




"""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  lowercase:







 '''simple docstring'''





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




   '''simple docstring'''



   _snake_case							:		Tuple								=  parent
   _snake_case							:		List[str]								=  batch_size
   _snake_case							:		Tuple								=  seq_length
   _snake_case							:		Dict								=  act_dim
   _snake_case							:		Union[str, Any]								=  state_dim
   _snake_case							:		Any								=  hidden_size
   _snake_case							:		List[Any]								=  max_length
   _snake_case							:		Optional[Any]								=  is_training


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



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

   _snake_case							:		Optional[Any]								=  self.get_config()

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


 def 			UpperCamelCase_       (    self:		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 			UpperCamelCase_       (    self:		Optional[int],				a_:		Dict,				a_:		Tuple,				a_:		Optional[Any],				a_:		Optional[int],				a_:		Any,				a_:		Any,				a_:		Tuple,				):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  DecisionTransformerModel(config=a_		)
   model.to(a_		)
   model.eval()
   _snake_case							:		Tuple								=  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 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.prepare_config_and_inputs()
   (
       (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, 
   )							:		Union[str, Any]								=  config_and_inputs
   _snake_case							:		List[Any]								=  {
       """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  lowercase(					__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 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  DecisionTransformerModelTester(self		)
   _snake_case							:		List[Any]								=  ConfigTester(self,				config_class=a_,				hidden_size=37		)


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   self.config_tester.run_common_tests()


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		str								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a_		)


 @slow
 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:		Optional[int]								=  DecisionTransformerModel.from_pretrained(a_		)
     self.assertIsNotNone(a_		)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case		, _snake_case							:		Optional[int]								=  self.model_tester.prepare_config_and_inputs_for_common()

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

     _snake_case							:		Any								=  [
         """states""",
         """actions""",
         """rewards""",
         """returns_to_go""",
         """timesteps""",
         """attention_mask""",
     ]

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







@require_torch
class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 @slow
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



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

   _snake_case							:		List[Any]								=  torch.tensor(
       [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]],				device=a_		)

   _snake_case							:		List[str]								=  torch.tensor(a_,				device=a_,				dtype=torch.floataa		).reshape(1,				1,				1		)
   _snake_case							:		Optional[Any]								=  state
   _snake_case							:		str								=  torch.zeros(1,				0,				config.act_dim,				device=a_,				dtype=torch.floataa		)
   _snake_case							:		Tuple								=  torch.zeros(1,				0,				device=a_,				dtype=torch.floataa		)
   _snake_case							:		str								=  torch.tensor(0,				device=a_,				dtype=torch.long		).reshape(1,				1		)

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

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

     with torch.no_grad():
       _snake_case		, _snake_case		, _snake_case							:		List[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		)		)
     _snake_case		, _snake_case		, _snake_case		, _snake_case							:		Dict								=  (  # env.step(action)
         torch.randn(1,				1,				config.state_dim		).to(device=a_,				dtype=torch.floataa		),
         1.0,
         False,
         {},
     )

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








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 10_00   ):
  """simple docstring"""

  _snake_case		, _snake_case							:		List[Any]								=  1, 1
  _snake_case							:		str								=  []
  for i in range(1		,       n + 1   ):
    _snake_case							:		Any								=  prev_numerator + 2 * prev_denominator
    _snake_case							:		Optional[Any]								=  prev_numerator + prev_denominator
    if len(str(snake_case__   )   ) > len(str(snake_case__   )   ):
      result.append(snake_case__   )
    _snake_case							:		int								=  numerator
    _snake_case							:		Any								=  denominator

  return len(snake_case__   )


if __name__ == "__main__":
     print(F'''{solution() = }''')


