'''simple docstring'''






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   (DEISMultistepScheduler,)
   __lowerCAmelCase         =   (("num_inference_steps", 2_5),)
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], **UpperCamelCase__      :			Optional[int]     )  ->			str:
        _A    		=  {
            'num_train_timesteps': 10_00,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'solver_order': 2,
        }

        config.update(**UpperCamelCase__     )
        return config
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Union[str, Any]=0, **UpperCamelCase__      :			Dict     )  ->			List[Any]:
        _A    		=  dict(self.forward_default_kwargs     )
        _A    		=  kwargs.pop('num_inference_steps', UpperCamelCase__     )
        _A    		=  self.dummy_sample
        _A    		=  0.1 * sample
        _A    		=  [residual + 0.2, residual + 0.15, residual + 0.10]

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(UpperCamelCase__     )
                  _A    		=  scheduler_class.from_pretrained(UpperCamelCase__     )
                  new_scheduler.set_timesteps(UpperCamelCase__     )
                  # copy over dummy past residuals
                  _A    		=  dummy_past_residuals[: new_scheduler.config.solver_order]

             _A		,       _A    		=  sample, sample
             for t in range(UpperCamelCase__, time_step + scheduler.config.solver_order + 1     ):
                  _A    		=  scheduler.step(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     ).prev_sample
                  _A    		=  new_scheduler.step(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     ).prev_sample

                  assert torch.sum(torch.abs(output - new_output     )     ) < 1e-5, "Scheduler outputs are not identical"
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			List[Any]:
        pass
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Optional[int]=0, **UpperCamelCase__      :			int     )  ->			Tuple:
        _A    		=  dict(self.forward_default_kwargs     )
        _A    		=  kwargs.pop('num_inference_steps', UpperCamelCase__     )
        _A    		=  self.dummy_sample
        _A    		=  0.1 * sample
        _A    		=  [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
             _A    		=  self.get_scheduler_config()
             _A    		=  scheduler_class(**UpperCamelCase__     )
             scheduler.set_timesteps(UpperCamelCase__     )

             # copy over dummy past residuals (must be after setting timesteps)
             _A    		=  dummy_past_residuals[: scheduler.config.solver_order]

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

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

             _A    		=  scheduler.step(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     ).prev_sample
             _A    		=  new_scheduler.step(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     ).prev_sample

             assert torch.sum(torch.abs(output - new_output     )     ) < 1e-5, "Scheduler outputs are not identical"
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			Optional[int]=None, **UpperCamelCase__      :			Union[str, Any]     )  ->			Tuple:
        if scheduler is None:
             _A    		=  self.scheduler_classes[0]
             _A    		=  self.get_scheduler_config(**UpperCamelCase__     )
             _A    		=  scheduler_class(**UpperCamelCase__     )

        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config(**UpperCamelCase__     )
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A    		=  10
        _A    		=  self.dummy_model()
        _A    		=  self.dummy_sample_deter
        scheduler.set_timesteps(UpperCamelCase__     )

        for i, t in enumerate(scheduler.timesteps     ):
             _A    		=  model(UpperCamelCase__, UpperCamelCase__     )
             _A    		=  scheduler.step(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     ).prev_sample

        return sample
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Dict:
        _A    		=  dict(self.forward_default_kwargs     )

        _A    		=  kwargs.pop('num_inference_steps', UpperCamelCase__     )

        for scheduler_class in self.scheduler_classes:
             _A    		=  self.get_scheduler_config()
             _A    		=  scheduler_class(**UpperCamelCase__     )

             _A    		=  self.dummy_sample
             _A    		=  0.1 * sample

             if num_inference_steps is not None and hasattr(UpperCamelCase__, 'set_timesteps'     ):
                  scheduler.set_timesteps(UpperCamelCase__     )
             elif num_inference_steps is not None and not hasattr(UpperCamelCase__, 'set_timesteps'     ):
                  _A    		=  num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             _A    		=  [residual + 0.2, residual + 0.15, residual + 0.10]
             _A    		=  dummy_past_residuals[: scheduler.config.solver_order]

             _A    		=  scheduler.timesteps[5]
             _A    		=  scheduler.timesteps[6]

             _A    		=  scheduler.step(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     ).prev_sample
             _A    		=  scheduler.step(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     ).prev_sample

             self.assertEqual(output_a.shape, sample.shape     )
             self.assertEqual(output_a.shape, output_a.shape     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			List[str]:
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        _A    		=  DEISMultistepScheduler(**self.get_scheduler_config()     )
        _A    		=  self.full_loop(scheduler=UpperCamelCase__     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_mean.item() - 0.23_916     ) < 1e-3

