





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 			__A       :




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__=13      ,							a__=7      ,							a__=6      ,							a__=17      ,							a__=23      ,							a__=11      ,							a__=True      ,							):



             """simple docstring"""





             _lowerCamelCase							:     str	=		parent
             _lowerCamelCase							:     Optional[int]	=		batch_size
             _lowerCamelCase							:     int	=		seq_length
             _lowerCamelCase							:     List[str]	=		act_dim
             _lowerCamelCase							:     Union[str, Any]	=		state_dim
             _lowerCamelCase							:     int	=		hidden_size
             _lowerCamelCase							:     Any	=		max_length
             _lowerCamelCase							:     int	=		is_training


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		floats_tensor((self.batch_size, self.seq_length, self.state_dim))
             _lowerCamelCase							:     int	=		floats_tensor((self.batch_size, self.seq_length, self.act_dim))
             _lowerCamelCase							:     Dict	=		floats_tensor((self.batch_size, self.seq_length, 1))
             _lowerCamelCase							:     List[str]	=		floats_tensor((self.batch_size, self.seq_length, 1))
             _lowerCamelCase							:     Optional[int]	=		ids_tensor((self.batch_size, self.seq_length)      ,							vocab_size=1000)
             _lowerCamelCase							:     Optional[Any]	=		random_attention_mask((self.batch_size, self.seq_length))

             _lowerCamelCase							:     Union[str, Any]	=		self.get_config()

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


      def 				__snake_case			(		self):



             """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 				__snake_case			(		self      ,							a__      ,							a__      ,							a__      ,							a__      ,							a__      ,							a__      ,							a__      ,							):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		DecisionTransformerModel(config=_lowerCAmelCase)
             model.to(_lowerCAmelCase)
             model.eval()
             _lowerCamelCase							:     Any	=		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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.prepare_config_and_inputs()
             (
                 (
                 _lowerCamelCase						
             ),  (
                 _lowerCamelCase						
             ),  (
                 _lowerCamelCase						
             ),  (
                 _lowerCamelCase						
             ),  (
                 _lowerCamelCase						
             ),  (
                 _lowerCamelCase						
             ),  (
                 _lowerCamelCase						
             ),  						
             )							:     Union[str, Any]	=		config_and_inputs
             _lowerCamelCase							:     str	=		{
                 '''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 			__A       (      _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		DecisionTransformerModelTester(self)
             _lowerCamelCase							:     str	=		ConfigTester(self      ,							config_class=_lowerCAmelCase      ,							hidden_size=37)


      def 				__snake_case			(		self):



             """simple docstring"""





             self.config_tester.run_common_tests()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*_lowerCAmelCase)


      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase							:     int	=		DecisionTransformerModel.from_pretrained(_lowerCAmelCase)
                    self.assertIsNotNone(_lowerCAmelCase)



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.model_tester.prepare_config_and_inputs_for_common()

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

                    _lowerCamelCase							:     int	=		[
                        '''states''',
                        '''actions''',
                        '''rewards''',
                        '''returns_to_go''',
                        '''timesteps''',
                        '''attention_mask''',
                    ]

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







@require_torch
class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     Optional[int]	=		torch.tensor(
                 [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]      ,							device=_lowerCAmelCase)

             _lowerCamelCase							:     int	=		torch.tensor(_lowerCAmelCase      ,							device=_lowerCAmelCase      ,							dtype=torch.floataa).reshape(1      ,							1      ,							1)
             _lowerCamelCase							:     Any	=		state
             _lowerCamelCase							:     Optional[int]	=		torch.zeros(1      ,							0      ,							config.act_dim      ,							device=_lowerCAmelCase      ,							dtype=torch.floataa)
             _lowerCamelCase							:     Optional[Any]	=		torch.zeros(1      ,							0      ,							device=_lowerCAmelCase      ,							dtype=torch.floataa)
             _lowerCamelCase							:     List[Any]	=		torch.tensor(0      ,							device=_lowerCAmelCase      ,							dtype=torch.long).reshape(1      ,							1)

             for step in range(_lowerCAmelCase):
                    _lowerCamelCase							:     Optional[int]	=		torch.cat([actions, torch.zeros(1      ,							1      ,							config.act_dim      ,							device=_lowerCAmelCase)]      ,							dim=1)
                    _lowerCamelCase							:     Tuple	=		torch.cat([rewards, torch.zeros(1      ,							1      ,							device=_lowerCAmelCase)]      ,							dim=1)

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

                    with torch.no_grad():
                           _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		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))
                    _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		(  # env.step(action)
                        torch.randn(1      ,							1      ,							config.state_dim).to(device=_lowerCAmelCase      ,							dtype=torch.floataa),
                        1.0,
                        False,
                        {},
                    )

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








import math




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[str]	=		len(lowercase_      )
       _lowerCamelCase							:     Optional[int]	=		int(math.floor(math.sqrt(lowercase_      )      )      )
       _lowerCamelCase							:     Tuple	=		0
       while arr[min(lowercase_ ,    lowercase_      ) - 1] < x:
              _lowerCamelCase							:     Optional[Any]	=		step
              step += int(math.floor(math.sqrt(lowercase_      )      )      )
              if prev >= n:
                     return -1

       while arr[prev] < x:
              _lowerCamelCase							:     int	=		prev + 1
              if prev == min(lowercase_ ,    lowercase_      ):
                     return -1
       if arr[prev] == x:
              return prev
       return -1


if __name__ == "__main__":
       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       _lowerCamelCase			       =      int(input('Enter the number to be searched:\n'))
       _lowerCamelCase			       =      jump_search(arr, x)
       if res == -1:
              print('Number not found!')
       else:
              print(F'''Number {x} is at index {res}''')

