




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:		Any,     _lowercase:		List[Any],     _lowercase:		List[Any]=99,     _lowercase:		str=13,     _lowercase:		Union[str, Any]=16,     _lowercase:		List[str]=7,     _lowercase:		str=True,     _lowercase:		Dict=True,     _lowercase:		Optional[int]=True,     _lowercase:		Optional[Any]=False,     _lowercase:		int=True,     _lowercase:		Union[str, Any]=2,     _lowercase:		List[str]=32,     _lowercase:		Optional[Any]=4,     _lowercase:		List[str]=4,     _lowercase:		Optional[int]=30,     _lowercase:		Any=0,     _lowercase:		Optional[int]=1,     _lowercase:		List[str]=2,     _lowercase:		Union[str, Any]=None,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  decoder_seq_length
									# For common tests
									__lowerCAmelCase					=  self.decoder_seq_length
									__lowerCAmelCase					=  is_training
									__lowerCAmelCase					=  use_attention_mask
									__lowerCAmelCase					=  use_labels

									__lowerCAmelCase					=  vocab_size
									__lowerCAmelCase					=  d_model
									__lowerCAmelCase					=  d_model
									__lowerCAmelCase					=  decoder_layers
									__lowerCAmelCase					=  decoder_layers
									__lowerCAmelCase					=  decoder_ffn_dim
									__lowerCAmelCase					=  decoder_attention_heads
									__lowerCAmelCase					=  decoder_attention_heads
									__lowerCAmelCase					=  eos_token_id
									__lowerCAmelCase					=  bos_token_id
									__lowerCAmelCase					=  pad_token_id
									__lowerCAmelCase					=  decoder_start_token_id
									__lowerCAmelCase					=  use_cache
									__lowerCAmelCase					=  max_position_embeddings

									__lowerCAmelCase					=  None
									__lowerCAmelCase					=  decoder_seq_length
									__lowerCAmelCase					=  2
									__lowerCAmelCase					=  1




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  ids_tensor([self.batch_size, self.decoder_seq_length],     self.vocab_size)

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

									__lowerCAmelCase					=  None
									if self.use_labels:
													__lowerCAmelCase					=  ids_tensor([self.batch_size, self.decoder_seq_length],     self.vocab_size)

									__lowerCAmelCase					=  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     _lowercase						(   self:		Dict,     _lowercase:		int,     _lowercase:		Union[str, Any],     _lowercase:		Tuple,     _lowercase:		Any,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  True
									__lowerCAmelCase					=  SpeechaTextaDecoder(config=_lowercase).to(_lowercase).eval()
									__lowerCAmelCase					=  input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									__lowerCAmelCase					=  model(_lowercase,     use_cache=_lowercase)
									__lowerCAmelCase					=  model(_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     use_cache=_lowercase)

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

									__lowerCAmelCase					=  outputs["""past_key_values"""]

									# create hypothetical next token and extent to next_input_ids
									__lowerCAmelCase					=  ids_tensor((2, 1),     config.vocab_size - 1) + 1

									# append to next input_ids and
									__lowerCAmelCase					=  torch.cat([input_ids, next_tokens],     dim=-1)

									print(_lowercase)

									__lowerCAmelCase					=  model(_lowercase)["""last_hidden_state"""]
									__lowerCAmelCase					=  model(_lowercase,     past_key_values=_lowercase)["""last_hidden_state"""]

									# select random slice
									__lowerCAmelCase					=  ids_tensor((1,),     output_from_past.shape[-1]).item()
									__lowerCAmelCase					=  output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									__lowerCAmelCase					=  output_from_past[:, 0, random_slice_idx].detach()

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







					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.prepare_config_and_inputs()
									(
									    (
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				
									)					=  config_and_inputs

									__lowerCAmelCase					=  {
									    """input_ids""": input_ids,
									    """attention_mask""": attention_mask,
									}
									return config, inputs_dict





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




					def     _lowercase						(   self:		Union[str, Any],     ):


									'''simple docstring'''


									__lowerCAmelCase					=  SpeechaTextaStandaloneDecoderModelTester(self,     is_training=_lowercase)
									__lowerCAmelCase					=  ConfigTester(self,     config_class=_lowercase)




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									self.config_tester.run_common_tests()




					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*_lowercase)







					def     _lowercase						(   self:		str):


									'''simple docstring'''


									return













'''simple docstring'''







