
from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

UpperCamelCase_	:    Optional[Any]  					=       logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



							def 		UpperCamelCase    (					cls     :      Any     )      -> List[str]:






													'''simple docstring'''

													return cls



def 		UpperCamelCase    (					_UpperCAmelCase     :      Union[Tuple, List]   , _UpperCAmelCase     :      Optional[Union[List["torch.Generator"], "torch.Generator"]] = None   , _UpperCAmelCase     :      Optional["torch.device"] = None   , _UpperCAmelCase     :      Optional["torch.dtype"] = None   , _UpperCAmelCase     :      Optional["torch.layout"] = None   , )      -> Union[str, Any]:






						'''simple docstring'''

						_lowercase					:  Union[str, Any]    			=      device
						_lowercase					:  int    			=      shape[0]

						_lowercase					:  int    			=      layout or torch.strided
						_lowercase					:  List[str]    			=      device or torch.device("cpu"     )

						if generator is not None:
												_lowercase					:  str    			=      generator.device.type if not isinstance(_UpperCAmelCase   , _UpperCAmelCase     ) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		_lowercase					:  Union[str, Any]    			=      "cpu"
																		if device != "mps":
																								logger.info(
																								    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																								    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																								    f""" slighly speed up this function by passing a generator that was created on the {device} device."""     )
												elif gen_device_type != device.type and gen_device_type == "cuda":
																		raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""     )

						if isinstance(_UpperCAmelCase   , _UpperCAmelCase     ):
												_lowercase					:  Dict    			=      (1,) + shape[1:]
												_lowercase					:  int    			=      [
												    torch.randn(_UpperCAmelCase   , generator=generator[i]   , device=_UpperCAmelCase   , dtype=_UpperCAmelCase   , layout=_UpperCAmelCase     )
												    for i in range(_UpperCAmelCase     )
												]
												_lowercase					:  List[Any]    			=      torch.cat(_UpperCAmelCase   , dim=0     ).to(_UpperCAmelCase     )
						else:
												_lowercase					:  Optional[int]    			=      torch.randn(_UpperCAmelCase   , generator=_UpperCAmelCase   , device=_UpperCAmelCase   , dtype=_UpperCAmelCase   , layout=_UpperCAmelCase     ).to(_UpperCAmelCase     )

						return latents



def 		UpperCamelCase    (					_UpperCAmelCase     :      Tuple     )      -> List[str]:






						'''simple docstring'''

						if is_torch_version("<"   , "2.0.0"     ) or not hasattr(_UpperCAmelCase   , "_dynamo"     ):
												return False
						return isinstance(_UpperCAmelCase   , torch._dynamo.eval_frame.OptimizedModule     )






from dataclasses import dataclass

UpperCamelCase_	:    List[str]  					=       """0.1"""
UpperCamelCase_	:    Optional[Any]  					=       """Lucia Harcekova"""




@dataclass
class      __lowercase	:
	_A   			=      42
	_A   			=      42
	_A   			=      42






	def __repr__(self							:   List[str]						)   -> str:
							return F"""({self.offset}, {self.length}, {self.indicator})"""




class      __lowercase	:






	def __init__(self							:   Union[str, Any]			,  snake_case							:   int = 13			,  snake_case							:   int = 6						)   -> None:
							_lowercase					:  int    			=      window_size
							_lowercase					:  List[Any]    			=      lookahead_buffer_size
							_lowercase					:  str    			=      self.window_size - self.lookahead_buffer_size






	def 			_a(self							:   List[str]			,  snake_case							:   str						)   -> list[Token]:
							_lowercase					:  List[Any]    			=      []
							_lowercase					:  str    			=      ""

							# 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)
													_lowercase					:  Union[str, Any]    			=      self._find_encoding_token(snake_case			,  snake_case						)

													# 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(snake_case						) > self.search_buffer_size:
																			_lowercase					:  Optional[int]    			=      search_buffer[-self.search_buffer_size :]

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

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

							return output






	def 			_a(self							:   Optional[Any]			,  snake_case							:   list[Token]						)   -> str:
							_lowercase					:  Optional[int]    			=      ""

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

							return output






	def 			_a(self							:   Dict			,  snake_case							:   str			,  snake_case							:   str						)   -> Token:

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

							# Initialise result parameters to default values
							_lowercase			,						_lowercase					:  Tuple    			=      0, 0

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

							for i, character in enumerate(snake_case						):
													_lowercase					:  Tuple    			=      len(snake_case						) - i
													if character == text[0]:
																			_lowercase					:  List[str]    			=      self._match_length_from_index(snake_case			,  snake_case			,  0			,  snake_case						)
																			# 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:
																									_lowercase			,						_lowercase					:  Optional[Any]    			=      found_offset, found_length

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






	def 			_a(self							:   Union[str, Any]			,  snake_case							:   str			,  snake_case							:   str			,  snake_case							:   int			,  snake_case							:   int						)   -> int:
							if not text or text[text_index] != window[window_index]:
													return 0
							return 1 + self._match_length_from_index(
							    snake_case			,  window + text[text_index]			,  text_index + 1			,  window_index + 1						)


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

							testmod()
							# Initialize compressor class
							UpperCamelCase_	:    Optional[int]  					=       LZaaCompressor(window_size=13, lookahead_buffer_size=6)

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




