






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class       __a  (   A__	):
 _lowerCAmelCase	:	int				=			(UniPCMultistepScheduler,)
 _lowerCAmelCase	:	List[str]				=			(('''num_inference_steps''', 2_5),)







 def  __lowercase (		self :       Optional[Any]							,       **SCREAMING_SNAKE_CASE :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  {
          "num_train_timesteps": 10_00,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
          "solver_order": 2,
          "solver_type": "bh2",
      }

      config.update(**SCREAMING_SNAKE_CASE						)
      return config







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       List[str]=0							,       **SCREAMING_SNAKE_CASE :       Dict						):


      '''simple docstring'''






      UpperCamelCase__      :       Union[str, Any]	    =  dict(self.forward_default_kwargs						)
      UpperCamelCase__      :       List[Any]	    =  kwargs.pop("num_inference_steps"							,       SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Any	    =  self.dummy_sample
      UpperCamelCase__      :       str	    =  0.1 * sample
      UpperCamelCase__      :       Any	    =  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__      :       Tuple	    =  self.get_scheduler_config(**SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       Union[str, Any]	    =  scheduler_class(**SCREAMING_SNAKE_CASE						)
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
           # copy over dummy past residuals
           UpperCamelCase__      :       List[str]	    =  dummy_past_residuals[: scheduler.config.solver_order]

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

           UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  sample, sample
           for t in range(SCREAMING_SNAKE_CASE							,       time_step + scheduler.config.solver_order + 1						):
                UpperCamelCase__      :       Optional[Any]	    =  scheduler.step(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						).prev_sample
                UpperCamelCase__      :       str	    =  new_scheduler.step(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						).prev_sample

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







 def  __lowercase (		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       List[Any]=0							,       **SCREAMING_SNAKE_CASE :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  dict(self.forward_default_kwargs						)
      UpperCamelCase__      :       Tuple	    =  kwargs.pop("num_inference_steps"							,       SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Tuple	    =  self.dummy_sample
      UpperCamelCase__      :       Optional[Any]	    =  0.1 * sample
      UpperCamelCase__      :       Dict	    =  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__      :       Any	    =  self.get_scheduler_config()
           UpperCamelCase__      :       Tuple	    =  scheduler_class(**SCREAMING_SNAKE_CASE						)
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

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

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

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

           UpperCamelCase__      :       Any	    =  scheduler.step(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						).prev_sample
           UpperCamelCase__      :       Optional[Any]	    =  new_scheduler.step(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						).prev_sample

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







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=None							,       **SCREAMING_SNAKE_CASE :       Union[str, Any]						):


      '''simple docstring'''






      if scheduler is None:
           UpperCamelCase__      :       Optional[Any]	    =  self.scheduler_classes[0]
           UpperCamelCase__      :       Union[str, Any]	    =  self.get_scheduler_config(**SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       Dict	    =  scheduler_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[int]	    =  self.scheduler_classes[0]
      UpperCamelCase__      :       Optional[int]	    =  self.get_scheduler_config(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Dict	    =  scheduler_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       str	    =  10
      UpperCamelCase__      :       Optional[Any]	    =  self.dummy_model()
      UpperCamelCase__      :       Tuple	    =  self.dummy_sample_deter
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

      for i, t in enumerate(scheduler.timesteps						):
           UpperCamelCase__      :       List[str]	    =  model(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       int	    =  scheduler.step(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						).prev_sample

      return sample







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Union[str, Any]	    =  dict(self.forward_default_kwargs						)

      UpperCamelCase__      :       Any	    =  kwargs.pop("num_inference_steps"							,       SCREAMING_SNAKE_CASE						)

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__      :       str	    =  self.get_scheduler_config()
           UpperCamelCase__      :       Dict	    =  scheduler_class(**SCREAMING_SNAKE_CASE						)

           UpperCamelCase__      :       List[Any]	    =  self.dummy_sample
           UpperCamelCase__      :       int	    =  0.1 * sample

           if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE							,       "set_timesteps"						):
                scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
           elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE							,       "set_timesteps"						):
                UpperCamelCase__      :       Optional[Any]	    =  num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           UpperCamelCase__      :       List[Any]	    =  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
           UpperCamelCase__      :       Dict	    =  dummy_past_residuals[: scheduler.config.solver_order]

           UpperCamelCase__      :       Tuple	    =  scheduler.timesteps[5]
           UpperCamelCase__      :       str	    =  scheduler.timesteps[6]

           UpperCamelCase__      :       str	    =  scheduler.step(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						).prev_sample
           UpperCamelCase__      :       Any	    =  scheduler.step(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						).prev_sample

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







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  UniPCMultistepScheduler(**self.get_scheduler_config()						)
      UpperCamelCase__      :       List[str]	    =  self.full_loop(scheduler=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Union[str, Any]	    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_mean.item() - 0.2_4_6_4						) < 1e-3

