






"""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        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     List[Any]				,		__A:     Union[str, Any]				,		__A:     Dict=13				,		__A:     Optional[int]=7				,		__A:     Optional[Any]=6				,		__A:     int=17				,		__A:     Any=23				,		__A:     Optional[int]=11				,		__A:     Any=True				,		):

												'''simple docstring'''

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




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

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

												a__          =  self.get_config()

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




							def  lowercase		(   self:     Union[str, 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  lowercase		(   self:     Union[str, Any]				,		__A:     Tuple				,		__A:     List[Any]				,		__A:     List[Any]				,		__A:     Tuple				,		__A:     Dict				,		__A:     Optional[int]				,		__A:     str				,		):

												'''simple docstring'''

												a__          =  DecisionTransformerModel(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  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:     int    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  config_and_inputs
												a__          =  {
												    '''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        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





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

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

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




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  DecisionTransformerModelTester(self    )
												a__          =  ConfigTester(self				,		config_class=__A				,		hidden_size=37    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A    )




							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

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

																	a__          =  [
																	    '''states''',
																	    '''actions''',
																	    '''rewards''',
																	    '''returns_to_go''',
																	    '''timesteps''',
																	    '''attention_mask''',
																	]

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







@require_torch
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

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

												a__          =  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=__A    )

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

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

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

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

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










"""simple docstring"""







from typing import TYPE_CHECKING

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


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	__a       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


