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 			__lowercase		:







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase=1_3     ,			_lowerCamelCase=7     ,			_lowerCamelCase=6     ,			_lowerCamelCase=1_7     ,			_lowerCamelCase=2_3     ,			_lowerCamelCase=1_1     ,			_lowerCamelCase=True     ,			):
         __UpperCamelCase	:	Union[str, Any]		    =  parent
         __UpperCamelCase	:	Dict		    =  batch_size
         __UpperCamelCase	:	Dict		    =  seq_length
         __UpperCamelCase	:	Dict		    =  act_dim
         __UpperCamelCase	:	str		    =  state_dim
         __UpperCamelCase	:	Union[str, Any]		    =  hidden_size
         __UpperCamelCase	:	Optional[Any]		    =  max_length
         __UpperCamelCase	:	str		    =  is_training







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  floats_tensor((self.batch_size, self.seq_length, self.state_dim)    )
         __UpperCamelCase	:	Union[str, Any]		    =  floats_tensor((self.batch_size, self.seq_length, self.act_dim)    )
         __UpperCamelCase	:	str		    =  floats_tensor((self.batch_size, self.seq_length, 1)    )
         __UpperCamelCase	:	str		    =  floats_tensor((self.batch_size, self.seq_length, 1)    )
         __UpperCamelCase	:	Any		    =  ids_tensor((self.batch_size, self.seq_length)     ,			vocab_size=1_0_0_0    )
         __UpperCamelCase	:	str		    =  random_attention_mask((self.batch_size, self.seq_length)    )

         __UpperCamelCase	:	List[Any]		    =  self.get_config()

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







     def lowerCAmelCase     (		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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         __UpperCamelCase	:	Any		    =  DecisionTransformerModel(config=__a    )
         model.to(__a    )
         model.eval()
         __UpperCamelCase	:	Tuple		    =  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 lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  self.prepare_config_and_inputs()
         (
             __UpperCamelCase  
         )	:	Union[str, Any]		    =  config_and_inputs
         __UpperCamelCase	:	Tuple		    =  {
             "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 			__lowercase		(						_UpperCamelCase			,							_UpperCamelCase			,							_UpperCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						(DecisionTransformerModel,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__		   =						()
     SCREAMING_SNAKE_CASE__		   =						{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

     # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  DecisionTransformerModelTester(self    )
         __UpperCamelCase	:	Any		    =  ConfigTester(self     ,			config_class=__a     ,			hidden_size=3_7    )







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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a    )







     @slow
     def lowerCAmelCase     (		self    ):
         for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __UpperCamelCase	:	int		    =  DecisionTransformerModel.from_pretrained(__a    )
             self.assertIsNotNone(__a    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             __UpperCamelCase	:	Optional[int]		    =  model_class(__a    )
             __UpperCamelCase	:	Any		    =  inspect.signature(model.forward    )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             __UpperCamelCase	:	Tuple		    =  [*signature.parameters.keys()]

             __UpperCamelCase	:	List[str]		    =  [
                 "states",
                 "actions",
                 "rewards",
                 "returns_to_go",
                 "timesteps",
                 "attention_mask",
             ]

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






@require_torch
class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     @slow
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  2  # number of steps of autoregressive prediction we will perform
         __UpperCamelCase	:	List[Any]		    =  1_0  # defined by the RL environment, may be normalized
         __UpperCamelCase	:	Tuple		    =  DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'    )
         __UpperCamelCase	:	Optional[Any]		    =  model.to(__a    )
         __UpperCamelCase	:	str		    =  model.config
         torch.manual_seed(0    )
         __UpperCamelCase	:	List[str]		    =  torch.randn(1     ,			1     ,			config.state_dim    ).to(device=__a     ,			dtype=torch.floataa    )  # env.reset()

         __UpperCamelCase	:	int		    =  torch.tensor(
             [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]     ,			device=__a    )

         __UpperCamelCase	:	List[Any]		    =  torch.tensor(__a     ,			device=__a     ,			dtype=torch.floataa    ).reshape(1     ,			1     ,			1    )
         __UpperCamelCase	:	List[Any]		    =  state
         __UpperCamelCase	:	Any		    =  torch.zeros(1     ,			0     ,			config.act_dim     ,			device=__a     ,			dtype=torch.floataa    )
         __UpperCamelCase	:	int		    =  torch.zeros(1     ,			0     ,			device=__a     ,			dtype=torch.floataa    )
         __UpperCamelCase	:	List[Any]		    =  torch.tensor(0     ,			device=__a     ,			dtype=torch.long    ).reshape(1     ,			1    )

         for step in range(__a    ):
             __UpperCamelCase	:	Dict		    =  torch.cat([actions, torch.zeros(1     ,			1     ,			config.act_dim     ,			device=__a    )]     ,			dim=1    )
             __UpperCamelCase	:	int		    =  torch.cat([rewards, torch.zeros(1     ,			1     ,			device=__a    )]     ,			dim=1    )

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

             with torch.no_grad():
                 __UpperCamelCase	:	Any		    =  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	:	Dict		    =  (  # env.step(action)
                 torch.randn(1     ,			1     ,			config.state_dim    ).to(device=__a     ,			dtype=torch.floataa    ),
                 1.0,
                 False,
                 {},
             )

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




'''simple docstring'''
def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count < 0:
        raise ValueError('The given input must be positive'			)

    # get the generated string sequence
    __UpperCamelCase	:	Dict		    =  gray_code_sequence_string(_a			)
    #
    # convert them to integers
    for i in range(len(_a			)			):
        __UpperCamelCase	:	int		    =  int(sequence[i]				,     2			)

    return sequence







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    __UpperCamelCase	:	Dict		    =  1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    __UpperCamelCase	:	Tuple		    =  gray_code_sequence_string(bit_count - 1			)

    __UpperCamelCase	:	str		    =  []

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2			):
        __UpperCamelCase	:	Optional[Any]		    =  '0' + smaller_sequence[i]
        sequence.append(_a			)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2			)			):
        __UpperCamelCase	:	List[Any]		    =  '1' + smaller_sequence[i]
        sequence.append(_a			)

    return sequence


if __name__ == "__main__":
  import doctest

  doctest.testmod()



