


from dataclasses import dataclass

lowercase_           =	'0.1'
lowercase_           =	'Lucia Harcekova'


@dataclass
class        A_					:




      '''simple docstring'''


      __snake_case					       =   42
      __snake_case					       =   42
      __snake_case					       =   42




      def __repr__(				self:       Any				):
       return F'({self.offset}, {self.length}, {self.indicator})'



class        A_					:




      '''simple docstring'''


      def __init__(				self:       str						,      a:       int = 13						,      a:       int = 6				):
       __lowerCamelCase		:							Union[str, Any]							=						window_size
       __lowerCamelCase		:							Any							=						lookahead_buffer_size
       __lowerCamelCase		:							int							=						self.window_size - self.lookahead_buffer_size




      def      _snake_case	(				self:       Optional[Any]						,      a:       str				):
       __lowerCamelCase		:							Optional[int]							=						[]
       __lowerCamelCase		:							List[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)
        __lowerCamelCase		:							int							=						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:
         __lowerCamelCase		:							Dict							=						search_buffer[-self.search_buffer_size :]

        # update the text
        __lowerCamelCase		:							Optional[int]							=						text[token.length + 1 :]

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

       return output




      def      _snake_case	(				self:       str						,      a:       list[Token]				):
       __lowerCamelCase		:							Optional[int]							=						''

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

       return output




      def      _snake_case	(				self:       int						,      a:       str						,      a:       str				):

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

       # Initialise result parameters to default values
       __lowerCamelCase					,     __lowerCamelCase		:							str							=						0, 0

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

       for i, character in enumerate(a				):
        __lowerCamelCase		:							List[str]							=						len(a				) - i
        if character == text[0]:
         __lowerCamelCase		:							Optional[int]							=						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:
          __lowerCamelCase					,     __lowerCamelCase		:							List[Any]							=						found_offset, found_length

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




      def      _snake_case	(				self:       Optional[Any]						,      a:       str						,      a:       str						,      a:       int						,      a:       int				):
       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_           =	LZaaCompressor(window_size=1_3, lookahead_buffer_size=6)

       # Example
       lowercase_           =	'cabracadabrarrarrad'
       lowercase_           =	lzaa_compressor.compress(TEXT)
       print(lzaa_compressor.compress('ababcbababaa'))
       lowercase_           =	lzaa_compressor.decompress(compressed_text)
       assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import CamembertModel


@require_torch
@require_sentencepiece
@require_tokenizers
class        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.tensor(
           [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,      device=a						,      dtype=torch.float						,      )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(torch.allclose(output[:, :3, :3]						,      a						,      atol=1e-4				)				)

