



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 						lowerCamelCase   :







					'''simple docstring'''







					def __init__(      self     ,						lowerCAmelCase     ,						lowerCAmelCase=13     ,						lowerCAmelCase=7     ,						lowerCAmelCase=6     ,						lowerCAmelCase=17     ,						lowerCAmelCase=23     ,						lowerCAmelCase=11     ,						lowerCAmelCase=True     ,						):
							UpperCAmelCase_						       =					parent
							UpperCAmelCase_						       =					batch_size
							UpperCAmelCase_						       =					seq_length
							UpperCAmelCase_						       =					act_dim
							UpperCAmelCase_						       =					state_dim
							UpperCAmelCase_						       =					hidden_size
							UpperCAmelCase_						       =					max_length
							UpperCAmelCase_						       =					is_training



					def  A__		(      self  ):
							UpperCAmelCase_						       =					floats_tensor((self.batch_size, self.seq_length, self.state_dim)  )
							UpperCAmelCase_						       =					floats_tensor((self.batch_size, self.seq_length, self.act_dim)  )
							UpperCAmelCase_						       =					floats_tensor((self.batch_size, self.seq_length, 1)  )
							UpperCAmelCase_						       =					floats_tensor((self.batch_size, self.seq_length, 1)  )
							UpperCAmelCase_						       =					ids_tensor((self.batch_size, self.seq_length)     ,						vocab_size=1000  )
							UpperCAmelCase_						       =					random_attention_mask((self.batch_size, self.seq_length)  )

							UpperCAmelCase_						       =					self.get_config()

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



					def  A__		(      self  ):
							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     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						):
							UpperCAmelCase_						       =					DecisionTransformerModel(config=A_  )
							model.to(A_  )
							model.eval()
							UpperCAmelCase_						       =					model(A_     ,						A_     ,						A_     ,						A_     ,						A_     ,						A_  )

							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  ):
							UpperCAmelCase_						       =					self.prepare_config_and_inputs()
							(
							    UpperCAmelCase_
							)						       =					config_and_inputs
							UpperCAmelCase_						       =					{
							    """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 						lowerCamelCase   (    lowercase__,  lowercase__,  lowercase__,  unittest.TestCase	):







					'''simple docstring'''







					lowerCAmelCase_					:     int					       =	(DecisionTransformerModel,) if is_torch_available() else ()
					lowerCAmelCase_					:     int					       =	()
					lowerCAmelCase_					:     Tuple					       =	{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

					# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
					lowerCAmelCase_					:     Optional[Any]					       =	False

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



					def  A__		(      self  ):
							UpperCAmelCase_						       =					DecisionTransformerModelTester(self  )
							UpperCAmelCase_						       =					ConfigTester(self     ,						config_class=A_     ,						hidden_size=37  )



					def  A__		(      self  ):
							self.config_tester.run_common_tests()



					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_  )



					@slow
					def  A__		(      self  ):
							for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_						       =					DecisionTransformerModel.from_pretrained(A_  )
									self.assertIsNotNone(A_  )



					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									UpperCAmelCase_						       =					model_class(A_  )
									UpperCAmelCase_						       =					inspect.signature(model.forward  )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCAmelCase_						       =					[*signature.parameters.keys()]

									UpperCAmelCase_						       =					[
									    """states""",
									    """actions""",
									    """rewards""",
									    """returns_to_go""",
									    """timesteps""",
									    """attention_mask""",
									]

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





@require_torch
class 						lowerCamelCase   (    unittest.TestCase	):







					'''simple docstring'''







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

							UpperCAmelCase_						       =					torch.tensor(
							    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]     ,						device=A_  )

							UpperCAmelCase_						       =					torch.tensor(A_     ,						device=A_     ,						dtype=torch.floataa  ).reshape(1     ,						1     ,						1  )
							UpperCAmelCase_						       =					state
							UpperCAmelCase_						       =					torch.zeros(1     ,						0     ,						config.act_dim     ,						device=A_     ,						dtype=torch.floataa  )
							UpperCAmelCase_						       =					torch.zeros(1     ,						0     ,						device=A_     ,						dtype=torch.floataa  )
							UpperCAmelCase_						       =					torch.tensor(0     ,						device=A_     ,						dtype=torch.long  ).reshape(1     ,						1  )

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

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

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

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

									UpperCAmelCase_						       =					action_pred[0, -1]
									UpperCAmelCase_						       =					torch.cat([states, state]     ,						dim=1  )
									UpperCAmelCase_						       =					returns_to_go[0, -1] - reward
									UpperCAmelCase_						       =					torch.cat([returns_to_go, pred_return.reshape(1     ,						1     ,						1  )]     ,						dim=1  )
									UpperCAmelCase_						       =					torch.cat(
									    [timesteps, torch.ones((1, 1)     ,						device=A_     ,						dtype=torch.long  ) * (step + 1)]     ,						dim=1  )





from random import randint, random




def    a__    (		snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							bool = False						,   snake_case__					:							bool = False						,   snake_case__					:							int = 5						,   ):
  _UpperCAmelCase		:							Optional[int]							=     [[-1] * number_of_cells]  # Create a highway without any car
  _UpperCAmelCase		:							Optional[Any]							=     0
  _UpperCAmelCase		:							Dict							=     max(snake_case__						,   0     )
  while i < number_of_cells:
    _UpperCAmelCase		:							int							=     (
        randint(0						,   snake_case__     ) if random_speed else initial_speed
    )  # Place the cars
    i += (
        randint(1						,   max_speed * 2     ) if random_frequency else frequency
    )  # Arbitrary number, may need tuning
  return highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							List[Any]							=     0
  _UpperCAmelCase		:							int							=     highway_now[car_index + 1 :]
  for cell in range(len(snake_case__     )     ):  # May need a better name for this
    if cells[cell] != -1:  # If the cell is not empty then
      return distance  # we have the distance we wanted
    distance += 1
  # Here if the car is near the end of the highway
  return distance + get_distance(snake_case__						,   -1     )




def    a__    (		snake_case__					:							list						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     len(snake_case__     )
  # Beforce calculations, the highway is empty
  _UpperCAmelCase		:							Dict							=     [-1] * number_of_cells

  for car_index in range(snake_case__     ):
    if highway_now[car_index] != -1:
      # Add 1 to the current speed of the car and cap the speed
      _UpperCAmelCase		:							Dict							=     min(highway_now[car_index] + 1						,   snake_case__     )
      # Number of empty cell before the next car
      _UpperCAmelCase		:							List[str]							=     get_distance(snake_case__						,   snake_case__     ) - 1
      # We can't have the car causing an accident
      _UpperCAmelCase		:							List[str]							=     min(next_highway[car_index]						,   snake_case__     )
      if random() < probability:
        # Randomly, a driver will slow down
        _UpperCAmelCase		:							Dict							=     max(next_highway[car_index] - 1						,   0     )
  return next_highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Union[str, Any]							=     len(highway[0]     )

  for i in range(snake_case__     ):
    _UpperCAmelCase		:							Tuple							=     update(highway[i]						,   snake_case__						,   snake_case__     )
    _UpperCAmelCase		:							int							=     [-1] * number_of_cells

    for car_index in range(snake_case__     ):
      _UpperCAmelCase		:							List[Any]							=     next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        _UpperCAmelCase		:							Optional[Any]							=     (car_index + speed) % number_of_cells
        # Commit the change of position
        _UpperCAmelCase		:							Optional[Any]							=     speed
    highway.append(snake_case__     )

  return highway


if __name__ == "__main__":
 import doctest

 doctest.testmod()





