code: 
  import unittest
  
  from transformers import SpeechaTextaConfig
  from transformers.testing_utils import is_torch_available, require_torch, torch_device
  
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  							import torch
  
  							from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
  							    SpeechaTextaDecoder,
  							    SpeechaTextaForCausalLM,
  							)
  
  
  
  
  @require_torch
  class      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   Dict			,  snake_case							:   Optional[Any]			,  snake_case							:   int=99			,  snake_case							:   Any=13			,  snake_case							:   List[Any]=16			,  snake_case							:   Union[str, Any]=7			,  snake_case							:   str=True			,  snake_case							:   Optional[int]=True			,  snake_case							:   Tuple=True			,  snake_case							:   List[Any]=False			,  snake_case							:   Union[str, Any]=True			,  snake_case							:   Dict=2			,  snake_case							:   Dict=32			,  snake_case							:   List[str]=4			,  snake_case							:   Dict=4			,  snake_case							:   int=30			,  snake_case							:   Tuple=0			,  snake_case							:   Dict=1			,  snake_case							:   Dict=2			,  snake_case							:   List[str]=None			,  )   -> List[Any]:
  							_lowercase					:  List[str]    			=      parent
  							_lowercase					:  Any    			=      batch_size
  							_lowercase					:  str    			=      decoder_seq_length
  							# For common tests
  							_lowercase					:  int    			=      self.decoder_seq_length
  							_lowercase					:  Optional[int]    			=      is_training
  							_lowercase					:  Union[str, Any]    			=      use_attention_mask
  							_lowercase					:  Optional[Any]    			=      use_labels
  
  							_lowercase					:  Tuple    			=      vocab_size
  							_lowercase					:  Any    			=      d_model
  							_lowercase					:  Tuple    			=      d_model
  							_lowercase					:  Optional[Any]    			=      decoder_layers
  							_lowercase					:  Union[str, Any]    			=      decoder_layers
  							_lowercase					:  int    			=      decoder_ffn_dim
  							_lowercase					:  List[str]    			=      decoder_attention_heads
  							_lowercase					:  str    			=      decoder_attention_heads
  							_lowercase					:  int    			=      eos_token_id
  							_lowercase					:  Optional[Any]    			=      bos_token_id
  							_lowercase					:  Dict    			=      pad_token_id
  							_lowercase					:  Dict    			=      decoder_start_token_id
  							_lowercase					:  List[Any]    			=      use_cache
  							_lowercase					:  List[Any]    			=      max_position_embeddings
  
  							_lowercase					:  Optional[int]    			=      None
  							_lowercase					:  Optional[Any]    			=      decoder_seq_length
  							_lowercase					:  Optional[int]    			=      2
  							_lowercase					:  Optional[Any]    			=      1
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]						)   -> str:
  							_lowercase					:  str    			=      ids_tensor([self.batch_size, self.decoder_seq_length]			,  self.vocab_size						)
  
  							_lowercase					:  List[str]    			=      None
  							if self.use_attention_mask:
  													_lowercase					:  Dict    			=      ids_tensor([self.batch_size, self.decoder_seq_length]			,  vocab_size=2						)
  
  							_lowercase					:  List[Any]    			=      None
  							if self.use_labels:
  													_lowercase					:  Dict    			=      ids_tensor([self.batch_size, self.decoder_seq_length]			,  self.vocab_size						)
  
  							_lowercase					:  Tuple    			=      SpeechaTextaConfig(
  							    vocab_size=self.vocab_size			,  d_model=self.d_model			,  decoder_layers=self.decoder_layers			,  decoder_ffn_dim=self.decoder_ffn_dim			,  decoder_attention_heads=self.decoder_attention_heads			,  eos_token_id=self.eos_token_id			,  bos_token_id=self.bos_token_id			,  use_cache=self.use_cache			,  pad_token_id=self.pad_token_id			,  decoder_start_token_id=self.decoder_start_token_id			,  max_position_embeddings=self.max_position_embeddings			,  )
  
  							return (
  							    config,
  							    input_ids,
  							    attention_mask,
  							    lm_labels,
  							)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple			,  snake_case							:   int			,  snake_case							:   int			,  snake_case							:   Optional[Any]			,  snake_case							:   Tuple			,  )   -> Any:
  							_lowercase					:  int    			=      True
  							_lowercase					:  int    			=      SpeechaTextaDecoder(config=snake_case						).to(snake_case						).eval()
  							_lowercase					:  str    			=      input_ids[:2]
  
  							input_ids[input_ids == 0] += 1
  							# first forward pass
  							_lowercase					:  int    			=      model(snake_case			,  use_cache=snake_case						)
  							_lowercase					:  List[str]    			=      model(snake_case						)
  							_lowercase					:  int    			=      model(snake_case			,  use_cache=snake_case						)
  
  							self.parent.assertTrue(len(snake_case						) == len(snake_case						)						)
  							self.parent.assertTrue(len(snake_case						) == len(snake_case						) + 1						)
  
  							_lowercase					:  str    			=      outputs["past_key_values"]
  
  							# create hypothetical next token and extent to next_input_ids
  							_lowercase					:  List[str]    			=      ids_tensor((2, 1)			,  config.vocab_size - 1						) + 1
  
  							# append to next input_ids and
  							_lowercase					:  Dict    			=      torch.cat([input_ids, next_tokens]			,  dim=-1						)
  
  							print(snake_case						)
  
  							_lowercase					:  List[Any]    			=      model(snake_case						)["last_hidden_state"]
  							_lowercase					:  List[str]    			=      model(snake_case			,  past_key_values=snake_case						)["last_hidden_state"]
  
  							# select random slice
  							_lowercase					:  Optional[int]    			=      ids_tensor((1,)			,  output_from_past.shape[-1]						).item()
  							_lowercase					:  int    			=      output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
  							_lowercase					:  Union[str, Any]    			=      output_from_past[:, 0, random_slice_idx].detach()
  
  							# test that outputs are equal for slice
  							assert torch.allclose(snake_case			,  snake_case			,  atol=1e-3						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]						)   -> Dict:
  							_lowercase					:  List[str]    			=      self.prepare_config_and_inputs()
  							(
  							    _lowercase						
  							)					:  Dict    			=      config_and_inputs
  
  							_lowercase					:  Any    			=      {
  							    "input_ids": input_ids,
  							    "attention_mask": attention_mask,
  							}
  							return config, inputs_dict
  
  
  
  
  @require_torch
  class      __lowercase	(	__snake_case      ,   __snake_case      ,   __snake_case      ,   unittest.TestCase					):
  	_A   			=      (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
  	_A   			=      (SpeechaTextaForCausalLM,) if is_torch_available() else ()
  	_A   			=      {"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
  	_A   			=      True
  	_A   			=      False
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]			,  )   -> Union[str, Any]:
  							_lowercase					:  Union[str, Any]    			=      SpeechaTextaStandaloneDecoderModelTester(self			,  is_training=snake_case						)
  							_lowercase					:  Any    			=      ConfigTester(self			,  config_class=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]						)   -> Optional[Any]:
  							pass
  
  
  
  
  
  
  	def 			_a(self							:   Dict						)   -> Dict:
  							pass
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> Union[str, Any]:
  							pass
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]						)   -> int:
  							self.config_tester.run_common_tests()
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]						)   -> List[Any]:
  							_lowercase					:  int    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_decoder_model_past(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]						)   -> List[str]:
  							return
  
  
  
  
  
style_context: 
  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."
  
  
  
  
  