






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    lowercase_    (nn.Module     ):






      def __init__(    self     ,     a_ = 1_6     ,     a_ = 8_8     ,     a_ = None     ,     a_ = 1     ,     a_ = 0.0     ,     a_ = 3_2     ,     a_ = None     ,     a_ = False     ,     a_ = None     ,     a_ = None     ,     a_ = "geglu"     ,     a_ = None     ,     )	->str:







        '''simple docstring'''


        super().__init__()
        _a							=	nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=_a     ,     attention_head_dim=_a     ,     in_channels=_a     ,     num_layers=_a     ,     dropout=_a     ,     norm_num_groups=_a     ,     cross_attention_dim=_a     ,     attention_bias=_a     ,     sample_size=_a     ,     num_vector_embeds=_a     ,     activation_fn=_a     ,     num_embeds_ada_norm=_a     ,     )
                for _ in range(2	)
            ]	)

        # Variables that can be set by a pipeline:

        # The ratio of transformer1 to transformer2's output states to be combined during inference
        _a							=	0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        _a							=	[7_7, 2_5_7]

        # Which transformer to use to encode which condition.
        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
        _a							=	[1, 0]






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_=None     ,     a_=None     ,     a_=None     ,     a_ = True     ,     )	->List[Any]:







        '''simple docstring'''


        _a							=	hidden_states

        _a							=	[]
        _a							=	0
        # attention_mask is not used yet
        for i in range(2	):
          # for each of the two transformers, pass the corresponding condition tokens
          _a							=	encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
          _a							=	self.transformer_index_for_condition[i]
          _a							=	self.transformers[transformer_index](
              _a     ,     encoder_hidden_states=_a     ,     timestep=_a     ,     cross_attention_kwargs=_a     ,     return_dict=_a     ,     )[0]
          encoded_states.append(encoded_state - input_states	)
          tokens_start += self.condition_lengths[i]

        _a							=	encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        _a							=	output_states + input_states

        if not return_dict:
          return (output_states,)

        return TransformeraDModelOutput(sample=_a	)










"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Tuple       			=				(KDPMaDiscreteScheduler,)
      A__	:		Tuple       			=				10






      def 			lowerCamelCase__    (    self     ,     **a_	)	->List[str]:







        '''simple docstring'''


        _a							=	{
            "num_train_timesteps": 1_1_0_0,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**a_	)
        return config






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


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






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







        '''simple docstring'''


        for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,     [0.0_002, 0.002, 0.02]	):
          self.check_over_configs(beta_start=a_     ,     beta_end=a_	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


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






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


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






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







        '''simple docstring'''


        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(prediction_type="v_prediction"	)
        _a							=	scheduler_class(**a_	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter * scheduler.init_noise_sigma
        _a							=	sample.to(a_	)

        for i, t in enumerate(scheduler.timesteps	):
          _a							=	scheduler.scale_model_input(a_     ,     a_	)

          _a							=	model(a_     ,     a_	)

          _a							=	scheduler.step(a_     ,     a_     ,     a_	)
          _a							=	output.prev_sample

        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6_934E-07	) < 1E-2
          assert abs(result_mean.item() - 6.1_112E-10	) < 1E-3
        else:
          # CUDA
          assert abs(result_sum.item() - 4.693_428_650_170_972E-07	) < 1E-2
          assert abs(result_mean.item() - 0.0_002	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


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

        scheduler.set_timesteps(self.num_inference_steps	)

        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter * scheduler.init_noise_sigma
        _a							=	sample.to(a_	)

        for i, t in enumerate(scheduler.timesteps	):
          _a							=	scheduler.scale_model_input(a_     ,     a_	)

          _a							=	model(a_     ,     a_	)

          _a							=	scheduler.step(a_     ,     a_     ,     a_	)
          _a							=	output.prev_sample

        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 20.4_125	) < 1E-2
          assert abs(result_mean.item() - 0.0_266	) < 1E-3
        else:
          # CUDA
          assert abs(result_sum.item() - 20.4_125	) < 1E-2
          assert abs(result_mean.item() - 0.0_266	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


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

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

        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter.to(a_	) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
          _a							=	scheduler.scale_model_input(a_     ,     a_	)

          _a							=	model(a_     ,     a_	)

          _a							=	scheduler.step(a_     ,     a_     ,     a_	)
          _a							=	output.prev_sample

        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        if str(a_	).startswith("cpu"	):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 20.4_125	) < 1E-2
          assert abs(result_mean.item() - 0.0_266	) < 1E-3
        else:
          # CUDA
          assert abs(result_sum.item() - 20.4_125	) < 1E-2
          assert abs(result_mean.item() - 0.0_266	) < 1E-3


