


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  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Any=1_3     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=7     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=6     ,							SCREAMING_SNAKE_CASE__				:       Any=1_7     ,							SCREAMING_SNAKE_CASE__				:       str=2_3     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1_1     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=True     ,							)		->			List[Any]:
             a_					:		Dict							=  parent
             a_					:		Dict							=  batch_size
             a_					:		List[str]							=  seq_length
             a_					:		Tuple							=  act_dim
             a_					:		Optional[Any]							=  state_dim
             a_					:		Any							=  hidden_size
             a_					:		Any							=  max_length
             a_					:		Dict							=  is_training
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Tuple:
             a_					:		Optional[int]							=  floats_tensor((self.batch_size, self.seq_length, self.state_dim)     )
             a_					:		Tuple							=  floats_tensor((self.batch_size, self.seq_length, self.act_dim)     )
             a_					:		Tuple							=  floats_tensor((self.batch_size, self.seq_length, 1)     )
             a_					:		List[Any]							=  floats_tensor((self.batch_size, self.seq_length, 1)     )
             a_					:		Optional[Any]							=  ids_tensor((self.batch_size, self.seq_length)     ,							vocab_size=1_0_0_0     )
             a_					:		Optional[int]							=  random_attention_mask((self.batch_size, self.seq_length)     )

             a_					:		int							=  self.get_config()

             return (
                 config,
                 states,
                 actions,
                 rewards,
                 returns_to_go,
                 timesteps,
                 attention_mask,
             )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			List[Any]:
             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				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							)		->			Dict:
             a_					:		Optional[Any]							=  DecisionTransformerModel(config=UpperCamelCase_     )
             model.to(UpperCamelCase_     )
             model.eval()
             a_					:		Union[str, Any]							=  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				:       List[Any]     )		->			str:
             a_					:		int							=  self.prepare_config_and_inputs()
             (
                 (
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,				
             )					:		str							=  config_and_inputs
             a_					:		Dict							=  {
                 '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  SCREAMING_SNAKE_CASE__  (				UpperCamelCase__						,    UpperCamelCase__						,    UpperCamelCase__						,    unittest.TestCase			):
      snake_case__				:     str        =						(DecisionTransformerModel,) if is_torch_available() else ()
      snake_case__				:     List[str]        =						()
      snake_case__				:     List[Any]        =						{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

      # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
      snake_case__				:     Tuple        =						False
      snake_case__				:     str        =						False
      snake_case__				:     Tuple        =						False
      snake_case__				:     Optional[Any]        =						False
      snake_case__				:     Tuple        =						False
      snake_case__				:     Dict        =						False
      snake_case__				:     Tuple        =						False
      snake_case__				:     Optional[Any]        =						False
      snake_case__				:     Optional[int]        =						False
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Optional[Any]:
             a_					:		Optional[Any]							=  DecisionTransformerModelTester(self     )
             a_					:		Union[str, Any]							=  ConfigTester(self     ,							config_class=UpperCamelCase_     ,							hidden_size=3_7     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Optional[Any]:
             self.config_tester.run_common_tests()
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Optional[int]:
             a_					:		int							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase_     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[Any]:







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

      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Dict:
             a_							,		a_					:		Any							=  self.model_tester.prepare_config_and_inputs_for_common()

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

                    a_					:		Any							=  [
                        'states',
                        'actions',
                        'rewards',
                        'returns_to_go',
                        'timesteps',
                        'attention_mask',
                    ]

                    self.assertListEqual(arg_names[: len(UpperCamelCase_     )]     ,							UpperCamelCase_     )
@require_torch
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Any:
             a_					:		int							=  2  # number of steps of autoregressive prediction we will perform
             a_					:		Optional[Any]							=  1_0  # defined by the RL environment, may be normalized
             a_					:		List[Any]							=  DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'     )
             a_					:		List[Any]							=  model.to(UpperCamelCase_     )
             a_					:		List[str]							=  model.config
             torch.manual_seed(0     )
             a_					:		List[str]							=  torch.randn(1     ,							1     ,							config.state_dim     ).to(device=UpperCamelCase_     ,							dtype=torch.floataa     )  # env.reset()

             a_					:		Dict							=  torch.tensor(
                 [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]     ,							device=UpperCamelCase_     )

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

             for step in range(UpperCamelCase_     ):
                    a_					:		Dict							=  torch.cat([actions, torch.zeros(1     ,							1     ,							config.act_dim     ,							device=UpperCamelCase_     )]     ,							dim=1     )
                    a_					:		int							=  torch.cat([rewards, torch.zeros(1     ,							1     ,							device=UpperCamelCase_     )]     ,							dim=1     )

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

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

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






class        _a							:

							def __init__(							self:	Any							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      ''''''
									lowercase__            =      ''''''
									lowercase__            =      []

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									if m == -1:
											return n + 1
									elif n == -1:
											return m + 1
									elif self.dp[m][n] > -1:
											return self.dp[m][n]
									else:
											if self.worda[m] == self.worda[n]:
													lowercase__            =      self.__min_dist_top_down_dp(m - 1    ,						n - 1							)
											else:
													lowercase__            =      self.__min_dist_top_down_dp(UpperCamelCase_    ,						n - 1							)
													lowercase__            =      self.__min_dist_top_down_dp(m - 1    ,						UpperCamelCase_							)
													lowercase__            =      self.__min_dist_top_down_dp(m - 1    ,						n - 1							)
													lowercase__            =      1 + min(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)

											return self.dp[m][n]

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									lowercase__            =      worda
									lowercase__            =      worda
									lowercase__            =      [[-1 for _ in range(len(UpperCamelCase_							)							)] for _ in range(len(UpperCamelCase_							)							)]

									return self.__min_dist_top_down_dp(len(UpperCamelCase_							) - 1    ,						len(UpperCamelCase_							) - 1							)


							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									lowercase__            =      worda
									lowercase__            =      worda
									lowercase__            =      len(UpperCamelCase_							)
									lowercase__            =      len(UpperCamelCase_							)
									lowercase__            =      [[0 for _ in range(n + 1							)] for _ in range(m + 1							)]

									for i in range(m + 1							):
											for j in range(n + 1							):
													if i == 0:  # first string is empty
															lowercase__            =      j
													elif j == 0:  # second string is empty
															lowercase__            =      i
													elif worda[i - 1] == worda[j - 1]:  # last characters are equal
															lowercase__            =      self.dp[i - 1][j - 1]
													else:
															lowercase__            =      self.dp[i][j - 1]
															lowercase__            =      self.dp[i - 1][j]
															lowercase__            =      self.dp[i - 1][j - 1]
															lowercase__            =      1 + min(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)
									return self.dp[m][n]


if __name__ == "__main__":
			lowerCAmelCase       =							EditDistance()

			print('****************** Testing Edit Distance DP Algorithm ******************')
			print()

			lowerCAmelCase       =							input('Enter the first string: ').strip()
			lowerCAmelCase       =							input('Enter the second string: ').strip()

			print()
			print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
			print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
			print()
			print('*************** End of Testing Edit Distance DP Algorithm ***************')


