






"""simple docstring"""



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =(DPMSolverSinglestepScheduler,)
   SCREAMING_SNAKE_CASE_    =(('''num_inference_steps''', 25),)
   def       __a							(		self      :	Any							,			**snake_case__      :	str							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							{
            "num_train_timesteps": 1_0_0_0,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "solver_order": 2,
            "prediction_type": "epsilon",
            "thresholding": False,
            "sample_max_value": 1.0,
            "algorithm_type": "dpmsolver++",
            "solver_type": "midpoint",
            "lambda_min_clipped": -float("inf"							),
            "variance_type": None,
        }

        config.update(**snake_case__							)
        return config
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	Union[str, Any]=0							,			**snake_case__      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							dict(self.forward_default_kwargs							)
        UpperCAmelCase__						:    Optional[Any]							=							kwargs.pop("num_inference_steps"							,			snake_case__							)
        UpperCAmelCase__						:    Dict							=							self.dummy_sample
        UpperCAmelCase__						:    Tuple							=							0.1 * sample
        UpperCAmelCase__						:    Dict							=							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
             UpperCAmelCase__						:    Dict							=							self.get_scheduler_config(**snake_case__							)
             UpperCAmelCase__						:    Dict							=							scheduler_class(**snake_case__							)
             scheduler.set_timesteps(snake_case__							)
             # copy over dummy past residuals
             UpperCAmelCase__						:    Dict							=							dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(snake_case__							)
                  UpperCAmelCase__						:    Optional[int]							=							scheduler_class.from_pretrained(snake_case__							)
                  new_scheduler.set_timesteps(snake_case__							)
                  # copy over dummy past residuals
                  UpperCAmelCase__						:    Dict							=							dummy_past_residuals[: new_scheduler.config.solver_order]

             UpperCAmelCase__							,				UpperCAmelCase__						:    Any							=							sample, sample
             for t in range(snake_case__							,			time_step + scheduler.config.solver_order + 1							):
                  UpperCAmelCase__						:    str							=							scheduler.step(snake_case__							,			snake_case__							,			snake_case__							,			**snake_case__							).prev_sample
                  UpperCAmelCase__						:    Dict							=							new_scheduler.step(snake_case__							,			snake_case__							,			snake_case__							,			**snake_case__							).prev_sample

                  assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        pass
   def       __a							(		self      :	str							,			snake_case__      :	List[str]=0							,			**snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							dict(self.forward_default_kwargs							)
        UpperCAmelCase__						:    str							=							kwargs.pop("num_inference_steps"							,			snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							self.dummy_sample
        UpperCAmelCase__						:    Optional[Any]							=							0.1 * sample
        UpperCAmelCase__						:    Union[str, Any]							=							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
             UpperCAmelCase__						:    Tuple							=							self.get_scheduler_config()
             UpperCAmelCase__						:    int							=							scheduler_class(**snake_case__							)
             scheduler.set_timesteps(snake_case__							)

             # copy over dummy past residuals (must be after setting timesteps)
             UpperCAmelCase__						:    Union[str, Any]							=							dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(snake_case__							)
                  UpperCAmelCase__						:    Tuple							=							scheduler_class.from_pretrained(snake_case__							)
                  # copy over dummy past residuals
                  new_scheduler.set_timesteps(snake_case__							)

                  # copy over dummy past residual (must be after setting timesteps)
                  UpperCAmelCase__						:    Tuple							=							dummy_past_residuals[: new_scheduler.config.solver_order]

             UpperCAmelCase__						:    int							=							scheduler.step(snake_case__							,			snake_case__							,			snake_case__							,			**snake_case__							).prev_sample
             UpperCAmelCase__						:    Dict							=							new_scheduler.step(snake_case__							,			snake_case__							,			snake_case__							,			**snake_case__							).prev_sample

             assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	Tuple=None							,			**snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        if scheduler is None:
             UpperCAmelCase__						:    Union[str, Any]							=							self.scheduler_classes[0]
             UpperCAmelCase__						:    Optional[Any]							=							self.get_scheduler_config(**snake_case__							)
             UpperCAmelCase__						:    str							=							scheduler_class(**snake_case__							)

