


'''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   UpperCAmelCase_    :





    """simple docstring"""






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







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      str														=						floats_tensor((self.batch_size, self.seq_length, self.state_dim)      )
         __lowerCamelCase  :      Optional[Any]														=						floats_tensor((self.batch_size, self.seq_length, self.act_dim)      )
         __lowerCamelCase  :      List[str]														=						floats_tensor((self.batch_size, self.seq_length, 1)      )
         __lowerCamelCase  :      Tuple														=						floats_tensor((self.batch_size, self.seq_length, 1)      )
         __lowerCamelCase  :      List[Any]														=						ids_tensor((self.batch_size, self.seq_length)     ,	vocab_size=10_00      )
         __lowerCamelCase  :      Any														=						random_attention_mask((self.batch_size, self.seq_length)      )

         __lowerCamelCase  :      str														=						self.get_config()

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







    def  lowercase_							(				self      )							->	Optional[int]:
         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_     ,	)							->	Optional[int]:
         __lowerCamelCase  :      List[Any]														=						DecisionTransformerModel(config=_snake_case      )
         model.to(_snake_case      )
         model.eval()
         __lowerCamelCase  :      Tuple														=						model(_snake_case     ,	_snake_case     ,	_snake_case     ,	_snake_case     ,	_snake_case     ,	_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[Any]:
         __lowerCamelCase  :      Optional[int]														=						self.prepare_config_and_inputs()
         (
             (
             __lowerCamelCase     
         )							,			(
             __lowerCamelCase     
         )							,			(
             __lowerCamelCase     
         )							,			(
             __lowerCamelCase     
         )							,			(
             __lowerCamelCase     
         )							,			(
             __lowerCamelCase     
         )							,			(
             __lowerCamelCase     
         )							,			     
         )  :      Tuple														=						config_and_inputs
         __lowerCamelCase  :      Optional[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   UpperCAmelCase_    (_lowercase   ,						_lowercase   ,						_lowercase   ,						unittest.TestCase    ):





    """simple docstring"""






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

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

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







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      Tuple														=						DecisionTransformerModelTester(self      )
         __lowerCamelCase  :      Dict														=						ConfigTester(self     ,	config_class=_snake_case     ,	hidden_size=37      )







    def  lowercase_							(				self      )							->	Tuple:
         self.config_tester.run_common_tests()







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_snake_case      )







    @slow
    def  lowercase_							(				self      )							->	Optional[int]:
         for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowerCamelCase  :      Optional[Any]														=						DecisionTransformerModel.from_pretrained(_snake_case      )
              self.assertIsNotNone(_snake_case      )







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						self.model_tester.prepare_config_and_inputs_for_common()

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

              __lowerCamelCase  :      Tuple														=						[
                  'states',
                  'actions',
                  'rewards',
                  'returns_to_go',
                  'timesteps',
                  'attention_mask',
              ]

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







@require_torch
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






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

         __lowerCamelCase  :      Optional[Any]														=						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=_snake_case      )

         __lowerCamelCase  :      Optional[Any]														=						torch.tensor(_snake_case     ,	device=_snake_case     ,	dtype=torch.floataa      ).reshape(1     ,	1     ,	1      )
         __lowerCamelCase  :      Dict														=						state
         __lowerCamelCase  :      Dict														=						torch.zeros(1     ,	0     ,	config.act_dim     ,	device=_snake_case     ,	dtype=torch.floataa      )
         __lowerCamelCase  :      Optional[Any]														=						torch.zeros(1     ,	0     ,	device=_snake_case     ,	dtype=torch.floataa      )
         __lowerCamelCase  :      int														=						torch.tensor(0     ,	device=_snake_case     ,	dtype=torch.long      ).reshape(1     ,	1      )

         for step in range(_snake_case      ):
              __lowerCamelCase  :      Any														=						torch.cat([actions, torch.zeros(1     ,	1     ,	config.act_dim     ,	device=_snake_case      )]     ,	dim=1      )
              __lowerCamelCase  :      Optional[Any]														=						torch.cat([rewards, torch.zeros(1     ,	1     ,	device=_snake_case      )]     ,	dim=1      )

              __lowerCamelCase  :      Optional[Any]														=						torch.ones(1     ,	states.shape[1]      ).to(dtype=torch.long     ,	device=states.device      )

              with torch.no_grad():
                   __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						model(
                       states=_snake_case     ,	actions=_snake_case     ,	rewards=_snake_case     ,	returns_to_go=_snake_case     ,	timesteps=_snake_case     ,	attention_mask=_snake_case     ,	return_dict=_snake_case     ,	)

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

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








"""simple docstring"""





from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase_       :    Any						      =			logging.get_logger(__name__)

# TODO: upload to AWS
UpperCamelCase_       :    List[str]						      =			{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					"retribert"




			def __init__(					self    :       str							, _snake_case    :       List[str]=30_522							, _snake_case    :       List[Any]=768							, _snake_case    :       List[Any]=8							, _snake_case    :       Union[str, Any]=12							, _snake_case    :       Optional[Any]=3_072							, _snake_case    :       List[Any]="gelu"							, _snake_case    :       Union[str, Any]=0.1							, _snake_case    :       Dict=0.1							, _snake_case    :       str=512							, _snake_case    :       Tuple=2							, _snake_case    :       Dict=0.0_2							, _snake_case    :       Optional[Any]=1e-12							, _snake_case    :       Tuple=True							, _snake_case    :       Union[str, Any]=128							, _snake_case    :       Union[str, Any]=0							, **_snake_case    :       Any							, )   ->			Optional[int]:







								"""simple docstring"""
								super().__init__(pad_token_id=_snake_case							, **_snake_case						)

								A_								=    vocab_size
								A_								=    hidden_size
								A_								=    num_hidden_layers
								A_								=    num_attention_heads
								A_								=    hidden_act
								A_								=    intermediate_size
								A_								=    hidden_dropout_prob
								A_								=    attention_probs_dropout_prob
								A_								=    max_position_embeddings
								A_								=    type_vocab_size
								A_								=    initializer_range
								A_								=    layer_norm_eps
								A_								=    share_encoders
								A_								=    projection_dim




