






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		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=7		,    __UpperCAmelCase=6		,    __UpperCAmelCase=17		,    __UpperCAmelCase=23		,    __UpperCAmelCase=11		,    __UpperCAmelCase=True		,    )->					int:







       '''simple docstring'''





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

 def 		UpperCAmelCase  (							self       )->					Dict:







       '''simple docstring'''





       lowerCAmelCase__       =						floats_tensor((self.batch_size, self.seq_length, self.state_dim)       )
       lowerCAmelCase__       =						floats_tensor((self.batch_size, self.seq_length, self.act_dim)       )
       lowerCAmelCase__       =						floats_tensor((self.batch_size, self.seq_length, 1)       )
       lowerCAmelCase__       =						floats_tensor((self.batch_size, self.seq_length, 1)       )
       lowerCAmelCase__       =						ids_tensor((self.batch_size, self.seq_length)		,    vocab_size=1000       )
       lowerCAmelCase__       =						random_attention_mask((self.batch_size, self.seq_length)       )

       lowerCAmelCase__       =						self.get_config()

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

 def 		UpperCAmelCase  (							self       )->					List[Any]:







       '''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 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					Any:







       '''simple docstring'''





       lowerCAmelCase__       =						DecisionTransformerModel(config=__UpperCAmelCase       )
       model.to(__UpperCAmelCase       )
       model.eval()
       lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					Optional[Any]:







       '''simple docstring'''





       lowerCAmelCase__       =						self.prepare_config_and_inputs()
       (
           (
           lowerCAmelCase__							
       )      ,							(
           lowerCAmelCase__							
       )      ,							(
           lowerCAmelCase__							
       )      ,							(
           lowerCAmelCase__							
       )      ,							(
           lowerCAmelCase__							
       )      ,							(
           lowerCAmelCase__							
       )      ,							(
           lowerCAmelCase__							
       )      ,														
       )       =						config_and_inputs
       lowerCAmelCase__       =						{
           "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	):
 a_         =(DecisionTransformerModel,) if is_torch_available() else ()
 a_         =()
 a_         ={"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

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

 def 		UpperCAmelCase  (							self       )->					str:







       '''simple docstring'''





       lowerCAmelCase__       =						DecisionTransformerModelTester(self       )
       lowerCAmelCase__       =						ConfigTester(self		,    config_class=__UpperCAmelCase		,    hidden_size=37       )

 def 		UpperCAmelCase  (							self       )->					List[str]:







       '''simple docstring'''





       self.config_tester.run_common_tests()

 def 		UpperCAmelCase  (							self       )->					Any:







       '''simple docstring'''





       lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCAmelCase       )

 @slow
 def 		UpperCAmelCase  (							self       )->					Optional[Any]:







       '''simple docstring'''





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


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







       '''simple docstring'''





       lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCAmelCase__       =						[
                 "states",
                 "actions",
                 "rewards",
                 "returns_to_go",
                 "timesteps",
                 "attention_mask",
             ]

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





@require_torch
class      lowercase__     (			unittest.TestCase	):

 @slow
 def 		UpperCAmelCase  (							self       )->					str:







       '''simple docstring'''





       lowerCAmelCase__       =						2  # number of steps of autoregressive prediction we will perform
       lowerCAmelCase__       =						10  # defined by the RL environment, may be normalized
       lowerCAmelCase__       =						DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"       )
       lowerCAmelCase__       =						model.to(__UpperCAmelCase       )
       lowerCAmelCase__       =						model.config
       torch.manual_seed(0       )
       lowerCAmelCase__       =						torch.randn(1		,    1		,    config.state_dim       ).to(device=__UpperCAmelCase		,    dtype=torch.floataa       )  # env.reset()

       lowerCAmelCase__       =						torch.tensor(
           [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]		,    device=__UpperCAmelCase       )

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

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

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

             with torch.no_grad():
                   lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						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       )       )
             lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						(  # env.step(action)
                 torch.randn(1		,    1		,    config.state_dim       ).to(device=__UpperCAmelCase		,    dtype=torch.floataa       ),
                 1.0,
                 False,
                 {},
             )

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














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ['''PLBartTokenizer''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''PLBartForCausalLM''',
	    '''PLBartForConditionalGeneration''',
	    '''PLBartForSequenceClassification''',
	    '''PLBartModel''',
	    '''PLBartPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)






