
"""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 __lowerCAmelCase					:




			"""simple docstring"""







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







								"""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   lowerCamelCase__  (					self    :       int						)   ->			List[Any]:







								"""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=1_000						)
								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   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Optional[int]:







								"""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   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Tuple							, _snake_case    :       Optional[Any]							, _snake_case    :       int							, _snake_case    :       List[Any]							, _snake_case    :       Optional[Any]							, _snake_case    :       List[str]							, _snake_case    :       Tuple							, )   ->			Optional[int]:







								"""simple docstring"""
								A_								=    DecisionTransformerModel(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(_snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case						)

								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    :       List[str]						)   ->			Optional[Any]:







								"""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 __lowerCAmelCase					(       _lowercase  ,					_lowercase  ,					_lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					(DecisionTransformerModel,) if is_torch_available() else ()
			snake_case						   =					()
			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
			snake_case						   =					False

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




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







								"""simple docstring"""
								A_								=    DecisionTransformerModelTester(self						)
								A_								=    ConfigTester(self							, config_class=_snake_case							, hidden_size=37						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Tuple:







								"""simple docstring"""
								self.config_tester.run_common_tests()




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Any:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case						)




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







								"""simple docstring"""
								for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_								=    DecisionTransformerModel.from_pretrained(_snake_case						)
													self.assertIsNotNone(_snake_case						)






			def   lowerCamelCase__  (					self    :       Tuple						)   ->			str:







								"""simple docstring"""
								A_			,     A_								=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_								=    model_class(_snake_case						)
													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(_snake_case						)]							, _snake_case						)





@require_torch
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







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







								"""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(_snake_case						)
								A_								=    model.config
								torch.manual_seed(0						)
								A_								=    torch.randn(1							, 1							, config.state_dim						).to(device=_snake_case							, 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=_snake_case						)

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

								for step in range(_snake_case						):
													A_								=    torch.cat([actions, torch.zeros(1							, 1							, config.act_dim							, device=_snake_case						)]							, dim=1						)
													A_								=    torch.cat([rewards, torch.zeros(1							, 1							, device=_snake_case						)]							, 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=_snake_case							, actions=_snake_case							, rewards=_snake_case							, returns_to_go=_snake_case							, timesteps=_snake_case							, attention_mask=_snake_case							, return_dict=_snake_case							, )

													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=_snake_case							, 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=_snake_case							, dtype=torch.long						) * (step + 1)]							, dim=1						)






"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					, __a = None					, __a = None					):

					'''simple docstring'''





					if start is None:
										A_								=    0

					if end is None:
										A_								=    len(__a					) - 1

					if start >= end:
										return

					A_								=    (start + end) // 2

					slowsort(__a					, __a					, __a					)
					slowsort(__a					, mid + 1					, __a					)

					if sequence[end] < sequence[mid]:
										A_			,     A_								=    sequence[mid], sequence[end]

					slowsort(__a					, __a					, end - 1					)


if __name__ == "__main__":
		from doctest import testmod

		testmod()




