




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





           '''simple docstring'''

           A__							=   DecisionTransformerModel(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)

           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 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Tuple:





           '''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     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    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 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[str]:





           '''simple docstring'''

           A__							=   DecisionTransformerModelTester(self)
           A__							=   ConfigTester(self ,			config_class=UpperCAmelCase__ ,			hidden_size=37)


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





           '''simple docstring'''

           self.config_tester.run_common_tests()


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





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase__)


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





           '''simple docstring'''

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[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(UpperCAmelCase__)
                  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(UpperCAmelCase__)] ,			UpperCAmelCase__)


@require_torch
class     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Any:





           '''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(UpperCAmelCase__)
           A__							=   model.config
           torch.manual_seed(0)
           A__							=   torch.randn(1 ,			1 ,			config.state_dim).to(device=UpperCAmelCase__ ,			dtype=torch.floataa)  # env.reset()

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

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

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

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








from __future__ import annotations

import queue


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Dict)						->Any:





           '''simple docstring'''

           A__							=   data
           A__							=   None
           A__							=   None







def SCREAMING_SNAKE_CASE    (	)       ->	TreeNode:



       """simple docstring"""

       print('''\n********Press N to stop entering at any point of time********\n'''						)
       A__							=   input('''Enter the value of the root node: '''						).strip().lower()
       A__							=   queue.Queue()
       A__							=   TreeNode(int(lowercase_						)						)
       q.put(lowercase_						)
       while not q.empty():
              A__							=   q.get()
              A__							=   f"""Enter the left node of {node_found.data}: """
              A__							=   input(lowercase_						).strip().lower() or '''n'''
              if check == "n":
                     return tree_node
              A__							=   TreeNode(int(lowercase_						)						)
              A__							=   left_node
              q.put(lowercase_						)
              A__							=   f"""Enter the right node of {node_found.data}: """
              A__							=   input(lowercase_						).strip().lower() or '''n'''
              if check == "n":
                     return tree_node
              A__							=   TreeNode(int(lowercase_						)						)
              A__							=   right_node
              q.put(lowercase_						)
       raise







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       print(node.data			,    end=''','''						)
       pre_order(node.left						)
       pre_order(node.right						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       in_order(node.left						)
       print(node.data			,    end=''','''						)
       in_order(node.right						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       post_order(node.left						)
       post_order(node.right						)
       print(node.data			,    end=''','''						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   queue.Queue()
       q.put(lowercase_						)
       while not q.empty():
              A__							=   q.get()
              print(node_dequeued.data			,    end=''','''						)
              if node_dequeued.left:
                     q.put(node_dequeued.left						)
              if node_dequeued.right:
                     q.put(node_dequeued.right						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   queue.Queue()
       q.put(lowercase_						)
       while not q.empty():
              A__							=   []
              while not q.empty():
                     A__							=   q.get()
                     print(node_dequeued.data			,    end=''','''						)
                     if node_dequeued.left:
                            list_.append(node_dequeued.left						)
                     if node_dequeued.right:
                            list_.append(node_dequeued.right						)
              print()
              for node in list_:
                     q.put(lowercase_						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   []
       A__							=   node
       while n or stack:
              while n:  # start from root node, find its left child
                     print(n.data			,    end=''','''						)
                     stack.append(lowercase_						)
                     A__							=   n.left
              # end of while means current node doesn't have left child
              A__							=   stack.pop()
              # start to traverse its right child
              A__							=   n.right







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   []
       A__							=   node
       while n or stack:
              while n:
                     stack.append(lowercase_						)
                     A__							=   n.left
              A__							=   stack.pop()
              print(n.data			,    end=''','''						)
              A__							=   n.right







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__			,							A__							=   [], []
       A__							=   node
       stacka.append(lowercase_						)
       while stacka:  # to find the reversed order of post order, store it in stack2
              A__							=   stacka.pop()
              if n.left:
                     stacka.append(n.left						)
              if n.right:
                     stacka.append(n.right						)
              stacka.append(lowercase_						)
       while stacka:  # pop up from stack2 will be the post order
              print(stacka.pop().data			,    end=''','''						)







def SCREAMING_SNAKE_CASE    (	lowercase_ = ""			,    lowercase_=50			,    lowercase_="*"						)       ->	str:



       """simple docstring"""

       if not s:
              return "\n" + width * char
       A__			,							A__							=   divmod(width - len(lowercase_						) - 2			,    2						)
       return f"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(prompt("""Binary Tree Traversals"""))

  _lowerCamelCase     :		TreeNode								=       build_tree()
  print(prompt("""Pre Order Traversal"""))
  pre_order(node)
  print(prompt() + """\n""")

  print(prompt("""In Order Traversal"""))
  in_order(node)
  print(prompt() + """\n""")

  print(prompt("""Post Order Traversal"""))
  post_order(node)
  print(prompt() + """\n""")

  print(prompt("""Level Order Traversal"""))
  level_order(node)
  print(prompt() + """\n""")

  print(prompt("""Actual Level Order Traversal"""))
  level_order_actual(node)
  print("""*""" * 50 + """\n""")

  print(prompt("""Pre Order Traversal - Iteration Version"""))
  pre_order_iter(node)
  print(prompt() + """\n""")

  print(prompt("""In Order Traversal - Iteration Version"""))
  in_order_iter(node)
  print(prompt() + """\n""")

  print(prompt("""Post Order Traversal - Iteration Version"""))
  post_order_iter(node)
  print(prompt())


