
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_	:    Any  					=       logging.get_logger(__name__)

UpperCamelCase_	:    List[str]  					=       {
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}




class      __lowercase	(	__snake_case					):
	_A   			=      "opt"
	_A   			=      ["past_key_values"]






	def __init__(self							:   Optional[Any]			,  snake_case							:   Tuple=5_0272			,  snake_case							:   Optional[int]=768			,  snake_case							:   str=12			,  snake_case							:   List[Any]=3072			,  snake_case							:   Dict=2048			,  snake_case							:   Union[str, Any]=True			,  snake_case							:   Tuple=False			,  snake_case							:   List[str]=None			,  snake_case							:   Tuple=0.1			,  snake_case							:   Union[str, Any]=0.0			,  snake_case							:   Any=12			,  snake_case							:   str="relu"			,  snake_case							:   Optional[int]=0.0			,  snake_case							:   Any=0.02			,  snake_case							:   Tuple=True			,  snake_case							:   Tuple=1			,  snake_case							:   Dict=2			,  snake_case							:   Dict=2			,  snake_case							:   List[Any]=True			,  snake_case							:   List[Any]=True			,  **snake_case							:   Tuple			,  )   -> Any:
							super().__init__(
							    pad_token_id=snake_case			,  bos_token_id=snake_case			,  eos_token_id=snake_case			,  **snake_case			,  )
							_lowercase					:  Optional[Any]    			=      vocab_size
							_lowercase					:  str    			=      max_position_embeddings
							_lowercase					:  List[Any]    			=      num_attention_heads
							_lowercase					:  int    			=      word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
							_lowercase					:  str    			=      ffn_dim
							_lowercase					:  str    			=      hidden_size
							_lowercase					:  List[str]    			=      num_hidden_layers
							_lowercase					:  List[str]    			=      dropout
							_lowercase					:  str    			=      attention_dropout
							_lowercase					:  List[Any]    			=      activation_function
							_lowercase					:  Optional[int]    			=      init_std
							_lowercase					:  Optional[int]    			=      layerdrop
							_lowercase					:  List[Any]    			=      use_cache
							_lowercase					:  List[Any]    			=      do_layer_norm_before
							# We keep these variables at `True` for backward compatibility.
							_lowercase					:  Optional[Any]    			=      enable_bias
							_lowercase					:  Any    			=      layer_norm_elementwise_affine

							# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
							# with checkpoints that have been fine-tuned before transformers v4.20.1
							# see https://github.com/facebookresearch/metaseq/pull/164
							_lowercase					:  Optional[int]    			=      _remove_final_layer_norm






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."




