
"""simple docstring"""




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:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=1_3     ,				__SCREAMING_SNAKE_CASE=7     ,				__SCREAMING_SNAKE_CASE=6     ,				__SCREAMING_SNAKE_CASE=1_7     ,				__SCREAMING_SNAKE_CASE=2_3     ,				__SCREAMING_SNAKE_CASE=1_1     ,				__SCREAMING_SNAKE_CASE=True     ,				)		->     List[Any]:







         """simple docstring"""



         a__		=			parent
         a__		=			batch_size
         a__		=			seq_length
         a__		=			act_dim
         a__		=			state_dim
         a__		=			hidden_size
         a__		=			max_length
         a__		=			is_training



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			floats_tensor((self.batch_size, self.seq_length, self.state_dim)   )
         a__		=			floats_tensor((self.batch_size, self.seq_length, self.act_dim)   )
         a__		=			floats_tensor((self.batch_size, self.seq_length, 1)   )
         a__		=			floats_tensor((self.batch_size, self.seq_length, 1)   )
         a__		=			ids_tensor((self.batch_size, self.seq_length)     ,				vocab_size=1_0_0_0   )
         a__		=			random_attention_mask((self.batch_size, self.seq_length)   )

         a__		=			self.get_config()

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



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				)		->     str:







         """simple docstring"""



         a__		=			DecisionTransformerModel(config=__SCREAMING_SNAKE_CASE   )
         model.to(__SCREAMING_SNAKE_CASE   )
         model.eval()
         a__		=			model(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         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 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			self.prepare_config_and_inputs()
         (
             (
             a__
         )	,     (
             a__
         )	,     (
             a__
         )	,     (
             a__
         )	,     (
             a__
         )	,     (
             a__
         )	,     (
             a__
         )	,     
         )		=			config_and_inputs
         a__		=			{
             '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(_lowercase    ,						_lowercase    ,						_lowercase    ,						unittest.TestCase       ):
     __snake_case:	int						=     (DecisionTransformerModel,) if is_torch_available() else ()
     __snake_case:	str						=     ()
     __snake_case:	Optional[Any]						=     {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

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



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			DecisionTransformerModelTester(self   )
         a__		=			ConfigTester(self     ,				config_class=__SCREAMING_SNAKE_CASE     ,				hidden_size=3_7   )



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







         """simple docstring"""



         self.config_tester.run_common_tests()



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         a__		=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )



     @slow
     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a__		=			DecisionTransformerModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
             self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__	,     a__		=			self.model_tester.prepare_config_and_inputs_for_common()

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

             a__		=			[
                 'states',
                 'actions',
                 'rewards',
                 'returns_to_go',
                 'timesteps',
                 'attention_mask',
             ]

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







@require_torch
class     lowercase(unittest.TestCase       ):



     @slow
     def 			lowercase__  (							self   )		->     int:







         """simple docstring"""



         a__		=			2  # number of steps of autoregressive prediction we will perform
         a__		=			1_0  # defined by the RL environment, may be normalized
         a__		=			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'   )
         a__		=			model.to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.config
         torch.manual_seed(0   )
         a__		=			torch.randn(1     ,				1     ,				config.state_dim   ).to(device=__SCREAMING_SNAKE_CASE     ,				dtype=torch.floataa   )  # env.reset()

         a__		=			torch.tensor(
             [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]     ,				device=__SCREAMING_SNAKE_CASE   )

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

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

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

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

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

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




"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		)     ->						float:
    a__		=			[redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters     ):
        raise ValueError('All input parameters must be positive'     )

    if any(p > 1 for p in parameters[1:4]     ):
        raise ValueError('Relative densities cannot be greater than one'     )
    else:
        a__		=			1 - (matter_density + radiation_density + dark_energy)

        a__		=			(
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        a__		=			hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo LCDM approximation
    a		:       Any     						=       0.3

    print(
        hubble_parameter(
            hubble_constant=68.3,
            radiation_density=1e-4,
            matter_density=matter_density,
            dark_energy=1 - matter_density,
            redshift=0,
        )
    )