        UpperCAmelCase__						:    Tuple							=							self.scheduler_classes[0]
        UpperCAmelCase__						:    List[Any]							=							self.get_scheduler_config(**snake_case__							)
        UpperCAmelCase__						:    List[Any]							=							scheduler_class(**snake_case__							)

        UpperCAmelCase__						:    List[str]							=							1_0
        UpperCAmelCase__						:    Tuple							=							self.dummy_model()
        UpperCAmelCase__						:    Optional[int]							=							self.dummy_sample_deter
        scheduler.set_timesteps(snake_case__							)

        for i, t in enumerate(scheduler.timesteps							):
             UpperCAmelCase__						:    Tuple							=							model(snake_case__							,			snake_case__							)
             UpperCAmelCase__						:    Optional[Any]							=							scheduler.step(snake_case__							,			snake_case__							,			snake_case__							).prev_sample

        return sample
   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
        UpperCAmelCase__						:    Dict							=							5_0
        UpperCAmelCase__						:    List[Any]							=							self.dummy_model()
        UpperCAmelCase__						:    Dict							=							self.dummy_sample_deter
        scheduler.set_timesteps(snake_case__							)

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:]							):
             UpperCAmelCase__						:    Tuple							=							model(snake_case__							,			snake_case__							)
             UpperCAmelCase__						:    Union[str, Any]							=							scheduler.step(snake_case__							,			snake_case__							,			snake_case__							).prev_sample

        UpperCAmelCase__						:    Union[str, Any]							=							torch.mean(torch.abs(snake_case__							)							)

        assert abs(result_mean.item() - 0.2574							) < 1e-3
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=snake_case__							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        UpperCAmelCase__						:    List[Any]							=							DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
        UpperCAmelCase__						:    Union[str, Any]							=							self.full_loop(scheduler=snake_case__							)
        UpperCAmelCase__						:    Dict							=							torch.mean(torch.abs(snake_case__							)							)

        assert abs(result_mean.item() - 0.2791							) < 1e-3

        UpperCAmelCase__						:    int							=							DEISMultistepScheduler.from_config(scheduler.config							)
        UpperCAmelCase__						:    List[Any]							=							DPMSolverMultistepScheduler.from_config(scheduler.config							)
        UpperCAmelCase__						:    Tuple							=							UniPCMultistepScheduler.from_config(scheduler.config							)
        UpperCAmelCase__						:    List[str]							=							DPMSolverSinglestepScheduler.from_config(scheduler.config							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.full_loop(scheduler=snake_case__							)
        UpperCAmelCase__						:    List[Any]							=							torch.mean(torch.abs(snake_case__							)							)

        assert abs(result_mean.item() - 0.2791							) < 1e-3
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        self.check_over_configs(thresholding=snake_case__							)
        for order in [1, 2, 3]:
             for solver_type in ["midpoint", "heun"]:
                  for threshold in [0.5, 1.0, 2.0]:
                       for prediction_type in ["epsilon", "sample"]:
                            self.check_over_configs(
                                thresholding=snake_case__							,			prediction_type=snake_case__							,			sample_max_value=snake_case__							,			algorithm_type="dpmsolver++"							,			solver_order=snake_case__							,			solver_type=snake_case__							,			)
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=snake_case__							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
             for solver_type in ["midpoint", "heun"]:
                  for order in [1, 2, 3]:
                       for prediction_type in ["epsilon", "sample"]:
                            self.check_over_configs(
                                solver_order=snake_case__							,			solver_type=snake_case__							,			prediction_type=snake_case__							,			algorithm_type=snake_case__							,			)
                            UpperCAmelCase__						:    Tuple							=							self.full_loop(
                                solver_order=snake_case__							,			solver_type=snake_case__							,			prediction_type=snake_case__							,			algorithm_type=snake_case__							,			)
                            assert not torch.isnan(snake_case__							).any(), "Samples have nan numbers"
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        self.check_over_configs(lower_order_final=snake_case__							)
        self.check_over_configs(lower_order_final=snake_case__							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        self.check_over_configs(lambda_min_clipped=-float("inf"							)							)
        self.check_over_configs(lambda_min_clipped=-5.1							)
   def       __a							(		self      :	int							):


