
"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Tuple					=			(DEISMultistepScheduler,)
						_lowerCamelCase:			Optional[Any]					=			(("num_inference_steps", 25),)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,**A_   :					str					)   ->      List[str]:
							A        =      {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							}

							config.update(**__A					)
							return config


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					Dict=0     ,**A_   :					Optional[Any]					)   ->      Dict:
							A        =      dict(self.forward_default_kwargs					)
							A        =      kwargs.pop('num_inference_steps'     ,__A					)
							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					)
								A        =      scheduler_class(**__A					)
								scheduler.set_timesteps(__A					)
								# copy over dummy past residuals
								A        =      dummy_past_residuals[: scheduler.config.solver_order]

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

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

									assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Optional[Any]:
							pass


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					List[Any]=0     ,**A_   :					List[Any]					)   ->      List[Any]:
							A        =      dict(self.forward_default_kwargs					)
							A        =      kwargs.pop('num_inference_steps'     ,__A					)
							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(**__A					)
								scheduler.set_timesteps(__A					)

								# 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(__A					)
									A        =      scheduler_class.from_pretrained(__A					)
									# copy over dummy past residuals
									new_scheduler.set_timesteps(__A					)

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

								A        =      scheduler.step(__A     ,__A     ,__A     ,**__A					).prev_sample
								A        =      new_scheduler.step(__A     ,__A     ,__A     ,**__A					).prev_sample

								assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any     ,A_   :					List[Any]=None     ,**A_   :					List[Any]					)   ->      int:





							if scheduler is None:
								A        =      self.scheduler_classes[0]
								A        =      self.get_scheduler_config(**__A					)
								A        =      scheduler_class(**__A					)

							A        =      self.scheduler_classes[0]
							A        =      self.get_scheduler_config(**__A					)
							A        =      scheduler_class(**__A					)

							A        =      10
							A        =      self.dummy_model()
							A        =      self.dummy_sample_deter
							scheduler.set_timesteps(__A					)

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

							return sample


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      Tuple:
							A        =      dict(self.forward_default_kwargs					)

							A        =      kwargs.pop('num_inference_steps'     ,__A					)

							for scheduler_class in self.scheduler_classes:
								A        =      self.get_scheduler_config()
								A        =      scheduler_class(**__A					)

								A        =      self.dummy_sample
								A        =      0.1 * sample

								if num_inference_steps is not None and hasattr(__A     ,'set_timesteps'					):
									scheduler.set_timesteps(__A					)
								elif num_inference_steps is not None and not hasattr(__A     ,'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(__A     ,__A     ,__A     ,**__A					).prev_sample
								A        =      scheduler.step(__A     ,__A     ,__A     ,**__A					).prev_sample

								self.assertEqual(output_a.shape     ,sample.shape					)
								self.assertEqual(output_a.shape     ,output_a.shape					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Union[str, Any]:
							A        =      DEISMultistepScheduler(**self.get_scheduler_config()					)
							A        =      self.full_loop(scheduler=__A					)
							A        =      torch.mean(torch.abs(__A					)					)

							assert abs(result_mean.item() - 0.2_39_16					) < 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=__A					)
							A        =      torch.mean(torch.abs(__A					)					)

							assert abs(result_mean.item() - 0.2_39_16					) < 1e-3


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      Optional[Any]:





							for timesteps in [25, 50, 100, 999, 1000]:
								self.check_over_configs(num_train_timesteps=__A					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Any:
							self.check_over_configs(thresholding=__A					)
							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=__A     ,prediction_type=__A     ,sample_max_value=__A     ,algorithm_type='deis'     ,solver_order=__A     ,solver_type=__A     ,)


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      List[Any]:





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


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      List[str]:





							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=__A     ,solver_type=__A     ,prediction_type=__A     ,algorithm_type=__A     ,)
											A        =      self.full_loop(
											    solver_order=__A     ,solver_type=__A     ,prediction_type=__A     ,algorithm_type=__A     ,)
											assert not torch.isnan(__A					).any(), "Samples have nan numbers"


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      str:
							self.check_over_configs(lower_order_final=__A					)
							self.check_over_configs(lower_order_final=__A					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Optional[Any]:





							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
								self.check_over_forward(num_inference_steps=__A     ,time_step=0					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      List[str]:
							A        =      self.full_loop()
							A        =      torch.mean(torch.abs(__A					)					)

							assert abs(result_mean.item() - 0.2_39_16					) < 1e-3


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      Tuple:
							A        =      self.full_loop(prediction_type='v_prediction'					)
							A        =      torch.mean(torch.abs(__A					)					)

							assert abs(result_mean.item() - 0.0_91					) < 1e-3




						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      List[Any]:
							A        =      self.scheduler_classes[0]
							A        =      self.get_scheduler_config(thresholding=__A     ,dynamic_thresholding_ratio=0					)
							A        =      scheduler_class(**__A					)

							A        =      10
							A        =      self.dummy_model()
							A        =      self.dummy_sample_deter.half()
							scheduler.set_timesteps(__A					)

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

							assert sample.dtype == torch.floataa



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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      str     		=		"decision_transformer"
   __A :      Union[str, Any]     		=		["past_key_values"]
   __A :      Optional[int]     		=		{
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(		self						,				__A=17						,				__A=4						,				__A=128						,				__A=4096						,				__A=True						,				__A=1						,				__A=1024						,				__A=3						,				__A=1						,				__A=None						,				__A="relu"						,				__A=0.1						,				__A=0.1						,				__A=0.1						,				__A=1e-5						,				__A=0.02						,				__A=True						,				__A=True						,				__A=5_0256						,				__A=5_0256						,				__A=False						,				__A=False						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				state_dim
     lowerCamelCase :					Tuple    =				act_dim
     lowerCamelCase :					List[str]    =				hidden_size
     lowerCamelCase :					Optional[Any]    =				max_ep_len
     lowerCamelCase :					Union[str, Any]    =				action_tanh
     lowerCamelCase :					int    =				vocab_size
     lowerCamelCase :					List[Any]    =				n_positions
     lowerCamelCase :					Dict    =				n_layer
     lowerCamelCase :					int    =				n_head
     lowerCamelCase :					List[Any]    =				n_inner
     lowerCamelCase :					Any    =				activation_function
     lowerCamelCase :					Optional[int]    =				resid_pdrop
     lowerCamelCase :					str    =				embd_pdrop
     lowerCamelCase :					Tuple    =				attn_pdrop
     lowerCamelCase :					List[Any]    =				layer_norm_epsilon
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					Optional[int]    =				scale_attn_weights
     lowerCamelCase :					List[Any]    =				use_cache
     lowerCamelCase :					Tuple    =				scale_attn_by_inverse_layer_idx
     lowerCamelCase :					Optional[int]    =				reorder_and_upcast_attn

     lowerCamelCase :					Dict    =				bos_token_id
     lowerCamelCase :					Any    =				eos_token_id

     super().__init__(bos_token_id=__A						,				eos_token_id=__A						,				**__A )



