
"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class     lowercase(_lowercase       ):
     __snake_case:	int						=     (DDPMScheduler,)



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         a__		=			{
             'num_train_timesteps': 1_0_0_0,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**__SCREAMING_SNAKE_CASE   )
         return config



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         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=__SCREAMING_SNAKE_CASE     ,				beta_end=__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



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



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







         """simple docstring"""



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



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







         """simple docstring"""



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



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







         """simple docstring"""



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



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



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



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







         """simple docstring"""



         for t in [0, 5_0_0, 9_9_9]:
             self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__		=			self.scheduler_classes[0]
         a__		=			self.get_scheduler_config()
         a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

         assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ) - 0.0_09_79   )   ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ) - 0.02   )   ) < 1e-5



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         a__		=			self.scheduler_classes[0]
         a__		=			self.get_scheduler_config()
         a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

         a__		=			len(__SCREAMING_SNAKE_CASE   )

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

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

             # 2. predict previous mean of sample x_t-1
             a__		=			scheduler.step(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				generator=__SCREAMING_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(__SCREAMING_SNAKE_CASE   )   )
         a__		=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE   )   )

         assert abs(result_sum.item() - 2_58.96_06   ) < 1e-2
         assert abs(result_mean.item() - 0.33_72   ) < 1e-3



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







         """simple docstring"""



         a__		=			self.scheduler_classes[0]
         a__		=			self.get_scheduler_config(prediction_type='v_prediction'   )
         a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

         a__		=			len(__SCREAMING_SNAKE_CASE   )

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

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

             # 2. predict previous mean of sample x_t-1
             a__		=			scheduler.step(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				generator=__SCREAMING_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(__SCREAMING_SNAKE_CASE   )   )
         a__		=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE   )   )

         assert abs(result_sum.item() - 2_02.02_96   ) < 1e-2
         assert abs(result_mean.item() - 0.26_31   ) < 1e-3



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.scheduler_classes[0]
         a__		=			self.get_scheduler_config()
         a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

         a__		=			[1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE   )

         a__		=			scheduler.timesteps

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

             a__		=			scheduler.previous_timestep(__SCREAMING_SNAKE_CASE   )
             a__		=			prev_t.item()

             self.assertEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         a__		=			self.scheduler_classes[0]
         a__		=			self.get_scheduler_config()
         a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

         a__		=			[1_0_0, 8_7, 5_0, 5_1, 0]

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



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         a__		=			self.scheduler_classes[0]
         a__		=			self.get_scheduler_config()
         a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

         a__		=			[1_0_0, 8_7, 5_0, 1, 0]
         a__		=			len(__SCREAMING_SNAKE_CASE   )

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




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







         """simple docstring"""



         a__		=			self.scheduler_classes[0]
         a__		=			self.get_scheduler_config()
         a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

         a__		=			[scheduler.config.num_train_timesteps]

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




"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class     lowercase(_lowercase    ,						_lowercase    ,						_lowercase       ):



     @register_to_config
     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__()

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			False

         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )

         a__		=			TaConfig(
             vocab_size=__SCREAMING_SNAKE_CASE     ,				d_model=__SCREAMING_SNAKE_CASE     ,				num_heads=__SCREAMING_SNAKE_CASE     ,				d_kv=__SCREAMING_SNAKE_CASE     ,				d_ff=__SCREAMING_SNAKE_CASE     ,				dropout_rate=__SCREAMING_SNAKE_CASE     ,				feed_forward_proj=__SCREAMING_SNAKE_CASE     ,				is_decoder=__SCREAMING_SNAKE_CASE     ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			nn.ModuleList()
         for lyr_num in range(__SCREAMING_SNAKE_CASE   ):
             a__		=			TaBlock(__SCREAMING_SNAKE_CASE   )
             self.encoders.append(__SCREAMING_SNAKE_CASE   )

         a__		=			TaLayerNorm(__SCREAMING_SNAKE_CASE   )
         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.token_embedder(__SCREAMING_SNAKE_CASE   )

         a__		=			encoder_input_tokens.shape[1]
         a__		=			torch.arange(__SCREAMING_SNAKE_CASE     ,				device=encoder_input_tokens.device   )
         x += self.position_encoding(__SCREAMING_SNAKE_CASE   )

         a__		=			self.dropout_pre(__SCREAMING_SNAKE_CASE   )

         # inverted the attention mask
         a__		=			encoder_input_tokens.size()
         a__		=			self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         for lyr in self.encoders:
             a__		=			lyr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )[0]
         a__		=			self.layer_norm(__SCREAMING_SNAKE_CASE   )

         return self.dropout_post(__SCREAMING_SNAKE_CASE   ), encoder_inputs_mask