        _A    		=  DPMSolverSinglestepScheduler.from_config(scheduler.config     )
        _A    		=  DPMSolverMultistepScheduler.from_config(scheduler.config     )
        _A    		=  UniPCMultistepScheduler.from_config(scheduler.config     )
        _A    		=  DEISMultistepScheduler.from_config(scheduler.config     )

        _A    		=  self.full_loop(scheduler=UpperCamelCase__     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_mean.item() - 0.23_916     ) < 1e-3
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			List[Any]:
        for timesteps in [25, 50, 1_00, 9_99, 10_00]:
             self.check_over_configs(num_train_timesteps=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Dict:
        self.check_over_configs(thresholding=UpperCamelCase__     )
        for order in [1, 2, 3]:
             for solver_type in ["logrho"]:
                  for threshold in [0.5, 1.0, 2.0]:
                       for prediction_type in ["epsilon", "sample"]:
                            self.check_over_configs(
                                thresholding=UpperCamelCase__, prediction_type=UpperCamelCase__, sample_max_value=UpperCamelCase__, algorithm_type='deis', solver_order=UpperCamelCase__, solver_type=UpperCamelCase__, )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Dict:
        for algorithm_type in ["deis"]:
             for solver_type in ["logrho"]:
                  for order in [1, 2, 3]:
                       for prediction_type in ["epsilon", "sample"]:
                            self.check_over_configs(
                                solver_order=UpperCamelCase__, solver_type=UpperCamelCase__, prediction_type=UpperCamelCase__, algorithm_type=UpperCamelCase__, )
                            _A    		=  self.full_loop(
                                solver_order=UpperCamelCase__, solver_type=UpperCamelCase__, prediction_type=UpperCamelCase__, algorithm_type=UpperCamelCase__, )
                            assert not torch.isnan(UpperCamelCase__     ).any(), "Samples have nan numbers"
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[Any]:
        self.check_over_configs(lower_order_final=UpperCamelCase__     )
        self.check_over_configs(lower_order_final=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Dict:
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
             self.check_over_forward(num_inference_steps=UpperCamelCase__, time_step=0     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			List[Any]:
        _A    		=  self.full_loop()
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_mean.item() - 0.23_916     ) < 1e-3
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        _A    		=  self.full_loop(prediction_type='v_prediction'     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_mean.item() - 0.091     ) < 1e-3

   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			int:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config(thresholding=UpperCamelCase__, dynamic_thresholding_ratio=0     )
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A    		=  10
        _A    		=  self.dummy_model()
        _A    		=  self.dummy_sample_deter.half()
        scheduler.set_timesteps(UpperCamelCase__     )

        for i, t in enumerate(scheduler.timesteps     ):
             _A    		=  model(UpperCamelCase__, UpperCamelCase__     )
             _A    		=  scheduler.step(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     ).prev_sample

        assert sample.dtype == torch.floataa











"""simple docstring"""



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


_lowerCAmelCase	:	Optional[int]				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Tuple				=      {
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''mra'''
   def __init__(		self      :	Any							,			snake_case__      :	List[str]=5_0_2_6_5							,			snake_case__      :	Any=7_6_8							,			snake_case__      :	Union[str, Any]=1_2							,			snake_case__      :	Optional[Any]=1_2							,			snake_case__      :	Tuple=3_0_7_2							,			snake_case__      :	str="gelu"							,			snake_case__      :	Any=0.1							,			snake_case__      :	Tuple=0.1							,			snake_case__      :	Tuple=5_1_2							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	List[Any]=0.02							,			snake_case__      :	str=1e-5							,			snake_case__      :	List[Any]="absolute"							,			snake_case__      :	str=4							,			snake_case__      :	List[str]="full"							,			snake_case__      :	Tuple=0							,			snake_case__      :	Any=0							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	int=0							,			snake_case__      :	int=2							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			bos_token_id=snake_case__							,			eos_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							vocab_size
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    Any							=							hidden_size
        UpperCAmelCase__						:    Union[str, Any]							=							num_hidden_layers
        UpperCAmelCase__						:    str							=							num_attention_heads
        UpperCAmelCase__						:    int							=							intermediate_size
        UpperCAmelCase__						:    int							=							hidden_act
        UpperCAmelCase__						:    List[str]							=							hidden_dropout_prob
        UpperCAmelCase__						:    List[str]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Any							=							initializer_range
        UpperCAmelCase__						:    Any							=							type_vocab_size
        UpperCAmelCase__						:    Dict							=							layer_norm_eps
        UpperCAmelCase__						:    Tuple							=							position_embedding_type
        UpperCAmelCase__						:    List[str]							=							block_per_row
        UpperCAmelCase__						:    Optional[Any]							=							approx_mode
        UpperCAmelCase__						:    Any							=							initial_prior_first_n_blocks
        UpperCAmelCase__						:    List[Any]							=							initial_prior_diagonal_n_blocks


