




"""simple docstring"""

from dataclasses import dataclass

_lowerCamelCase	:      Optional[int]   =							'0.1'
_lowerCamelCase	:      Tuple   =							'Lucia Harcekova'

@dataclass
class 							lowercase   :
				__lowerCAmelCase     :     int
				__lowerCAmelCase     :     int
				__lowerCAmelCase     :     str







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



					"""simple docstring"""

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

class 							lowercase   :







				def __init__(				self :       Union[str, Any]   ,  _lowerCamelCase :       int = 13   ,  _lowerCamelCase :       int = 6							):



					"""simple docstring"""

					A_	:					List[Any]						 =		window_size
					A_	:					Tuple						 =		lookahead_buffer_size
					A_	:					Dict						 =		self.window_size - self.lookahead_buffer_size







				def        a_	(				self :       Tuple   ,  _lowerCamelCase :       str							):



					"""simple docstring"""

					A_	:					str						 =		[]
					A_	:					Any						 =		''''''

					# 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_	:					Optional[int]						 =		self._find_encoding_token(_lowerCamelCase   ,  _lowerCamelCase							)

						# 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(_lowerCamelCase							) > self.search_buffer_size:
							A_	:					str						 =		search_buffer[-self.search_buffer_size :]

						# update the text
						A_	:					List[Any]						 =		text[token.length + 1 :]

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

					return output







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



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		''''''

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

					return output







				def        a_	(				self :       List[Any]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       str							):



					"""simple docstring"""

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

					# Initialise result parameters to default values
					A_ ,			A_	:					Dict						 =		0, 0

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

					for i, character in enumerate(_lowerCamelCase							):
						A_	:					int						 =		len(_lowerCamelCase							) - i
						if character == text[0]:
							A_	:					Any						 =		self._match_length_from_index(_lowerCamelCase   ,  _lowerCamelCase   ,  0   ,  _lowerCamelCase							)
							# 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_	:					Optional[int]						 =		found_offset, found_length

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







				def        a_	(				self :       Any   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       int							):



					"""simple docstring"""

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


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

		testmod()
		# Initialize compressor class
		_lowerCamelCase	:      List[Any]   =							LZaaCompressor(window_size=13, lookahead_buffer_size=6)

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











"""simple docstring"""

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xglm import XGLMTokenizer
else:
		_lowerCamelCase	:      Union[str, Any]   =							None


_lowerCamelCase	:      Optional[Any]   =							logging.get_logger(__name__)

_lowerCamelCase	:      Tuple   =							{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase	:      Union[str, Any]   =							{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

_lowerCamelCase	:      Dict   =							{
    'facebook/xglm-564M': 2048,
}

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     Optional[int]								= VOCAB_FILES_NAMES
				__lowerCAmelCase     :     Tuple								= PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase     :     List[Any]								= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase     :     Union[str, Any]								= ["""input_ids""", """attention_mask"""]
				__lowerCAmelCase     :     List[str]								= XGLMTokenizer







				def __init__(				self :       str   ,  _lowerCamelCase :       int=None   ,  _lowerCamelCase :       Any=None   ,  _lowerCamelCase :       List[str]="<s>"   ,  _lowerCamelCase :       int="</s>"   ,  _lowerCamelCase :       Any="</s>"   ,  _lowerCamelCase :       Optional[int]="<s>"   ,  _lowerCamelCase :       List[Any]="<unk>"   ,  _lowerCamelCase :       Optional[int]="<pad>"   ,  **_lowerCamelCase :       List[Any]   ,  ):



					"""simple docstring"""

					A_	:					Tuple						 =		7
					A_	:					Dict						 =		[F"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

					A_	:					str						 =		kwargs.get('''additional_special_tokens'''   ,  []							)
					kwargs["additional_special_tokens"] += [
					    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    _lowerCamelCase   ,  tokenizer_file=_lowerCamelCase   ,  bos_token=_lowerCamelCase   ,  eos_token=_lowerCamelCase   ,  sep_token=_lowerCamelCase   ,  cls_token=_lowerCamelCase   ,  unk_token=_lowerCamelCase   ,  pad_token=_lowerCamelCase   ,  **_lowerCamelCase   ,  )

					A_	:					Tuple						 =		vocab_file
					A_	:					str						 =		False if not self.vocab_file else True







				def        a_	(				self :       List[Any]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
					A_	:					List[Any]						 =		[self.sep_token_id]
					return sep + token_ids_a + sep + sep + token_ids_a







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					A_	:					Dict						 =		[self.sep_token_id]

					if token_ids_a is None:
						return len(sep + token_ids_a							) * [0]
					return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[str] = None							):



					"""simple docstring"""

					if not self.can_save_slow_tokenizer:
						raise ValueError(
						    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
						    '''tokenizer.'''							)

					if not os.path.isdir(_lowerCamelCase							):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""							)
						return
					A_	:					Tuple						 =		os.path.join(
					    _lowerCamelCase   ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

					if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							):
						copyfile(self.vocab_file   ,  _lowerCamelCase							)

					return (out_vocab_file,)





