



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    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				int  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				Union[str, Any]=9_9  ,	UpperCamelCase			:				Optional[Any]=1_3  ,	UpperCamelCase			:				Union[str, Any]=1_6  ,	UpperCamelCase			:				Any=7  ,	UpperCamelCase			:				int=True  ,	UpperCamelCase			:				Optional[Any]=True  ,	UpperCamelCase			:				Dict=True  ,	UpperCamelCase			:				Any=False  ,	UpperCamelCase			:				List[str]=True  ,	UpperCamelCase			:				Optional[Any]=2  ,	UpperCamelCase			:				str=3_2  ,	UpperCamelCase			:				int=4  ,	UpperCamelCase			:				Tuple=4  ,	UpperCamelCase			:				int=3_0  ,	UpperCamelCase			:				Tuple=0  ,	UpperCamelCase			:				Tuple=1  ,	UpperCamelCase			:				str=2  ,	UpperCamelCase			:				Any=None  ,	)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						parent
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						batch_size
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						decoder_seq_length
      # For common tests
      __SCREAMING_SNAKE_CASE							:     int							=						self.decoder_seq_length
      __SCREAMING_SNAKE_CASE							:     Tuple							=						is_training
      __SCREAMING_SNAKE_CASE							:     List[str]							=						use_attention_mask
      __SCREAMING_SNAKE_CASE							:     int							=						use_labels

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

      __SCREAMING_SNAKE_CASE							:     str							=						None
      __SCREAMING_SNAKE_CASE							:     List[str]							=						decoder_seq_length
      __SCREAMING_SNAKE_CASE							:     Dict							=						2
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						1





     def 						__snake_case     (							self			:				Any					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Dict							=						ids_tensor([self.batch_size, self.decoder_seq_length]  ,	self.vocab_size					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						None
      if self.use_attention_mask:
       __SCREAMING_SNAKE_CASE							:     List[str]							=						ids_tensor([self.batch_size, self.decoder_seq_length]  ,	vocab_size=2					)

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

      __SCREAMING_SNAKE_CASE							:     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			:				Optional[int]  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				List[Any]  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				Union[str, Any]  ,	)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						True
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						SpeechaTextaDecoder(config=UpperCamelCase					).to(UpperCamelCase					).eval()
      __SCREAMING_SNAKE_CASE							:     Any							=						input_ids[:2]

      input_ids[input_ids == 0] += 1
      # first forward pass
      __SCREAMING_SNAKE_CASE							:     int							=						model(UpperCamelCase  ,	use_cache=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						model(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     int							=						model(UpperCamelCase  ,	use_cache=UpperCamelCase					)

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

      __SCREAMING_SNAKE_CASE							:     str							=						outputs["past_key_values"]

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

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

      print(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						model(UpperCamelCase					)["last_hidden_state"]
      __SCREAMING_SNAKE_CASE							:     Any							=						model(UpperCamelCase  ,	past_key_values=UpperCamelCase					)["last_hidden_state"]

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

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






     def 						__snake_case     (							self			:				Optional[int]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.prepare_config_and_inputs()
      (
          (
          __SCREAMING_SNAKE_CASE
      ),		(
          __SCREAMING_SNAKE_CASE
      ),		(
          __SCREAMING_SNAKE_CASE
      ),		(
          __SCREAMING_SNAKE_CASE
      ),		
      )							:     Optional[int]							=						config_and_inputs

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						{
          "input_ids": input_ids,
          "attention_mask": attention_mask,
      }
      return config, inputs_dict






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





     def 						__snake_case     (							self			:				List[str]  ,	)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						SpeechaTextaStandaloneDecoderModelTester(self  ,	is_training=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Any							=						ConfigTester(self  ,	config_class=UpperCamelCase					)





     def 						__snake_case     (							self			:				Dict					)->Optional[Any]:
      pass





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





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





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





     def 						__snake_case     (							self			:				List[str]					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase					)






     def 						__snake_case     (							self			:				Optional[int]					)->str:
      return













import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					(LMSDiscreteScheduler,)
   lowerCAmelCase__  	=					10




   def _lowerCAmelCase (self      :List[str]       ,  **_UpperCamelCase      :Optional[int]					)->   List[str]:
         __A			  =							{
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**_UpperCamelCase					)
         return config




   def _lowerCAmelCase (self      :List[str]					)->   Tuple:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Dict					)->   Optional[int]:
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]					):
               self.check_over_configs(beta_start=_UpperCamelCase       ,  beta_end=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[int]					)->   Any:
         for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=_UpperCamelCase					)




   def _lowerCAmelCase (self      :List[str]					)->   str:
         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=_UpperCamelCase					)




   def _lowerCAmelCase (self      :str					)->   List[str]:
         for t in [0, 500, 800]:
               self.check_over_forward(time_step=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[Any]					)->   Tuple:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
         assert abs(result_mean.item() - 1.3_1					) < 1e-3




   def _lowerCAmelCase (self      :Optional[Any]					)->   Optional[int]:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config(prediction_type='''v_prediction'''					)
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 0.0_0_1_7					) < 1e-2
         assert abs(result_mean.item() - 2.2_676e-06					) < 1e-3




   def _lowerCAmelCase (self      :List[Any]					)->   List[Any]:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps       ,  device=_UpperCamelCase					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __A			  =							sample.to(_UpperCamelCase					)

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
         assert abs(result_mean.item() - 1.3_1					) < 1e-3






   def _lowerCAmelCase (self      :List[str]					)->   int:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase       ,  use_karras_sigmas=_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps       ,  device=_UpperCamelCase					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter.to(_UpperCamelCase					) * scheduler.init_noise_sigma
         __A			  =							sample.to(_UpperCamelCase					)

         for t in scheduler.timesteps:
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 3_8_1_2.9_9_2_7					) < 2e-2
         assert abs(result_mean.item() - 4.9_6_4_8					) < 1e-3