      UpperCamelCase__      :       Dict	    =  DPMSolverSinglestepScheduler.from_config(scheduler.config						)
      UpperCamelCase__      :       Optional[int]	    =  DEISMultistepScheduler.from_config(scheduler.config						)
      UpperCamelCase__      :       Any	    =  DPMSolverMultistepScheduler.from_config(scheduler.config						)
      UpperCamelCase__      :       Union[str, Any]	    =  UniPCMultistepScheduler.from_config(scheduler.config						)

      UpperCamelCase__      :       str	    =  self.full_loop(scheduler=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Dict	    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_mean.item() - 0.2_4_6_4						) < 1e-3







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE						)
      for order in [1, 2, 3]:
           for solver_type in ["bh1", "bh2"]:
                for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              thresholding=SCREAMING_SNAKE_CASE							,       prediction_type=SCREAMING_SNAKE_CASE							,       sample_max_value=SCREAMING_SNAKE_CASE							,       solver_order=SCREAMING_SNAKE_CASE							,       solver_type=SCREAMING_SNAKE_CASE							,       )







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






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







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      for solver_type in ["bh1", "bh2"]:
           for order in [1, 2, 3]:
                for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=SCREAMING_SNAKE_CASE							,       solver_type=SCREAMING_SNAKE_CASE							,       prediction_type=SCREAMING_SNAKE_CASE							,       )
                     UpperCamelCase__      :       Tuple	    =  self.full_loop(
                         solver_order=SCREAMING_SNAKE_CASE							,       solver_type=SCREAMING_SNAKE_CASE							,       prediction_type=SCREAMING_SNAKE_CASE							,       )
                     assert not torch.isnan(SCREAMING_SNAKE_CASE						).any(), "Samples have nan numbers"







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE						)
      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
           self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE							,       time_step=0						)







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.full_loop()
      UpperCamelCase__      :       List[Any]	    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_mean.item() - 0.2_4_6_4						) < 1e-3







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  self.full_loop(prediction_type="v_prediction"						)
      UpperCamelCase__      :       Optional[int]	    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_mean.item() - 0.1_0_1_4						) < 1e-3







 def  __lowercase (		self :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  self.scheduler_classes[0]
      UpperCamelCase__      :       Tuple	    =  self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE							,       dynamic_thresholding_ratio=0						)
      UpperCamelCase__      :       Dict	    =  scheduler_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[Any]	    =  10
      UpperCamelCase__      :       List[Any]	    =  self.dummy_model()
      UpperCamelCase__      :       Optional[Any]	    =  self.dummy_sample_deter.half()
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

      for i, t in enumerate(scheduler.timesteps						):
           UpperCamelCase__      :       Union[str, Any]	    =  model(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       str	    =  scheduler.step(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						).prev_sample

      assert sample.dtype == torch.floataa







 def  __lowercase (		self :       List[str]							,       **SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__      :       Tuple	    =  self.get_scheduler_config(**SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       Union[str, Any]	    =  scheduler_class(**SCREAMING_SNAKE_CASE						)

           scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
           assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps







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


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

lowerCamelCase     :							Optional[Any]    ={
    '''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       __a  (   A__	):
 _lowerCAmelCase	:	str				=			'''decision_transformer'''
 _lowerCAmelCase	:	Optional[Any]				=			['''past_key_values''']
 _lowerCAmelCase	:	Optional[int]				=			{
     '''max_position_embeddings''': '''n_positions''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }







 def __init__(		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[int]=17							,       SCREAMING_SNAKE_CASE :       Optional[int]=4							,       SCREAMING_SNAKE_CASE :       Optional[int]=1_28							,       SCREAMING_SNAKE_CASE :       Dict=40_96							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=True							,       SCREAMING_SNAKE_CASE :       Tuple=1							,       SCREAMING_SNAKE_CASE :       Optional[int]=10_24							,       SCREAMING_SNAKE_CASE :       Any=3							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       SCREAMING_SNAKE_CASE :       Dict="relu"							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=1e-5							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.0_2							,       SCREAMING_SNAKE_CASE :       Optional[Any]=True							,       SCREAMING_SNAKE_CASE :       Tuple=True							,       SCREAMING_SNAKE_CASE :       int=5_02_56							,       SCREAMING_SNAKE_CASE :       List[Any]=5_02_56							,       SCREAMING_SNAKE_CASE :       Any=False							,       SCREAMING_SNAKE_CASE :       List[Any]=False							,       **SCREAMING_SNAKE_CASE :       Optional[int]							,       ):


      '''simple docstring'''






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

      UpperCamelCase__      :       Optional[Any]	    =  bos_token_id
      UpperCamelCase__      :       Optional[int]	    =  eos_token_id

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