






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      :

				"""simple docstring"""




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

								'''simple docstring'''
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    seq_length
								lowercase__     				=    act_dim
								lowercase__     				=    state_dim
								lowercase__     				=    hidden_size
								lowercase__     				=    max_length
								lowercase__     				=    is_training




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
								lowercase__     				=    floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
								lowercase__     				=    floats_tensor((self.batch_size, self.seq_length, 1)						)
								lowercase__     				=    floats_tensor((self.batch_size, self.seq_length, 1)						)
								lowercase__     				=    ids_tensor((self.batch_size, self.seq_length),						vocab_size=1_000						)
								lowercase__     				=    random_attention_mask((self.batch_size, self.seq_length)						)

								lowercase__     				=    self.get_config()

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




				def        lowercase__     (  self		:					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        lowercase__     (  self		:					int,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Any,						lowerCamelCase		:					int,						lowerCamelCase		:					List[str],						lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								lowercase__     				=    DecisionTransformerModel(config=lowerCamelCase						)
								model.to(lowerCamelCase						)
								model.eval()
								lowercase__     				=    model(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)

								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		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.prepare_config_and_inputs()
								(
								    (
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,				
								)     				=    config_and_inputs
								lowercase__     				=    {
								    '''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			):

				"""simple docstring"""




				lowercase__       							= (DecisionTransformerModel,) if is_torch_available() else ()
				lowercase__       							= ()
				lowercase__       							= {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

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




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    DecisionTransformerModelTester(self						)
								lowercase__     				=    ConfigTester(self,						config_class=lowerCamelCase,						hidden_size=37						)




				def        lowercase__     (  self		:					List[str]						):

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




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase						)




				@slow
				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__     				=    DecisionTransformerModel.from_pretrained(lowerCamelCase						)
												self.assertIsNotNone(lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

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

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

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



@require_torch
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				@slow
				def        lowercase__     (  self		:					Dict						):

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

								lowercase__     				=    torch.tensor(
								    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]],						device=lowerCamelCase						)

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

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

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

												with torch.no_grad():
																lowercase__				,		lowercase__				,		lowercase__     				=    model(
																    states=lowerCamelCase,						actions=lowerCamelCase,						rewards=lowerCamelCase,						returns_to_go=lowerCamelCase,						timesteps=lowerCamelCase,						attention_mask=lowerCamelCase,						return_dict=lowerCamelCase,						)

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

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












from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= None
				lowercase__       							= None






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    Node(1       )
				lowercase__     				=    Node(2       )
				lowercase__     				=    Node(3       )
				lowercase__     				=    Node(4       )
				lowercase__     				=    Node(5       )
				return tree






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return [root.data, *preorder(root.left       ), *preorder(root.right       )] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return postorder(root.left       ) + postorder(root.right       ) + [root.data] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return [*inorder(root.left       ), root.data, *inorder(root.right       )] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return (max(height(root.left       )	,     height(root.right       )       ) + 1) if root else 0






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				if root is None:
								return output

				lowercase__     				=    deque([root]       )

				while process_queue:
								lowercase__     				=    process_queue.popleft()
								output.append(node.data       )

								if node.left:
												process_queue.append(node.left       )
								if node.right:
												process_queue.append(node.right       )
				return output






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				def populate_output(lowerCamelCase_	,     lowerCamelCase_       ) -> None:
								if not root:
												return
								if level == 1:
												output.append(root.data       )
								elif level > 1:
												populate_output(root.left	,     level - 1       )
												populate_output(root.right	,     level - 1       )

				populate_output(lowerCamelCase_	,     lowerCamelCase_       )
				return output






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				def populate_output(lowerCamelCase_	,     lowerCamelCase_       ) -> None:
								if root is None:
												return
								if level == 1:
												output.append(root.data       )
								elif level > 1:
												populate_output(root.right	,     level - 1       )
												populate_output(root.left	,     level - 1       )

				populate_output(lowerCamelCase_	,     lowerCamelCase_       )
				return output






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if root is None:
								return []

				lowercase__     				=    []

				lowercase__     				=    0
				lowercase__     				=    height(lowerCamelCase_       )

				for h in range(1	,     height_tree + 1       ):
								if not flag:
												output.append(get_nodes_from_left_to_right(lowerCamelCase_	,     lowerCamelCase_       )       )
												lowercase__     				=    1
								else:
												output.append(get_nodes_from_right_to_left(lowerCamelCase_	,     lowerCamelCase_       )       )
												lowercase__     				=    0

				return output






def 						a    (		):  # Main function for testing.






				'''simple docstring'''
				lowercase__     				=    make_tree()
				print(F"""In-order Traversal: {inorder(lowerCamelCase_       )}"""       )
				print(F"""Pre-order Traversal: {preorder(lowerCamelCase_       )}"""       )
				print(F"""Post-order Traversal: {postorder(lowerCamelCase_       )}"""	,     '''\n'''       )

				print(F"""Height of Tree: {height(lowerCamelCase_       )}"""	,     '''\n'''       )

				print('''Complete Level Order Traversal: '''       )
				print(level_order(lowerCamelCase_       )	,     '''\n'''       )

				print('''Level-wise order Traversal: '''       )

				for level in range(1	,     height(lowerCamelCase_       ) + 1       ):
								print(F"""Level {level}:"""	,     get_nodes_from_left_to_right(lowerCamelCase_	,     level=lowerCamelCase_       )       )

				print('''\nZigZag order Traversal: '''       )
				print(zigzag(lowerCamelCase_       )       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()




