
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






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()




