






"""simple docstring"""

from dataclasses import dataclass

lowercase           =							'''0.1'''
lowercase           =							'''Lucia Harcekova'''

@dataclass
class     A_				:
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					42







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





class     A_				:







				def __init__(				self				:    str    ,     __lowerCamelCase				:    int = 1_3    ,     __lowerCamelCase				:    int = 6						)					->       None:
										__magic_name__      =  window_size
										__magic_name__      =  lookahead_buffer_size
										__magic_name__      =  self.window_size - self.lookahead_buffer_size







				def      _snake_case       (				self				:    Tuple    ,     __lowerCamelCase				:    str						)					->       list[Token]:
										__magic_name__      =  []
										__magic_name__      =  ""

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

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

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

										return output







				def      _snake_case       (				self				:    Dict    ,     __lowerCamelCase				:    list[Token]						)					->       str:
										__magic_name__      =  ""

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

										return output







				def      _snake_case       (				self				:    Optional[int]    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    str						)					->       Token:

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

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

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

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

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







				def      _snake_case       (				self				:    Optional[Any]    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    int    ,     __lowerCamelCase				:    int						)					->       int:
										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
			lowercase           =							LZaaCompressor(window_size=13, 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."











"""simple docstring"""

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	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     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]    ,     __lowerCamelCase    ,     atol=1e-4						)						)



