

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  (DPMSolverSDEScheduler,)
 _UpperCamelCase			  =  10
 def 	UpperCamelCase__  (							self      ,							**A_					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					{
           '''num_train_timesteps''': 1100,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''noise_sampler_seed''': 0,
       }

       config.update(**A_					)
       return config
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=A_					)
 def 	UpperCamelCase__  (							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 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A_					)
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					self.scheduler_classes[0]
       __lowerCAmelCase					:						str					=					self.get_scheduler_config()
       __lowerCAmelCase					:						Optional[int]					=					scheduler_class(**A_					)

       scheduler.set_timesteps(self.num_inference_steps					)

       __lowerCAmelCase					:						Optional[int]					=					self.dummy_model()
       __lowerCAmelCase					:						int					=					self.dummy_sample_deter * scheduler.init_noise_sigma
       __lowerCAmelCase					:						Optional[Any]					=					sample.to(A_					)

       for i, t in enumerate(scheduler.timesteps					):
             __lowerCAmelCase					:						Tuple					=					scheduler.scale_model_input(A_      ,							A_					)

             __lowerCAmelCase					:						Optional[Any]					=					model(A_      ,							A_					)

             __lowerCAmelCase					:						str					=					scheduler.step(A_      ,							A_      ,							A_					)
             __lowerCAmelCase					:						int					=					output.prev_sample

       __lowerCAmelCase					:						Optional[int]					=					torch.sum(torch.abs(A_					)					)
       __lowerCAmelCase					:						Optional[Any]					=					torch.mean(torch.abs(A_					)					)

       if torch_device in ["mps"]:
             assert abs(result_sum.item() - 167.47_821_044_921_875					) < 1e-2
             assert abs(result_mean.item() - 0.2_178_705_964_565_277					) < 1e-3
       elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 171.59_352_111_816_406					) < 1e-2
             assert abs(result_mean.item() - 0.22_342_906_892_299_652					) < 1e-3
       else:
             assert abs(result_sum.item() - 162.52_383_422_851_562					) < 1e-2
             assert abs(result_mean.item() - 0.211_619_570_851_326					) < 1e-3
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					self.scheduler_classes[0]
       __lowerCAmelCase					:						Tuple					=					self.get_scheduler_config(prediction_type='''v_prediction'''					)
       __lowerCAmelCase					:						Optional[int]					=					scheduler_class(**A_					)

       scheduler.set_timesteps(self.num_inference_steps					)

       __lowerCAmelCase					:						Optional[Any]					=					self.dummy_model()
       __lowerCAmelCase					:						List[Any]					=					self.dummy_sample_deter * scheduler.init_noise_sigma
       __lowerCAmelCase					:						List[str]					=					sample.to(A_					)

       for i, t in enumerate(scheduler.timesteps					):
             __lowerCAmelCase					:						Dict					=					scheduler.scale_model_input(A_      ,							A_					)

             __lowerCAmelCase					:						Optional[int]					=					model(A_      ,							A_					)

             __lowerCAmelCase					:						List[str]					=					scheduler.step(A_      ,							A_      ,							A_					)
             __lowerCAmelCase					:						List[str]					=					output.prev_sample

       __lowerCAmelCase					:						Dict					=					torch.sum(torch.abs(A_					)					)
       __lowerCAmelCase					:						List[Any]					=					torch.mean(torch.abs(A_					)					)

       if torch_device in ["mps"]:
             assert abs(result_sum.item() - 124.77_149_200_439_453					) < 1e-2
             assert abs(result_mean.item() - 0.16_226_289_014_816_284					) < 1e-3
       elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 128.1_663_360_595_703					) < 1e-2
             assert abs(result_mean.item() - 0.16_688_326_001_167_297					) < 1e-3
       else:
             assert abs(result_sum.item() - 119.8_487_548_828_125					) < 1e-2
             assert abs(result_mean.item() - 0.1_560_530_662_536_621					) < 1e-3
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					self.scheduler_classes[0]
       __lowerCAmelCase					:						int					=					self.get_scheduler_config()
       __lowerCAmelCase					:						str					=					scheduler_class(**A_					)

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

       __lowerCAmelCase					:						Union[str, Any]					=					self.dummy_model()
       __lowerCAmelCase					:						Dict					=					self.dummy_sample_deter.to(A_					) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
             __lowerCAmelCase					:						Optional[Any]					=					scheduler.scale_model_input(A_      ,							A_					)

             __lowerCAmelCase					:						int					=					model(A_      ,							A_					)

             __lowerCAmelCase					:						List[Any]					=					scheduler.step(A_      ,							A_      ,							A_					)
             __lowerCAmelCase					:						str					=					output.prev_sample

       __lowerCAmelCase					:						Optional[Any]					=					torch.sum(torch.abs(A_					)					)
       __lowerCAmelCase					:						List[Any]					=					torch.mean(torch.abs(A_					)					)

       if torch_device in ["mps"]:
             assert abs(result_sum.item() - 167.46_957_397_460_938					) < 1e-2
             assert abs(result_mean.item() - 0.21_805_934_607_982_635					) < 1e-3
       elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 171.59_353_637_695_312					) < 1e-2
             assert abs(result_mean.item() - 0.22_342_908_382_415_771					) < 1e-3
       else:
             assert abs(result_sum.item() - 162.52_383_422_851_562					) < 1e-2
             assert abs(result_mean.item() - 0.211_619_570_851_326					) < 1e-3





 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					self.scheduler_classes[0]
       __lowerCAmelCase					:						Optional[Any]					=					self.get_scheduler_config()
       __lowerCAmelCase					:						Optional[int]					=					scheduler_class(**A_      ,							use_karras_sigmas=A_					)

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

       __lowerCAmelCase					:						int					=					self.dummy_model()
       __lowerCAmelCase					:						int					=					self.dummy_sample_deter.to(A_					) * scheduler.init_noise_sigma
       __lowerCAmelCase					:						Optional[Any]					=					sample.to(A_					)

       for t in scheduler.timesteps:
             __lowerCAmelCase					:						Tuple					=					scheduler.scale_model_input(A_      ,							A_					)

             __lowerCAmelCase					:						int					=					model(A_      ,							A_					)

             __lowerCAmelCase					:						Union[str, Any]					=					scheduler.step(A_      ,							A_      ,							A_					)
             __lowerCAmelCase					:						str					=					output.prev_sample

       __lowerCAmelCase					:						List[str]					=					torch.sum(torch.abs(A_					)					)
       __lowerCAmelCase					:						Optional[Any]					=					torch.mean(torch.abs(A_					)					)

       if torch_device in ["mps"]:
             assert abs(result_sum.item() - 176.66_974_135_742_188					) < 1e-2
             assert abs(result_mean.item() - 0.23_003_872_730_981_811					) < 1e-2
       elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 177.63_653_564_453_125					) < 1e-2
             assert abs(result_mean.item() - 0.23_003_872_730_981_811					) < 1e-2
       else:
             assert abs(result_sum.item() - 170.3_135_223_388_672					) < 1e-2
             assert abs(result_mean.item() - 0.23_003_872_730_981_811					) < 1e-2





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


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(							self      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)


