

"""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      _UpperCAmelCase      :
			def __init__(		self   ,		_A   ,		_A=13   ,		_A=7   ,		_A=6   ,		_A=17   ,		_A=23   ,		_A=11   ,		_A=True   ,		)     ->     str:






				'''simple docstring'''







				_UpperCAmelCase     :				Dict          =       parent
				_UpperCAmelCase     :				Union[str, Any]          =       batch_size
				_UpperCAmelCase     :				List[str]          =       seq_length
				_UpperCAmelCase     :				Optional[int]          =       act_dim
				_UpperCAmelCase     :				List[Any]          =       state_dim
				_UpperCAmelCase     :				Optional[Any]          =       hidden_size
				_UpperCAmelCase     :				int          =       max_length
				_UpperCAmelCase     :				List[Any]          =       is_training
			def 				__snake_case			(		self  )     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       floats_tensor((self.batch_size, self.seq_length, self.state_dim)  )
				_UpperCAmelCase     :				List[str]          =       floats_tensor((self.batch_size, self.seq_length, self.act_dim)  )
				_UpperCAmelCase     :				Union[str, Any]          =       floats_tensor((self.batch_size, self.seq_length, 1)  )
				_UpperCAmelCase     :				Optional[Any]          =       floats_tensor((self.batch_size, self.seq_length, 1)  )
				_UpperCAmelCase     :				str          =       ids_tensor((self.batch_size, self.seq_length)   ,		vocab_size=10_00  )
				_UpperCAmelCase     :				Optional[Any]          =       random_attention_mask((self.batch_size, self.seq_length)  )

				_UpperCAmelCase     :				Optional[int]          =       self.get_config()

				return (
				    config,
				    states,
				    actions,
				    rewards,
				    returns_to_go,
				    timesteps,
				    attention_mask,
				)
			def 				__snake_case			(		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 				__snake_case			(		self   ,		_A   ,		_A   ,		_A   ,		_A   ,		_A   ,		_A   ,		_A   ,		)     ->     Dict:






				'''simple docstring'''







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

				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 				__snake_case			(		self  )     ->     List[str]:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       self.prepare_config_and_inputs()
				(
				    _UpperCAmelCase
				)     :				Dict          =       config_and_inputs
				_UpperCAmelCase     :				Tuple          =       {
				    '''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      _UpperCAmelCase      (		a_     ,  a_     ,  a_     ,  unittest.TestCase):
			__a			:							Union[str, Any]					       =				(DecisionTransformerModel,) if is_torch_available() else ()
			__a			:							Tuple					       =				()
			__a			:							Dict					       =				{"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

			# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
			__a			:							Optional[int]					       =				False

			# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
			__a			:							Union[str, Any]					       =				False
			__a			:							Dict					       =				False
			__a			:							Union[str, Any]					       =				False
			__a			:							Dict					       =				False
			__a			:							Optional[Any]					       =				False
			__a			:							Union[str, Any]					       =				False
			__a			:							Any					       =				False
			__a			:							Dict					       =				False
			__a			:							List[Any]					       =				False
			def 				__snake_case			(		self  )     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       DecisionTransformerModelTester(self  )
				_UpperCAmelCase     :				List[str]          =       ConfigTester(self   ,		config_class=lowercase_   ,		hidden_size=37  )
			def 				__snake_case			(		self  )     ->     Optional[Any]:






				'''simple docstring'''







				self.config_tester.run_common_tests()
			def 				__snake_case			(		self  )     ->     Dict:






				'''simple docstring'''







				_UpperCAmelCase     :				Union[str, Any]          =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase_  )
			@slow
			def 				__snake_case			(		self  )     ->     Any:






				'''simple docstring'''







				for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					_UpperCAmelCase     :				str          =       DecisionTransformerModel.from_pretrained(lowercase_  )
					self.assertIsNotNone(lowercase_  )
			def 				__snake_case			(		self  )     ->     str:






				'''simple docstring'''







				_UpperCAmelCase     :				List[Any]          =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					_UpperCAmelCase     :				List[Any]          =       model_class(lowercase_  )
					_UpperCAmelCase     :				Dict          =       inspect.signature(model.forward  )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					_UpperCAmelCase     :				Tuple          =       [*signature.parameters.keys()]

					_UpperCAmelCase     :				Union[str, Any]          =       [
					    '''states''',
					    '''actions''',
					    '''rewards''',
					    '''returns_to_go''',
					    '''timesteps''',
					    '''attention_mask''',
					]

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






@require_torch
class      _UpperCAmelCase      (		unittest.TestCase):
			@slow
			def 				__snake_case			(		self  )     ->     List[Any]:






				'''simple docstring'''







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

				_UpperCAmelCase     :				Optional[int]          =       torch.tensor(
				    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]   ,		device=lowercase_  )

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

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

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

					with torch.no_grad():
						_UpperCAmelCase     :				List[str]          =       model(
						    states=lowercase_   ,		actions=lowercase_   ,		rewards=lowercase_   ,		returns_to_go=lowercase_   ,		timesteps=lowercase_   ,		attention_mask=lowercase_   ,		return_dict=lowercase_   ,		)

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

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








"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__UpperCamelCase    : List[str]				 =						logging.get_logger(__name__)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,*lowercase_     :							Dict  ,**lowercase_     :							List[str]						):
						warnings.warn(
						    '''The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
						    ''' Please use ImageGPTImageProcessor instead.'''  ,lowercase_  ,)
						super().__init__(*lowercase_  ,**lowercase_						)




