






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      UpperCamelCase_							:




						def __init__(    self		,	lowercase		,	lowercase=99		,	lowercase=13		,	lowercase=16		,	lowercase=7		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=False		,	lowercase=True		,	lowercase=2		,	lowercase=32		,	lowercase=4		,	lowercase=4		,	lowercase=30		,	lowercase=0		,	lowercase=1		,	lowercase=2		,	lowercase=None		,	)			->						int:
													_a  :    Union[str, Any]			=						parent
													_a  :    Dict			=						batch_size
													_a  :    int			=						decoder_seq_length
													# For common tests
													_a  :    Any			=						self.decoder_seq_length
													_a  :    List[Any]			=						is_training
													_a  :    Optional[Any]			=						use_attention_mask
													_a  :    str			=						use_labels

													_a  :    int			=						vocab_size
													_a  :    Optional[int]			=						d_model
													_a  :    Optional[int]			=						d_model
													_a  :    Optional[Any]			=						decoder_layers
													_a  :    str			=						decoder_layers
													_a  :    List[Any]			=						decoder_ffn_dim
													_a  :    Optional[int]			=						decoder_attention_heads
													_a  :    Optional[int]			=						decoder_attention_heads
													_a  :    Tuple			=						eos_token_id
													_a  :    Optional[int]			=						bos_token_id
													_a  :    str			=						pad_token_id
													_a  :    Optional[int]			=						decoder_start_token_id
													_a  :    List[Any]			=						use_cache
													_a  :    Dict			=						max_position_embeddings

													_a  :    Tuple			=						None
													_a  :    Optional[Any]			=						decoder_seq_length
													_a  :    int			=						2
													_a  :    str			=						1




						def      snake_case__(    self       )			->						Union[str, Any]:
													_a  :    Optional[int]			=						ids_tensor([self.batch_size, self.decoder_seq_length]		,	self.vocab_size       )

													_a  :    Any			=						None
													if self.use_attention_mask:
																				_a  :    Any			=						ids_tensor([self.batch_size, self.decoder_seq_length]		,	vocab_size=2       )

													_a  :    List[Any]			=						None
													if self.use_labels:
																				_a  :    int			=						ids_tensor([self.batch_size, self.decoder_seq_length]		,	self.vocab_size       )

													_a  :    Optional[int]			=						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      snake_case__(    self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)			->						List[str]:
													_a  :    Tuple			=						True
													_a  :    Optional[Any]			=						SpeechaTextaDecoder(config=_lowerCamelCase       ).to(_lowerCamelCase       ).eval()
													_a  :    Dict			=						input_ids[:2]

													input_ids[input_ids == 0] += 1
													# first forward pass
													_a  :    Any			=						model(_lowerCamelCase		,	use_cache=_lowerCamelCase       )
													_a  :    Dict			=						model(_lowerCamelCase       )
													_a  :    Any			=						model(_lowerCamelCase		,	use_cache=_lowerCamelCase       )

													self.parent.assertTrue(len(_lowerCamelCase       ) == len(_lowerCamelCase       )       )
													self.parent.assertTrue(len(_lowerCamelCase       ) == len(_lowerCamelCase       ) + 1       )

													_a  :    Dict			=						outputs["past_key_values"]

													# create hypothetical next token and extent to next_input_ids
													_a  :    Optional[int]			=						ids_tensor((2, 1)		,	config.vocab_size - 1       ) + 1

													# append to next input_ids and
													_a  :    Optional[int]			=						torch.cat([input_ids, next_tokens]		,	dim=-1       )

													print(_lowerCamelCase       )

													_a  :    Union[str, Any]			=						model(_lowerCamelCase       )["last_hidden_state"]
													_a  :    str			=						model(_lowerCamelCase		,	past_key_values=_lowerCamelCase       )["last_hidden_state"]

													# select random slice
													_a  :    Optional[int]			=						ids_tensor((1,)		,	output_from_past.shape[-1]       ).item()
													_a  :    List[Any]			=						output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
													_a  :    Dict			=						output_from_past[:, 0, random_slice_idx].detach()

													# test that outputs are equal for slice
													assert torch.allclose(_lowerCamelCase		,	_lowerCamelCase		,	atol=1e-3       )







						def      snake_case__(    self       )			->						Dict:
													_a  :    Optional[Any]			=						self.prepare_config_and_inputs()
													(
													    _a
													)  :    int			=						config_and_inputs

													_a  :    Dict			=						{
													    "input_ids": input_ids,
													    "attention_mask": attention_mask,
													}
													return config, inputs_dict

@require_torch
class      UpperCamelCase_							(				UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       unittest.TestCase       ):
						lowercase									=  (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
						lowercase									=  (SpeechaTextaForCausalLM,) if is_torch_available() else ()
						lowercase									=  {"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
						lowercase									=  True
						lowercase									=  False




						def      snake_case__(    self		,	)			->						Tuple:
													_a  :    Dict			=						SpeechaTextaStandaloneDecoderModelTester(self		,	is_training=_lowerCamelCase       )
													_a  :    Tuple			=						ConfigTester(self		,	config_class=_lowerCamelCase       )




						def      snake_case__(    self       )			->						List[Any]:
													pass




						def      snake_case__(    self       )			->						Union[str, Any]:
													pass




						def      snake_case__(    self       )			->						str:
													pass




						def      snake_case__(    self       )			->						List[Any]:
													self.config_tester.run_common_tests()




						def      snake_case__(    self       )			->						List[Any]:
													_a  :    Any			=						self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_decoder_model_past(*_lowerCamelCase       )







						def      snake_case__(    self       )			->						str:
													return

import math




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      list[int]:

							"""simple docstring"""






							if not isinstance(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  ):
														_UpperCAmelCase  :			Optional[Any]	       =		F"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE  )
							if number < 1:
														_UpperCAmelCase  :			Dict	       =		F"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			List[Any]	       =		[number]
							while number != 1:
														if number % 2 == 0:
																					_UpperCAmelCase  :			List[str]	       =		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE  )  )
														else:
																					_UpperCAmelCase  :			Dict	       =		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE  ) * math.sqrt(_SCREAMING_SNAKE_CASE  ) * math.sqrt(_SCREAMING_SNAKE_CASE  )  )
														sequence.append(_SCREAMING_SNAKE_CASE  )
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()




