
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class 			__A( a			):
				snake_case_						   =    (DDPMScheduler,)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								__a										=    {
								    '''num_train_timesteps''': 1_000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''variance_type''': '''fixed_small''',
								    '''clip_sample''': True,
								}

								config.update(**_snake_case				)
								return config




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								for timesteps in [1, 5, 100, 1_000]:
												self.check_over_configs(num_train_timesteps=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,     [0.002, 0.02, 0.2, 2]				):
												self.check_over_configs(beta_start=_snake_case		,     beta_end=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								for t in [0, 500, 999]:
												self.check_over_forward(time_step=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_0979				)				) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    len(_snake_case				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter
								__a										=    torch.manual_seed(0				)

								for t in reversed(range(_snake_case				)				):
												# 1. predict noise residual
												__a										=    model(_snake_case		,     _snake_case				)

												# 2. predict previous mean of sample x_t-1
												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case		,     generator=_snake_case				).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												__a										=    pred_prev_sample

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 258.9606				) < 1E-2
								assert abs(result_mean.item() - 0.3372				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config(prediction_type='''v_prediction'''				)
								__a										=    scheduler_class(**_snake_case				)

								__a										=    len(_snake_case				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter
								__a										=    torch.manual_seed(0				)

								for t in reversed(range(_snake_case				)				):
												# 1. predict noise residual
												__a										=    model(_snake_case		,     _snake_case				)

												# 2. predict previous mean of sample x_t-1
												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case		,     generator=_snake_case				).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												__a										=    pred_prev_sample

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 202.0296				) < 1E-2
								assert abs(result_mean.item() - 0.2631				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [100, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=_snake_case				)

								__a										=    scheduler.timesteps

								for i, timestep in enumerate(_snake_case				):
												if i == len(_snake_case				) - 1:
																__a										=    -1
												else:
																__a										=    timesteps[i + 1]

												__a										=    scheduler.previous_timestep(_snake_case				)
												__a										=    prev_t.item()

												self.assertEqual(_snake_case		,     _snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [100, 87, 50, 51, 0]

								with self.assertRaises(_snake_case		,     msg='''`custom_timesteps` must be in descending order.'''				):
												scheduler.set_timesteps(timesteps=_snake_case				)




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


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [100, 87, 50, 1, 0]
								__a										=    len(_snake_case				)

								with self.assertRaises(_snake_case		,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''				):
												scheduler.set_timesteps(num_inference_steps=_snake_case		,     timesteps=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [scheduler.config.num_train_timesteps]

								with self.assertRaises(
								    _snake_case		,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''		,     ):
												scheduler.set_timesteps(timesteps=_snake_case				)



import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "mvp"
    UpperCAmelCase__						:		Tuple      =  ["past_key_values"]
    UpperCAmelCase__						:		Union[str, Any]      =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(			self					,   A_=50267					,   A_=1024					,   A_=12					,   A_=4096					,   A_=16					,   A_=12					,   A_=4096					,   A_=16					,   A_=0.0					,   A_=0.0					,   A_="gelu"					,   A_=1024					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=0.0					,   A_=False					,   A_=True					,   A_=1					,   A_=0					,   A_=2					,   A_=True					,   A_=2					,   A_=2					,   A_=False					,   A_=100					,   A_=800					,   **A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=d_model
         __UpperCamelCase    							=encoder_ffn_dim
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=encoder_attention_heads
         __UpperCamelCase    							=decoder_ffn_dim
         __UpperCamelCase    							=decoder_layers
         __UpperCamelCase    							=decoder_attention_heads
         __UpperCamelCase    							=dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=activation_function
         __UpperCamelCase    							=init_std
         __UpperCamelCase    							=encoder_layerdrop
         __UpperCamelCase    							=decoder_layerdrop
         __UpperCamelCase    							=classifier_dropout
         __UpperCamelCase    							=use_cache
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True
         __UpperCamelCase    							=use_prompt
         __UpperCamelCase    							=prompt_length
         __UpperCamelCase    							=prompt_mid_dim

         super().__init__(
             pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_					,   is_encoder_decoder=A_					,   decoder_start_token_id=A_					,   forced_eos_token_id=A_					,   **A_					,   )

         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'					,   A_    ):
              __UpperCamelCase    							=self.bos_token_id
              warnings.warn(
                  f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                  'The config can simply be saved and uploaded again to be fixed.'    )