        '''simple docstring'''
        self.check_over_configs(variance_type=snake_case__							)
        self.check_over_configs(variance_type="learned_range"							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
             self.check_over_forward(num_inference_steps=snake_case__							,			time_step=0							)
   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							self.full_loop()
        UpperCAmelCase__						:    Tuple							=							torch.mean(torch.abs(snake_case__							)							)

        assert abs(result_mean.item() - 0.2791							) < 1e-3
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							self.full_loop(use_karras_sigmas=snake_case__							)
        UpperCAmelCase__						:    List[Any]							=							torch.mean(torch.abs(snake_case__							)							)

        assert abs(result_mean.item() - 0.2248							) < 1e-3
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.full_loop(prediction_type="v_prediction"							)
        UpperCAmelCase__						:    Any							=							torch.mean(torch.abs(snake_case__							)							)

        assert abs(result_mean.item() - 0.1453							) < 1e-3
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.full_loop(prediction_type="v_prediction"							,			use_karras_sigmas=snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							torch.mean(torch.abs(snake_case__							)							)

        assert abs(result_mean.item() - 0.0649							) < 1e-3




   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							self.scheduler_classes[0]
        UpperCAmelCase__						:    List[Any]							=							self.get_scheduler_config(thresholding=snake_case__							,			dynamic_thresholding_ratio=0							)
        UpperCAmelCase__						:    Tuple							=							scheduler_class(**snake_case__							)

        UpperCAmelCase__						:    Dict							=							1_0
        UpperCAmelCase__						:    Optional[Any]							=							self.dummy_model()
        UpperCAmelCase__						:    Any							=							self.dummy_sample_deter.half()
        scheduler.set_timesteps(snake_case__							)

        for i, t in enumerate(scheduler.timesteps							):
             UpperCAmelCase__						:    List[Any]							=							model(snake_case__							,			snake_case__							)
             UpperCAmelCase__						:    int							=							scheduler.step(snake_case__							,			snake_case__							,			snake_case__							).prev_sample

        assert sample.dtype == torch.floataa










"""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 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	float							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	str							,			snake_case__      :	bool = False							,			):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Optional[int]							=							nn.Embedding(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							nn.Embedding(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							False

        UpperCAmelCase__						:    Optional[int]							=							nn.Dropout(p=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							TaConfig(
            vocab_size=snake_case__							,			d_model=snake_case__							,			num_heads=snake_case__							,			d_kv=snake_case__							,			d_ff=snake_case__							,			dropout_rate=snake_case__							,			feed_forward_proj=snake_case__							,			is_decoder=snake_case__							,			is_encoder_decoder=snake_case__							,			)

        UpperCAmelCase__						:    Tuple							=							nn.ModuleList()
        for lyr_num in range(snake_case__							):
             UpperCAmelCase__						:    Tuple							=							TaBlock(snake_case__							)
             self.encoders.append(snake_case__							)

        UpperCAmelCase__						:    str							=							TaLayerNorm(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							nn.Dropout(p=snake_case__							)




   def       __a							(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.token_embedder(snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							encoder_input_tokens.shape[1]
        UpperCAmelCase__						:    List[Any]							=							torch.arange(snake_case__							,			device=encoder_input_tokens.device							)
        x += self.position_encoding(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.dropout_pre(snake_case__							)

        # inverted the attention mask
        UpperCAmelCase__						:    List[Any]							=							encoder_input_tokens.size()
        UpperCAmelCase__						:    Tuple							=							self.get_extended_attention_mask(snake_case__							,			snake_case__							)

        for lyr in self.encoders:
             UpperCAmelCase__						:    Any							=							lyr(snake_case__							,			snake_case__							)[0]
        UpperCAmelCase__						:    Any							=							self.layer_norm(snake_case__							)

        return self.dropout_post(snake_case__							), encoder_inputs_mask


