






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     :Union[str, Any]  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :Union[str, Any]=13  ,     lowerCamelCase__     :List[Any]=7  ,     lowerCamelCase__     :int=6  ,     lowerCamelCase__     :Union[str, Any]=17  ,     lowerCamelCase__     :Any=23  ,     lowerCamelCase__     :str=11  ,     lowerCamelCase__     :List[Any]=True  ,     ):
					UpperCamelCase__ :Optional[int]						=      parent
					UpperCamelCase__ :List[Any]						=      batch_size
					UpperCamelCase__ :List[Any]						=      seq_length
					UpperCamelCase__ :Any						=      act_dim
					UpperCamelCase__ :str						=      state_dim
					UpperCamelCase__ :Tuple						=      hidden_size
					UpperCamelCase__ :List[str]						=      max_length
					UpperCamelCase__ :List[str]						=      is_training







				def 				__a   ( self     :Optional[Any]     ):
					UpperCamelCase__ :Tuple						=      floats_tensor((self.batch_size, self.seq_length, self.state_dim)     )
					UpperCamelCase__ :Optional[Any]						=      floats_tensor((self.batch_size, self.seq_length, self.act_dim)     )
					UpperCamelCase__ :str						=      floats_tensor((self.batch_size, self.seq_length, 1)     )
					UpperCamelCase__ :int						=      floats_tensor((self.batch_size, self.seq_length, 1)     )
					UpperCamelCase__ :Any						=      ids_tensor((self.batch_size, self.seq_length)  ,     vocab_size=10_00     )
					UpperCamelCase__ :List[Any]						=      random_attention_mask((self.batch_size, self.seq_length)     )

					UpperCamelCase__ :Union[str, Any]						=      self.get_config()

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







				def 				__a   ( self     :Optional[Any]     ):
					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     :str  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :Union[str, Any]  ,     ):
					UpperCamelCase__ :List[str]						=      DecisionTransformerModel(config=lowerCamelCase__     )
					model.to(lowerCamelCase__     )
					model.eval()
					UpperCamelCase__ :Any						=      model(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )

					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     :List[Any]     ):
					UpperCamelCase__ :Optional[Any]						=      self.prepare_config_and_inputs()
					(
					    (
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		
					) :Optional[Any]						=      config_and_inputs
					UpperCamelCase__ :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        lowerCAmelCase_				(      lowercase						,      lowercase						,      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   Any        =			(DecisionTransformerModel,) if is_torch_available() else ()
				_snake_case				:   List[str]        =			()
				_snake_case				:   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				:   int        =			False

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







				def 				__a   ( self     :Optional[Any]     ):
					UpperCamelCase__ :Tuple						=      DecisionTransformerModelTester(self     )
					UpperCamelCase__ :Union[str, Any]						=      ConfigTester(self  ,     config_class=lowerCamelCase__  ,     hidden_size=37     )







				def 				__a   ( self     :Optional[Any]     ):
					self.config_tester.run_common_tests()







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :List[Any]						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCamelCase__     )







				@slow
				def 				__a   ( self     :Optional[int]     ):
					for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase__ :Any						=      DecisionTransformerModel.from_pretrained(lowerCamelCase__     )
						self.assertIsNotNone(lowerCamelCase__     )







				def 				__a   ( self     :Optional[Any]     ):
					UpperCamelCase__				,		UpperCamelCase__ :str						=      self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase__ :Optional[Any]						=      [
						    """states""",
						    """actions""",
						    """rewards""",
						    """returns_to_go""",
						    """timesteps""",
						    """attention_mask""",
						]

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

@require_torch
class        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




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

					UpperCamelCase__ :Optional[int]						=      torch.tensor(
					    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]  ,     device=lowerCamelCase__     )

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

					for step in range(lowerCamelCase__     ):
						UpperCamelCase__ :int						=      torch.cat([actions, torch.zeros(1  ,     1  ,     config.act_dim  ,     device=lowerCamelCase__     )]  ,     dim=1     )
						UpperCamelCase__ :Any						=      torch.cat([rewards, torch.zeros(1  ,     1  ,     device=lowerCamelCase__     )]  ,     dim=1     )

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

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

						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__ :Optional[Any]						=      (  # env.step(action)
						    torch.randn(1  ,     1  ,     config.state_dim     ).to(device=lowerCamelCase__  ,     dtype=torch.floataa     ),
						    1.0,
						    False,
						    {},
						)

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







"""simple docstring"""



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	float							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	str							,			snake_case__      :	bool = False							,			):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Optional[int]							=							nn.Embedding(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							nn.Embedding(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							False

        UpperCAmelCase__						:    Optional[int]							=							nn.Dropout(p=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							TaConfig(
            vocab_size=snake_case__							,			d_model=snake_case__							,			num_heads=snake_case__							,			d_kv=snake_case__							,			d_ff=snake_case__							,			dropout_rate=snake_case__							,			feed_forward_proj=snake_case__							,			is_decoder=snake_case__							,			is_encoder_decoder=snake_case__							,			)

        UpperCAmelCase__						:    Tuple							=							nn.ModuleList()
        for lyr_num in range(snake_case__							):
             UpperCAmelCase__						:    Tuple							=							TaBlock(snake_case__							)
             self.encoders.append(snake_case__							)

        UpperCAmelCase__						:    str							=							TaLayerNorm(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							nn.Dropout(p=snake_case__							)




   def       __a							(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.token_embedder(snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							encoder_input_tokens.shape[1]
        UpperCAmelCase__						:    List[Any]							=							torch.arange(snake_case__							,			device=encoder_input_tokens.device							)
        x += self.position_encoding(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.dropout_pre(snake_case__							)

        # inverted the attention mask
        UpperCAmelCase__						:    List[Any]							=							encoder_input_tokens.size()
        UpperCAmelCase__						:    Tuple							=							self.get_extended_attention_mask(snake_case__							,			snake_case__							)

        for lyr in self.encoders:
             UpperCAmelCase__						:    Any							=							lyr(snake_case__							,			snake_case__							)[0]
        UpperCAmelCase__						:    Any							=							self.layer_norm(snake_case__							)

        return self.dropout_post(snake_case__							), encoder_inputs_mask


