


'''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_  :






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


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							parent
          UpperCAmelCase__     :						Tuple						=							batch_size
          UpperCAmelCase__     :						Optional[Any]						=							seq_length
          UpperCAmelCase__     :						Tuple						=							act_dim
          UpperCAmelCase__     :						Dict						=							state_dim
          UpperCAmelCase__     :						int						=							hidden_size
          UpperCAmelCase__     :						Dict						=							max_length
          UpperCAmelCase__     :						Any						=							is_training






      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							floats_tensor((self.batch_size, self.seq_length, self.state_dim)					)
          UpperCAmelCase__     :						Dict						=							floats_tensor((self.batch_size, self.seq_length, self.act_dim)					)
          UpperCAmelCase__     :						Optional[Any]						=							floats_tensor((self.batch_size, self.seq_length, 1)					)
          UpperCAmelCase__     :						str						=							floats_tensor((self.batch_size, self.seq_length, 1)					)
          UpperCAmelCase__     :						str						=							ids_tensor((self.batch_size, self.seq_length)		,		vocab_size=1_000					)
          UpperCAmelCase__     :						Optional[Any]						=							random_attention_mask((self.batch_size, self.seq_length)					)

          UpperCAmelCase__     :						Tuple						=							self.get_config()

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






      def lowercase_				(   self							:       Dict					):


          '''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		,		_A							:       int		,		_A							:       Optional[int]		,		_A							:       int		,		_A							:       int		,		_A							:       Union[str, Any]		,		_A							:       List[str]		,		_A							:       List[str]		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							DecisionTransformerModel(config=_A					)
          model.to(_A					)
          model.eval()
          UpperCAmelCase__     :						List[Any]						=							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 lowercase_				(   self							:       Any					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		
          )     :						Union[str, Any]						=							config_and_inputs
          UpperCAmelCase__     :						Optional[int]						=							{
              '''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_  (      __a	,			__a	,			__a	,			unittest.TestCase ):
      lowerCAmelCase__							=	(DecisionTransformerModel,) if is_torch_available() else ()
      lowerCAmelCase__							=	()
      lowerCAmelCase__							=	{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

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






      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[Any]						=							DecisionTransformerModelTester(self					)
          UpperCAmelCase__     :						str						=							ConfigTester(self		,		config_class=_A		,		hidden_size=37					)






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          self.config_tester.run_common_tests()






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_A					)






      @slow
      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


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

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

              UpperCAmelCase__     :						str						=							[
                  '''states''',
                  '''actions''',
                  '''rewards''',
                  '''returns_to_go''',
                  '''timesteps''',
                  '''attention_mask''',
              ]

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




@require_torch
class      lowerCamelCase_  (      unittest.TestCase ):






      @slow
      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


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

          UpperCAmelCase__     :						Optional[int]						=							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					)

          UpperCAmelCase__     :						Tuple						=							torch.tensor(_A		,		device=_A		,		dtype=torch.floataa					).reshape(1		,		1		,		1					)
          UpperCAmelCase__     :						Tuple						=							state
          UpperCAmelCase__     :						Union[str, Any]						=							torch.zeros(1		,		0		,		config.act_dim		,		device=_A		,		dtype=torch.floataa					)
          UpperCAmelCase__     :						int						=							torch.zeros(1		,		0		,		device=_A		,		dtype=torch.floataa					)
          UpperCAmelCase__     :						Union[str, Any]						=							torch.tensor(0		,		device=_A		,		dtype=torch.long					).reshape(1		,		1					)

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

              UpperCAmelCase__     :						List[str]						=							torch.ones(1		,		states.shape[1]					).to(dtype=torch.long		,		device=states.device					)

              with torch.no_grad():
                  UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__     :						Any						=							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					)					)
              UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__     :						Dict						=							(  # env.step(action)
                  torch.randn(1		,		1		,		config.state_dim					).to(device=_A		,		dtype=torch.floataa					),
                  1.0,
                  False,
                  {},
              )

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

"""simple docstring"""






from __future__ import annotations


def      UpperCAmelCase__			(							lowerCAmelCase__	:dict   ,						lowerCAmelCase__	:str						)					->       set[str]:
      '''simple docstring'''


      lowercase				,				lowercase								=      set(lowerCAmelCase__						), [start]

      while stack:
            lowercase								=      stack.pop()
            explored.add(lowerCAmelCase__						)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]						):
                  if adj not in explored:
                        stack.append(lowerCAmelCase__						)
      return explored


__lowerCAmelCase       :			str   	={
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, """A"""))



