




'''simple docstring'''





from dataclasses import dataclass

_SCREAMING_SNAKE_CASE							:     Any   	=				"0.1"
_SCREAMING_SNAKE_CASE							:     str   	=				"Lucia Harcekova"




@dataclass
class 				_snake_case	:
			lowerCAmelCase_  :   int
			lowerCAmelCase_  :   int
			lowerCAmelCase_  :   str




			def __repr__(      self							)       ->       str:

										'''simple docstring'''




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




class 				_snake_case	:




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

										'''simple docstring'''




										snake_case_			      =   window_size
										snake_case_			      =   lookahead_buffer_size
										snake_case_			      =   self.window_size - self.lookahead_buffer_size




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       list[Token]:

										'''simple docstring'''




										snake_case_			      =   []
										snake_case_			      =   ""

										# 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)
																	snake_case_			      =   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:
																								snake_case_			      =   search_buffer[-self.search_buffer_size :]

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

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

										return output




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       str:

										'''simple docstring'''




										snake_case_			      =   ""

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

										return output




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       Token:

										'''simple docstring'''




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

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

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

										for i, character in enumerate(a__							):
																	snake_case_			      =   len(a__							) - i
																	if character == text[0]:
																								snake_case_			      =   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:
																															snake_case_      ,   snake_case_			      =   found_offset, found_length

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




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__       ,							a__							)       ->       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
		_SCREAMING_SNAKE_CASE							:     int   	=				LZaaCompressor(window_size=13, lookahead_buffer_size=6)

		# Example
		_SCREAMING_SNAKE_CASE							:     Tuple   	=				"cabracadabrarrarrad"
		_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				lzaa_compressor.compress(TEXT)
		print(lzaa_compressor.compress("ababcbababaa"))
		_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				lzaa_compressor.decompress(compressed_text)
		assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE							:     Dict   	=				{
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:     Optional[Any]   	=				[
		    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MobileViTV2ForImageClassification",
		    "MobileViTV2ForSemanticSegmentation",
		    "MobileViTV2Model",
		    "MobileViTV2PreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileViTVaConfig,
		    MobileViTVaOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTVaForImageClassification,
				    MobileViTVaForSemanticSegmentation,
				    MobileViTVaModel,
				    MobileViTVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE							:     Any   	=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