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    List[Any]				       =					(DDIMScheduler,)
      SCREAMING_SNAKE_CASE				:    str				       =					(("eta", 0.0), ("num_inference_steps", 5_0))




      def 							lowerCamelCase     (					self  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   List[str]												=	{
                 '''num_train_timesteps''': 1000,
                 '''beta_start''': 0.00_01,
                 '''beta_end''': 0.02,
                 '''beta_schedule''': '''linear''',
                 '''clip_sample''': True,
             }

             config.update(**_UpperCAmelCase						)
             return config




      def 							lowerCamelCase     (					self  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_scheduler_config(**_UpperCAmelCase						)
             UpperCAmelCase__	:   str												=	scheduler_class(**_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   int												=	10, 0.0

             UpperCAmelCase__	:   Optional[Any]												=	self.dummy_model()
             UpperCAmelCase__	:   List[Any]												=	self.dummy_sample_deter

             scheduler.set_timesteps(_UpperCAmelCase						)

             for t in scheduler.timesteps:
                    UpperCAmelCase__	:   Dict												=	model(_UpperCAmelCase  ,		_UpperCAmelCase						)
                    UpperCAmelCase__	:   int												=	scheduler.step(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						).prev_sample

             return sample




      def 							lowerCamelCase     (					self						):
             for timesteps in [100, 500, 1000]:
                    self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   Any												=	self.get_scheduler_config(steps_offset=1						)
             UpperCAmelCase__	:   Optional[Any]												=	scheduler_class(**_UpperCAmelCase						)
             scheduler.set_timesteps(5						)
             assert torch.equal(scheduler.timesteps  ,		torch.LongTensor([801, 601, 401, 201, 1]						)						)




      def 							lowerCamelCase     (					self						):
             for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,		[0.0_02, 0.02, 0.2, 2]						):
                    self.check_over_configs(beta_start=_UpperCAmelCase  ,		beta_end=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             self.check_over_configs(thresholding=_UpperCAmelCase						)
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=_UpperCAmelCase  ,		prediction_type=_UpperCAmelCase  ,		sample_max_value=_UpperCAmelCase  ,		)




      def 							lowerCamelCase     (					self						):
             for t in [1, 10, 49]:
                    self.check_over_forward(time_step=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for t, num_inference_steps in zip([1, 10, 50]  ,		[10, 50, 500]						):
                    self.check_over_forward(time_step=_UpperCAmelCase  ,		num_inference_steps=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for t, eta in zip([1, 10, 49]  ,		[0.0, 0.5, 1.0]						):
                    self.check_over_forward(time_step=_UpperCAmelCase  ,		eta=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   int												=	self.get_scheduler_config()
             UpperCAmelCase__	:   Optional[Any]												=	scheduler_class(**_UpperCAmelCase						)

             assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0						) - 0.0						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(420  ,		400						) - 0.1_47_71						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(980  ,		960						) - 0.3_24_60						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0						) - 0.0						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(487  ,		486						) - 0.0_09_79						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(999  ,		998						) - 0.02						)						) < 1E-5




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.full_loop()

             UpperCAmelCase__	:   Dict												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   str												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_72.00_67						) < 1E-2
             assert abs(result_mean.item() - 0.22_39_67						) < 1E-3




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Any												=	self.full_loop(prediction_type='''v_prediction'''						)

             UpperCAmelCase__	:   Union[str, Any]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   Tuple												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 52.53_02						) < 1E-2
             assert abs(result_mean.item() - 0.06_84						) < 1E-3




      def 							lowerCamelCase     (					self						):
             # We specify different beta, so that the first alpha is 0.99
             UpperCAmelCase__	:   str												=	self.full_loop(set_alpha_to_one=_UpperCAmelCase  ,		beta_start=0.01						)
             UpperCAmelCase__	:   List[str]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   Union[str, Any]												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_49.82_95						) < 1E-2
             assert abs(result_mean.item() - 0.19_51						) < 1E-3




      def 							lowerCamelCase     (					self						):
             # We specify different beta, so that the first alpha is 0.99
             UpperCAmelCase__	:   str												=	self.full_loop(set_alpha_to_one=_UpperCAmelCase  ,		beta_start=0.01						)
             UpperCAmelCase__	:   Optional[Any]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   int												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_49.07_84						) < 1E-2
             assert abs(result_mean.item() - 0.19_41						) < 1E-3