





"""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     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=13		,     _UpperCAmelCase=7		,     _UpperCAmelCase=6		,     _UpperCAmelCase=17		,     _UpperCAmelCase=23		,     _UpperCAmelCase=11		,     _UpperCAmelCase=True		,     ):
									__a	:  List[Any]         =					parent
									__a	:  Optional[int]         =					batch_size
									__a	:  Any         =					seq_length
									__a	:  str         =					act_dim
									__a	:  str         =					state_dim
									__a	:  Optional[int]         =					hidden_size
									__a	:  List[Any]         =					max_length
									__a	:  int         =					is_training







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					floats_tensor((self.batch_size, self.seq_length, self.state_dim)       )
									__a	:  Tuple         =					floats_tensor((self.batch_size, self.seq_length, self.act_dim)       )
									__a	:  Any         =					floats_tensor((self.batch_size, self.seq_length, 1)       )
									__a	:  List[Any]         =					floats_tensor((self.batch_size, self.seq_length, 1)       )
									__a	:  int         =					ids_tensor((self.batch_size, self.seq_length)		,     vocab_size=1000       )
									__a	:  List[str]         =					random_attention_mask((self.batch_size, self.seq_length)       )

									__a	:  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		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     ):
									__a	:  Any         =					DecisionTransformerModel(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  List[str]         =					model(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       )

									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       ):
									__a	:  str         =					self.prepare_config_and_inputs()
									(
									    __a  
									)	:  str         =					config_and_inputs
									__a	:  List[str]         =					{
									    '''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'''




					__lowerCAmelCase					=					(DecisionTransformerModel,) if is_torch_available() else ()
					__lowerCAmelCase					=					()
					__lowerCAmelCase					=					{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					DecisionTransformerModelTester(self       )
									__a	:  Optional[Any]         =					ConfigTester(self		,     config_class=_UpperCAmelCase		,     hidden_size=37       )







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







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase       )







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







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[int]         =					self.model_tester.prepare_config_and_inputs_for_common()

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

													__a	:  Any         =					[
													    '''states''',
													    '''actions''',
													    '''rewards''',
													    '''returns_to_go''',
													    '''timesteps''',
													    '''attention_mask''',
													]

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






@require_torch
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




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

									__a	:  List[str]         =					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=_UpperCAmelCase       )

									__a	:  Any         =					torch.tensor(_UpperCAmelCase		,     device=_UpperCAmelCase		,     dtype=torch.floataa       ).reshape(1		,     1		,     1       )
									__a	:  int         =					state
									__a	:  Tuple         =					torch.zeros(1		,     0		,     config.act_dim		,     device=_UpperCAmelCase		,     dtype=torch.floataa       )
									__a	:  Dict         =					torch.zeros(1		,     0		,     device=_UpperCAmelCase		,     dtype=torch.floataa       )
									__a	:  int         =					torch.tensor(0		,     device=_UpperCAmelCase		,     dtype=torch.long       ).reshape(1		,     1       )

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

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

													with torch.no_grad():
																	__a	:  int         =					model(
																	    states=_UpperCAmelCase		,     actions=_UpperCAmelCase		,     rewards=_UpperCAmelCase		,     returns_to_go=_UpperCAmelCase		,     timesteps=_UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     return_dict=_UpperCAmelCase		,     )

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

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






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A	     =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					['''MvpTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MvpForCausalLM''',
		    '''MvpForConditionalGeneration''',
		    '''MvpForQuestionAnswering''',
		    '''MvpForSequenceClassification''',
		    '''MvpModel''',
		    '''MvpPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
		from .tokenization_mvp import MvpTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mvp_fast import MvpTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mvp import (
				    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MvpForCausalLM,
				    MvpForConditionalGeneration,
				    MvpForQuestionAnswering,
				    MvpForSequenceClassification,
				    MvpModel,
				    MvpPreTrainedModel,
				)

else:
		import sys

		A	     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)