bigger than the current or if it's equal,
														# which means it's offset is smaller: update offset and length
														if found_length >= length:
																		_A     ,						_A         =	found_offset, found_length

						return Token(a__			,      a__			,      text[length]						)







		def        a_       (    self					:				Optional[Any]			,      a__					:				str			,      a__					:				str			,      a__					:				int			,      a__					:				int						)				->  int:





						'''simple docstring'''







						if not text or text[text_index] != window[window_index]:
										return 0
						return 1 + self._match_length_from_index(
						    a__			,      window + text[text_index]			,      text_index + 1			,      window_index + 1						)


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

		testmod()
		# Initialize compressor class
		a_											=			LZaaCompressor(window_size=13, lookahead_buffer_size=6)

		# Example
		a_											=			"cabracadabrarrarrad"
		a_											=			lzaa_compressor.compress(TEXT)
		print(lzaa_compressor.compress("ababcbababaa"))
		a_											=			lzaa_compressor.decompress(compressed_text)
		assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
 import torch




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Dict 			=       ['image_processor', 'tokenizer']
 _lowerCamelCase  :		Tuple 			=       'OneFormerImageProcessor'
 _lowerCamelCase  :		Dict 			=       ('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__(	self				:						str				,	lowerCamelCase				:						Any=None				,	lowerCamelCase				:						List[Any]=None				,	lowerCamelCase				:						int = 7_7				,	lowerCamelCase				:						int = 7_7				,	**lowerCamelCase				:						Union[str, Any]     )       ->							Any:
        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`.'     )

        _UpperCamelCase   :				int       =     max_seq_length
        _UpperCamelCase   :				Optional[Any]       =     task_seq_length

        super().__init__(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						Optional[int]=7_7     )       ->							Tuple:
        if text_list is None:
               raise ValueError('tokens cannot be None.'     )

        _UpperCamelCase   :				Dict       =     self.tokenizer(lowerCamelCase				,	padding='max_length'				,	max_length=lowerCamelCase				,	truncation=lowerCamelCase     )

        _UpperCamelCase  ,					_UpperCamelCase   :				List[str]       =     tokens['attention_mask'], tokens['input_ids']

        _UpperCamelCase   :				Optional[int]       =     []
        for attn_mask, input_id in zip(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     torch.tensor(lowerCamelCase     ) * torch.tensor(lowerCamelCase     )
               token_inputs.append(token.unsqueeze(0     )     )

        _UpperCamelCase   :				Dict       =     torch.cat(lowerCamelCase				,	dim=0     )
        return token_inputs

 def __call__(	self				:						Union[str, Any]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						Any=None				,	**lowerCamelCase				:						List[Any]     )       ->							List[Any]:

        if task_inputs is None:
               raise ValueError('You have to specify the task_input. Found None.'     )
        elif images is None:
               raise ValueError('You have to specify the image. Found None.'     )

        if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs     ):
               raise ValueError('task_inputs must be semantic, instance, or panoptic.'     )

        _UpperCamelCase   :				Optional[Any]       =     self.image_processor(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				str       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				Optional[Any]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				Dict       =     self._preprocess_text(lowerCamelCase				,	max_length=self.task_seq_length     )
        else:
               raise TypeError('Task Inputs should be a string or a list of strings.'     )

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				str       =     encoded_inputs.text_inputs

               _UpperCamelCase   :				Optional[Any]       =     []
               for texts in texts_list:
                      _UpperCamelCase   :				Optional[int]       =     self._preprocess_text(lowerCamelCase				,	max_length=self.max_seq_length     )
                      text_inputs.append(text_input_list.unsqueeze(0     )     )

               _UpperCamelCase   :				Optional[int]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						int=None				,	**lowerCamelCase				:						List[str]     )       ->							Tuple:

        if task_inputs is None:
               raise ValueError('You have to specify the task_input. Found None.'     )
        elif images is None:
               raise ValueError('You have to specify the image. Found None.'     )

        if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs     ):
               raise ValueError('task_inputs must be semantic, instance, or panoptic.'     )

        _UpperCamelCase   :				Any       =     self.image_processor.encode_inputs(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				int       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				List[str]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				int       =     self._preprocess_text(lowerCamelCase				,	max_length=self.task_seq_length     )
        else:
               raise TypeError('Task Inputs should be a string or a list of strings.'     )

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				Optional[int]       =     encoded_inputs.text_inputs

               _UpperCamelCase   :				Any       =     []
               for texts in texts_list:
                      _UpperCamelCase   :				Any       =     self._preprocess_text(lowerCamelCase				,	max_length=self.max_seq_length     )
                      text_inputs.append(text_input_list.unsqueeze(0     )     )

               _UpperCamelCase   :				Optional[Any]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						Tuple				,	**lowerCamelCase				:						Any     )       ->							str:
        return self.image_processor.post_process_semantic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Union[str, Any]				,	*lowerCamelCase				:						Optional[int]				,	**lowerCamelCase				:						Any     )       ->							Any:
        return self.image_processor.post_process_instance_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						List[str]				,	**lowerCamelCase				:						int     )       ->							List[Any]:
        return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )



