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
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _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: 
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> bool:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if not isinstance(_UpperCAmelCase   , _UpperCAmelCase     ):
  												_lowercase					:  Optional[int]    			=      f"""Input value of [number={number}] must be an integer"""
  												raise TypeError(_UpperCAmelCase     )
  						if number < 0 or number % 2 == 1:
  												return False
  						_lowercase					:  List[str]    			=      int(number**0.5     )
  						return number == number_sqrt * (number_sqrt + 1)
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  