


'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__lowercase:		List[str]							=     logging.get_logger(__name__)

__lowercase:		Any							=     {
    """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}







class  UpperCAmelCase (       lowerCAmelCase_):
	_lowerCamelCase :					Tuple    					=					"""plbart"""
	_lowerCamelCase :					Tuple    					=					["""past_key_values"""]
	_lowerCamelCase :					Optional[int]    					=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

	def __init__( self :	Tuple,					a_ :	Any=5_0005,					a_ :	List[Any]=1024,					a_ :	List[str]=6,					a_ :	int=3072,					a_ :	str=12,					a_ :	Tuple=6,					a_ :	Tuple=3072,					a_ :	int=12,					a_ :	int=0.0,					a_ :	List[Any]=0.0,					a_ :	int=True,					a_ :	str=True,					a_ :	str="gelu",					a_ :	List[Any]=768,					a_ :	Optional[Any]=0.1,					a_ :	Dict=0.1,					a_ :	Any=0.0,					a_ :	Tuple=0.02,					a_ :	Tuple=0.0,					a_ :	Any=True,					a_ :	Union[str, Any]=1,					a_ :	int=0,					a_ :	int=2,					a_ :	Tuple=2,					**a_ :	Tuple,					):





				"""simple docstring"""



				UpperCamelCase__ 						=						vocab_size
				UpperCamelCase__ 						=						max_position_embeddings
				UpperCamelCase__ 						=						d_model
				UpperCamelCase__ 						=						encoder_ffn_dim
				UpperCamelCase__ 						=						encoder_layers
				UpperCamelCase__ 						=						encoder_attention_heads
				UpperCamelCase__ 						=						decoder_ffn_dim
				UpperCamelCase__ 						=						decoder_layers
				UpperCamelCase__ 						=						decoder_attention_heads
				UpperCamelCase__ 						=						dropout
				UpperCamelCase__ 						=						attention_dropout
				UpperCamelCase__ 						=						activation_dropout
				UpperCamelCase__ 						=						activation_function
				UpperCamelCase__ 						=						init_std
				UpperCamelCase__ 						=						encoder_layerdrop
				UpperCamelCase__ 						=						decoder_layerdrop
				UpperCamelCase__ 						=						classifier_dropout
				UpperCamelCase__ 						=						use_cache
				UpperCamelCase__ 						=						encoder_layers
				UpperCamelCase__ 						=						scale_embedding  # scale factor will be sqrt(d_model) if True
				super().__init__(
				    pad_token_id=__SCREAMING_SNAKE_CASE,					bos_token_id=__SCREAMING_SNAKE_CASE,					eos_token_id=__SCREAMING_SNAKE_CASE,					is_encoder_decoder=__SCREAMING_SNAKE_CASE,					forced_eos_token_id=__SCREAMING_SNAKE_CASE,					**__SCREAMING_SNAKE_CASE,					)







class  UpperCAmelCase (       lowerCAmelCase_):

	@property
	def 		lowercase_						( self :	Any					):





				"""simple docstring"""



				return OrderedDict(
				    [
				        ("input_ids", {0: "batch", 1: "sequence"}),
				        ("attention_mask", {0: "batch", 1: "sequence"}),
				    ]					)

	@property
	def 		lowercase_						( self :	Any					):





				"""simple docstring"""



				if self.use_past:
							return OrderedDict(
							    [
							        ("last_hidden_state", {0: "batch", 1: "sequence"}),
							        ("past_keys", {0: "batch", 2: "sequence"}),
							        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
							    ]					)
				else:
							return OrderedDict(
							    [
							        ("last_hidden_state", {0: "batch", 1: "sequence"}),
							        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
							    ]					)



'''simple docstring'''

from dataclasses import dataclass

__lowercase:		List[str]							=     "0.1"
__lowercase:		Union[str, Any]							=     "Lucia Harcekova"







@dataclass
class  UpperCAmelCase :
 _lowerCamelCase :					int
 _lowerCamelCase :					int
 _lowerCamelCase :					str

 def __repr__( self :	List[str]					):





    """simple docstring"""



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







class  UpperCAmelCase :

 def __init__( self :	Optional[int],					a_ :	int = 13,					a_ :	int = 6					):





    """simple docstring"""



    UpperCamelCase__ 						=						window_size
    UpperCamelCase__ 						=						lookahead_buffer_size
    UpperCamelCase__ 						=						self.window_size - self.lookahead_buffer_size

 def 		lowercase_						( self :	Optional[int],					a_ :	str					):





    """simple docstring"""



    UpperCamelCase__ 						=						[]
    UpperCamelCase__ 						=						""

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

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

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

    return output

 def 		lowercase_						( self :	List[Any],					a_ :	list[Token]					):





    """simple docstring"""



    UpperCamelCase__ 						=						""

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

    return output

 def 		lowercase_						( self :	Any,					a_ :	str,					a_ :	str					):





    """simple docstring"""



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

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

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

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

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

 def 		lowercase_						( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	int,					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
       __lowercase:		Optional[Any]							=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)

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