'''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        __A      :



    def __init__(self    :    Any   ,			__a    :    List[str]   ,			__a    :    List[Any]=13   ,			__a    :    Dict=7   ,			__a    :    Optional[int]=6   ,			__a    :    Tuple=17   ,			__a    :    Any=23   ,			__a    :    Dict=11   ,			__a    :    Optional[int]=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 					_lowercase						(self    :    List[str] ):
         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 					_lowercase						(self    :    Dict ):
         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    :    int   ,			__a    :    Dict   ,			__a    :    Optional[int]   ,			__a    :    str   ,			__a    :    str   ,			__a    :    str   ,			__a    :    Optional[int]   ,			__a    :    Optional[Any]   ,			):
         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 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             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        __A      ( UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Any          =  (DecisionTransformerModel,) if is_torch_available() else ()
    a__       :						List[Any]          =  ()
    a__       :						int          =  {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

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



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   DecisionTransformerModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a   ,			hidden_size=37 )



    def 					_lowercase						(self    :    Dict ):
         self.config_tester.run_common_tests()



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a )



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






    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_    , 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        __A      ( unittest.TestCase	):



    @slow
    def 					_lowercase						(self    :    Optional[int] ):
         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(
             [[0.24_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=__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_    , UpperCAmelCase_    , 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_    , UpperCAmelCase_    , UpperCAmelCase_    , 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 )





'''simple docstring'''





from __future__ import annotations





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : int | None = None					,    snake_case_       : int | None = None			)    ->      None:


     '''simple docstring'''




     if start is None:
          UpperCAmelCase_           =   0

     if end is None:
          UpperCAmelCase_           =   len(snake_case_			) - 1

     if start >= end:
          return

     UpperCAmelCase_           =   (start + end) // 2

     slowsort(snake_case_					,    snake_case_					,    snake_case_			)
     slowsort(snake_case_					,    mid + 1					,    snake_case_			)

     if sequence[end] < sequence[mid]:
          UpperCAmelCase_    , UpperCAmelCase_           =   sequence[mid], sequence[end]

     slowsort(snake_case_					,    snake_case_					,    end - 1			)


if __name__ == "__main__":
    from doctest import testmod

    testmod()




