






import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import DecisionTransformerModel
   from transformers.models.decision_transformer.modeling_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						Union[str, Any]=13       ,			UpperCAmelCase_							:						Any=7       ,			UpperCAmelCase_							:						Union[str, Any]=6       ,			UpperCAmelCase_							:						Optional[Any]=17       ,			UpperCAmelCase_							:						Optional[Any]=23       ,			UpperCAmelCase_							:						Optional[int]=11       ,			UpperCAmelCase_							:						Any=True       ,			):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     parent
       SCREAMING_SNAKE_CASE							:   Dict						    =     batch_size
       SCREAMING_SNAKE_CASE							:   Tuple						    =     seq_length
       SCREAMING_SNAKE_CASE							:   List[str]						    =     act_dim
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     state_dim
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     hidden_size
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     max_length
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     is_training


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     floats_tensor((self.batch_size, self.seq_length, self.state_dim)   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     floats_tensor((self.batch_size, self.seq_length, self.act_dim)   )
       SCREAMING_SNAKE_CASE							:   int						    =     floats_tensor((self.batch_size, self.seq_length, 1)   )
       SCREAMING_SNAKE_CASE							:   Any						    =     floats_tensor((self.batch_size, self.seq_length, 1)   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     ids_tensor((self.batch_size, self.seq_length)       ,			vocab_size=1000   )
       SCREAMING_SNAKE_CASE							:   str						    =     random_attention_mask((self.batch_size, self.seq_length)   )

       SCREAMING_SNAKE_CASE							:   str						    =     self.get_config()

       return (
           config,
           states,
           actions,
           rewards,
           returns_to_go,
           timesteps,
           attention_mask,
       )


     def 					_A							(					self							:						List[Any]   ):
       return DecisionTransformerConfig(
           batch_size=self.batch_size       ,			seq_length=self.seq_length       ,			act_dim=self.act_dim       ,			state_dim=self.state_dim       ,			hidden_size=self.hidden_size       ,			max_length=self.max_length       ,			)


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						List[str]       ,			):
       SCREAMING_SNAKE_CASE							:   Dict						    =     DecisionTransformerModel(config=UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   Any						    =     model(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

       self.parent.assertEqual(result.state_preds.shape       ,			states.shape   )
       self.parent.assertEqual(result.action_preds.shape       ,			actions.shape   )
       self.parent.assertEqual(result.return_preds.shape       ,			returns_to_go.shape   )
       self.parent.assertEqual(
           result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length * 3, self.hidden_size)   )  # seq length *3 as there are 3 modelities: states, returns and actions






     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	
       )							:   Union[str, Any]						    =     config_and_inputs
       SCREAMING_SNAKE_CASE							:   Any						    =     {
           "states": states,
           "actions": actions,
           "rewards": rewards,
           "returns_to_go": returns_to_go,
           "timesteps": timesteps,
           "attention_mask": attention_mask,
       }
       return config, inputs_dict





@require_torch
class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase   ,		lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Union[str, Any]								=						(DecisionTransformerModel,) if is_torch_available() else ()
     UpperCamelCase_				:		Dict								=						()
     UpperCamelCase_				:		Optional[int]								=						{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

     # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
     UpperCamelCase_				:		str								=						False

     # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
     UpperCamelCase_				:		Tuple								=						False
     UpperCamelCase_				:		str								=						False
     UpperCamelCase_				:		Union[str, Any]								=						False
     UpperCamelCase_				:		Optional[Any]								=						False
     UpperCamelCase_				:		Tuple								=						False
     UpperCamelCase_				:		Union[str, Any]								=						False
     UpperCamelCase_				:		Optional[Any]								=						False
     UpperCamelCase_				:		Dict								=						False
     UpperCamelCase_				:		Optional[Any]								=						False


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   str						    =     DecisionTransformerModelTester(self   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     ConfigTester(self       ,			config_class=UpperCAmelCase_       ,			hidden_size=37   )


     def 					_A							(					self							:						Optional[int]   ):
       self.config_tester.run_common_tests()


     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase_   )


     @slow
     def 					_A							(					self							:						str   ):
       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE							:   int						    =     DecisionTransformerModel.from_pretrained(UpperCAmelCase_   )
         self.assertIsNotNone(UpperCAmelCase_   )






     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     model_class(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   int						    =     inspect.signature(model.forward   )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         SCREAMING_SNAKE_CASE							:   List[str]						    =     [*signature.parameters.keys()]

