




'''simple docstring'''

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					Dict    =  (KDPMaAncestralDiscreteScheduler,)
						UpperCAmelCase_		:					Any    =  10






						def       __lowerCAmelCase(   self						:						Optional[int]					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						{
									    """num_train_timesteps""": 1_1_0_0,
									    """beta_start""": 0.00_01,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**__UpperCamelCase       )
									return config






						def       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,   [0.00_02, 0.0_02, 0.02]       ):
												self.check_over_configs(beta_start=__UpperCamelCase					,   beta_end=__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									if torch_device == "mps":
												return
									snake_case__          =						self.scheduler_classes[0]
									snake_case__          =						self.get_scheduler_config()
									snake_case__          =						scheduler_class(**__UpperCamelCase       )

									scheduler.set_timesteps(self.num_inference_steps       )

									snake_case__          =						torch.manual_seed(0       )

									snake_case__          =						self.dummy_model()
									snake_case__          =						self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case__          =						sample.to(__UpperCamelCase       )

									for i, t in enumerate(scheduler.timesteps       ):
												snake_case__          =						scheduler.scale_model_input(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						model(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						scheduler.step(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   generator=__UpperCamelCase       )
												snake_case__          =						output.prev_sample

									snake_case__          =						torch.sum(torch.abs(__UpperCamelCase       )       )
									snake_case__          =						torch.mean(torch.abs(__UpperCamelCase       )       )

									assert abs(result_sum.item() - 1_38_49.38_77       ) < 1E-2
									assert abs(result_mean.item() - 18.03_31       ) < 5E-3






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									if torch_device == "mps":
												return
									snake_case__          =						self.scheduler_classes[0]
									snake_case__          =						self.get_scheduler_config(prediction_type="""v_prediction"""       )
									snake_case__          =						scheduler_class(**__UpperCamelCase       )

									scheduler.set_timesteps(self.num_inference_steps       )

									snake_case__          =						self.dummy_model()
									snake_case__          =						self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case__          =						sample.to(__UpperCamelCase       )

									snake_case__          =						torch.manual_seed(0       )

									for i, t in enumerate(scheduler.timesteps       ):
												snake_case__          =						scheduler.scale_model_input(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						model(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						scheduler.step(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   generator=__UpperCamelCase       )
												snake_case__          =						output.prev_sample

									snake_case__          =						torch.sum(torch.abs(__UpperCamelCase       )       )
									snake_case__          =						torch.mean(torch.abs(__UpperCamelCase       )       )

									assert abs(result_sum.item() - 3_28.99_70       ) < 1E-2
									assert abs(result_mean.item() - 0.42_84       ) < 1E-3







						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									if torch_device == "mps":
												return
									snake_case__          =						self.scheduler_classes[0]
									snake_case__          =						self.get_scheduler_config()
									snake_case__          =						scheduler_class(**__UpperCamelCase       )

									scheduler.set_timesteps(self.num_inference_steps					,   device=__UpperCamelCase       )
									snake_case__          =						torch.manual_seed(0       )

									snake_case__          =						self.dummy_model()
									snake_case__          =						self.dummy_sample_deter.to(__UpperCamelCase       ) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
												snake_case__          =						scheduler.scale_model_input(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						model(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						scheduler.step(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   generator=__UpperCamelCase       )
												snake_case__          =						output.prev_sample

									snake_case__          =						torch.sum(torch.abs(__UpperCamelCase       )       )
									snake_case__          =						torch.mean(torch.abs(__UpperCamelCase       )       )

									assert abs(result_sum.item() - 1_38_49.38_18       ) < 1E-1
									assert abs(result_mean.item() - 18.03_31       ) < 1E-3





'''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    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				@register_to_config
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    )      ->			Tuple:
							super().__init__()

							__UpperCamelCase :     Optional[int]										=  nn.Linear(_UpperCAmelCase					,    _UpperCAmelCase					,    bias=_UpperCAmelCase     )

							__UpperCamelCase :     str										=  nn.Embedding(_UpperCAmelCase					,    _UpperCAmelCase     )
							__UpperCamelCase :     Any										=  False

							__UpperCamelCase :     Union[str, Any]										=  nn.Dropout(p=_UpperCAmelCase     )

							__UpperCamelCase :     Dict										=  TaConfig(
							    d_model=_UpperCAmelCase					,    num_heads=_UpperCAmelCase					,    d_kv=_UpperCAmelCase					,    d_ff=_UpperCAmelCase					,    feed_forward_proj=_UpperCAmelCase					,    dropout_rate=_UpperCAmelCase					,    is_decoder=_UpperCAmelCase					,    is_encoder_decoder=_UpperCAmelCase					,    )
							__UpperCamelCase :     Tuple										=  nn.ModuleList()
							for lyr_num in range(_UpperCAmelCase     ):
										__UpperCamelCase :     List[str]										=  TaBlock(_UpperCAmelCase     )
										self.encoders.append(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  TaLayerNorm(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  nn.Dropout(p=_UpperCAmelCase     )





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Any										=  self.input_proj(_UpperCAmelCase     )

							# terminal relative positional encodings
							__UpperCamelCase :     Optional[Any]										=  encoder_inputs.shape[1]
							__UpperCamelCase :     Optional[int]										=  torch.arange(_UpperCAmelCase					,    device=encoder_inputs.device     )

							__UpperCamelCase :     Tuple										=  encoder_inputs_mask.sum(-1     )
							__UpperCamelCase :     Optional[Any]										=  torch.roll(input_positions.unsqueeze(0     )					,    tuple(seq_lens.tolist()     )					,    dims=0     )
							x += self.position_encoding(_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  self.dropout_pre(_UpperCAmelCase     )

							# inverted the attention mask
							__UpperCamelCase :     Union[str, Any]										=  encoder_inputs.size()
							__UpperCamelCase :     Optional[Any]										=  self.get_extended_attention_mask(_UpperCAmelCase					,    _UpperCAmelCase     )

							for lyr in self.encoders:
										__UpperCamelCase :     List[Any]										=  lyr(_UpperCAmelCase					,    _UpperCAmelCase     )[0]
							__UpperCamelCase :     Union[str, Any]										=  self.layer_norm(_UpperCAmelCase     )

							return self.dropout_post(_UpperCAmelCase     ), encoder_inputs_mask



