


"""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 snake_case      :







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





						'''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  a_ (					self      :       List[Any]		)       ->						Optional[int]:





						'''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=10_00		)
						_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  a_ (					self      :       Union[str, Any]		)       ->						str:





						'''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  a_ (					self      :       List[Any]    ,   a__      :       Union[str, Any]    ,   a__      :       str    ,   a__      :       Tuple    ,   a__      :       str    ,   a__      :       Dict    ,   a__      :       str    ,   a__      :       Optional[int]    ,   )       ->						Any:





						'''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  a_ (					self      :       Tuple		)       ->						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 snake_case      (  _UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			(DecisionTransformerModel,) if is_torch_available() else ()
		__UpperCamelCase           =			()
		__UpperCamelCase           =			{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

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







		def  a_ (					self      :       str		)       ->						Tuple:





						'''simple docstring'''







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







		def  a_ (					self      :       Tuple		)       ->						Optional[Any]:





						'''simple docstring'''







						self.config_tester.run_common_tests()







		def  a_ (					self      :       str		)       ->						Optional[int]:





						'''simple docstring'''







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







		@slow
		def  a_ (					self      :       List[Any]		)       ->						List[Any]:





						'''simple docstring'''







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







		def  a_ (					self      :       Optional[int]		)       ->						Dict:





						'''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 snake_case      (  unittest.TestCase):







		@slow
		def  a_ (					self      :       str		)       ->						str:





						'''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_       							=    {
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)