         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     [
             "states",
             "actions",
             "rewards",
             "returns_to_go",
             "timesteps",
             "attention_mask",
         ]

         self.assertListEqual(arg_names[: len(UpperCAmelCase_   )]       ,			UpperCAmelCase_   )





@require_torch
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     @slow
     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     2  # number of steps of autoregressive prediction we will perform
       SCREAMING_SNAKE_CASE							:   Tuple						    =     10  # defined by the RL environment, may be normalized
       SCREAMING_SNAKE_CASE							:   int						    =     DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"   )
       SCREAMING_SNAKE_CASE							:   int						    =     model.to(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   int						    =     model.config
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     torch.randn(1       ,			1       ,			config.state_dim   ).to(device=UpperCAmelCase_       ,			dtype=torch.floataa   )  # env.reset()

       SCREAMING_SNAKE_CASE							:   List[str]						    =     torch.tensor(
           [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]       ,			device=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     torch.tensor(UpperCAmelCase_       ,			device=UpperCAmelCase_       ,			dtype=torch.floataa   ).reshape(1       ,			1       ,			1   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     state
       SCREAMING_SNAKE_CASE							:   str						    =     torch.zeros(1       ,			0       ,			config.act_dim       ,			device=UpperCAmelCase_       ,			dtype=torch.floataa   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch.zeros(1       ,			0       ,			device=UpperCAmelCase_       ,			dtype=torch.floataa   )
       SCREAMING_SNAKE_CASE							:   Any						    =     torch.tensor(0       ,			device=UpperCAmelCase_       ,			dtype=torch.long   ).reshape(1       ,			1   )

       for step in range(UpperCAmelCase_   ):
         SCREAMING_SNAKE_CASE							:   str						    =     torch.cat([actions, torch.zeros(1       ,			1       ,			config.act_dim       ,			device=UpperCAmelCase_   )]       ,			dim=1   )
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     torch.cat([rewards, torch.zeros(1       ,			1       ,			device=UpperCAmelCase_   )]       ,			dim=1   )

         SCREAMING_SNAKE_CASE							:   Dict						    =     torch.ones(1       ,			states.shape[1]   ).to(dtype=torch.long       ,			device=states.device   )

         with torch.no_grad():
           SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Tuple						    =     model(
               states=UpperCAmelCase_       ,			actions=UpperCAmelCase_       ,			rewards=UpperCAmelCase_       ,			returns_to_go=UpperCAmelCase_       ,			timesteps=UpperCAmelCase_       ,			attention_mask=UpperCAmelCase_       ,			return_dict=UpperCAmelCase_       ,			)

         self.assertEqual(action_pred.shape       ,			actions.shape   )
         self.assertTrue(torch.allclose(action_pred[0, -1]       ,			expected_outputs[step]       ,			atol=1E-4   )   )
         SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   str						    =     (  # env.step(action)
             torch.randn(1       ,			1       ,			config.state_dim   ).to(device=UpperCAmelCase_       ,			dtype=torch.floataa   ),
             1.0,
             False,
             {},
         )

         SCREAMING_SNAKE_CASE							:   List[Any]						    =     action_pred[0, -1]
         SCREAMING_SNAKE_CASE							:   str						    =     torch.cat([states, state]       ,			dim=1   )
         SCREAMING_SNAKE_CASE							:   List[str]						    =     returns_to_go[0, -1] - reward
         SCREAMING_SNAKE_CASE							:   Dict						    =     torch.cat([returns_to_go, pred_return.reshape(1       ,			1       ,			1   )]       ,			dim=1   )
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch.cat(
             [timesteps, torch.ones((1, 1)       ,			device=UpperCAmelCase_       ,			dtype=torch.long   ) * (step + 1)]       ,			dim=1   )








'''simple docstring'''
import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowercase__											=False







class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=32	):
				set_seed(0	)
				a_      				=						UNetaDModel(sample_size=UpperCAmelCase					,   in_channels=3					,   out_channels=3	)
				a_      				=						torch.optim.SGD(model.parameters()					,   lr=0.00_01	)
				return model, optimizer






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				a_      				=						DDPMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )
				a_      				=						DDIMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				a_      				=						[torch.randn((4, 3, 32, 32)	).clip(-1					,   1	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randn((4, 3, 32, 32)	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randint(0					,   10_00					,   (4,)	).long().to(UpperCAmelCase	) for _ in range(4	)]

				# train with a DDPM scheduler
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddpm_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddim_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
