

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     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		List[str]=13     ,       __lowerCamelCase						:		str=7     ,       __lowerCamelCase						:		str=6     ,       __lowerCamelCase						:		Optional[int]=17     ,       __lowerCamelCase						:		Union[str, Any]=23     ,       __lowerCamelCase						:		Union[str, Any]=11     ,       __lowerCamelCase						:		str=True     ,       )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   parent
       SCREAMING_SNAKE_CASE__   =   batch_size
       SCREAMING_SNAKE_CASE__   =   seq_length
       SCREAMING_SNAKE_CASE__   =   act_dim
       SCREAMING_SNAKE_CASE__   =   state_dim
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   max_length
       SCREAMING_SNAKE_CASE__   =   is_training






  def 				lowercase_		(							self						:		Optional[Any] )		->							int:
       SCREAMING_SNAKE_CASE__   =   floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
       SCREAMING_SNAKE_CASE__   =   floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
       SCREAMING_SNAKE_CASE__   =   floats_tensor((self.batch_size, self.seq_length, 1) )
       SCREAMING_SNAKE_CASE__   =   floats_tensor((self.batch_size, self.seq_length, 1) )
       SCREAMING_SNAKE_CASE__   =   ids_tensor((self.batch_size, self.seq_length)     ,       vocab_size=1000 )
       SCREAMING_SNAKE_CASE__   =   random_attention_mask((self.batch_size, self.seq_length) )

       SCREAMING_SNAKE_CASE__   =   self.get_config()

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






  def 				lowercase_		(							self						:		str )		->							int:
       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						:		str     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		List[Any]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		List[str]     ,       )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   DecisionTransformerModel(config=__A )
       model.to(__A )
       model.eval()
       SCREAMING_SNAKE_CASE__   =   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						:		int )		->							str:
       SCREAMING_SNAKE_CASE__   =   self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),
       )   =   config_and_inputs
       SCREAMING_SNAKE_CASE__   =   {
           '''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__		(	A__						,					A__						,					A__						,					unittest.TestCase	):






  """simple docstring"""


  a									= (DecisionTransformerModel,) if is_torch_available() else ()
  a									= ()
  a									= {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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






  def 				lowercase_		(							self						:		int )		->							int:
       SCREAMING_SNAKE_CASE__   =   DecisionTransformerModelTester(self )
       SCREAMING_SNAKE_CASE__   =   ConfigTester(self     ,       config_class=__A     ,       hidden_size=37 )






  def 				lowercase_		(							self						:		Union[str, Any] )		->							Tuple:
       self.config_tester.run_common_tests()






  def 				lowercase_		(							self						:		List[str] )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__A )






  @slow
  def 				lowercase_		(							self						:		Dict )		->							Optional[Any]:
       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__   =   DecisionTransformerModel.from_pretrained(__A )
            self.assertIsNotNone(__A )







  def 				lowercase_		(							self						:		Union[str, Any] )		->							int:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__   =   [
                '''states''',
                '''actions''',
                '''rewards''',
                '''returns_to_go''',
                '''timesteps''',
                '''attention_mask''',
            ]

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






@require_torch
class 	UpperCAmelCase__		(	unittest.TestCase	):






  """simple docstring"""


  @slow
  def 				lowercase_		(							self						:		Dict )		->							Any:
       SCREAMING_SNAKE_CASE__   =   2  # number of steps of autoregressive prediction we will perform
       SCREAMING_SNAKE_CASE__   =   10  # defined by the RL environment, may be normalized
       SCREAMING_SNAKE_CASE__   =   DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
       SCREAMING_SNAKE_CASE__   =   model.to(__A )
       SCREAMING_SNAKE_CASE__   =   model.config
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__   =   torch.randn(1     ,       1     ,       config.state_dim ).to(device=__A     ,       dtype=torch.floataa )  # env.reset()

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

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

       for step in range(__A ):
            SCREAMING_SNAKE_CASE__   =   torch.cat([actions, torch.zeros(1     ,       1     ,       config.act_dim     ,       device=__A )]     ,       dim=1 )
            SCREAMING_SNAKE_CASE__   =   torch.cat([rewards, torch.zeros(1     ,       1     ,       device=__A )]     ,       dim=1 )

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

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

            SCREAMING_SNAKE_CASE__   =   action_pred[0, -1]
            SCREAMING_SNAKE_CASE__   =   torch.cat([states, state]     ,       dim=1 )
            SCREAMING_SNAKE_CASE__   =   returns_to_go[0, -1] - reward
            SCREAMING_SNAKE_CASE__   =   torch.cat([returns_to_go, pred_return.reshape(1     ,       1     ,       1 )]     ,       dim=1 )
            SCREAMING_SNAKE_CASE__   =   torch.cat(
                [timesteps, torch.ones((1, 1)     ,       device=__A     ,       dtype=torch.long ) * (step + 1)]     ,       dim=1 )







from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt
def     UpperCAmelCase_       (						_A				,		_A				,		_A    ):





     '''simple docstring'''




     if not arr:
          return None, None, 0
     if low == high:
          return low, high, arr[low]

     SCREAMING_SNAKE_CASE__   =   (low + high) // 2
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   max_subarray(_A				,		_A				,		_A    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   max_subarray(_A				,		mid + 1				,		_A    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   max_cross_sum(_A				,		_A				,		_A				,		_A    )
     if left_sum >= right_sum and left_sum >= cross_sum:
          return left_low, left_high, left_sum
     elif right_sum >= left_sum and right_sum >= cross_sum:
          return right_low, right_high, right_sum
     return cross_left, cross_right, cross_sum
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   float('''-inf'''    ), -1
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   float('''-inf'''    ), -1

     SCREAMING_SNAKE_CASE__   =   0
     for i in range(_A				,		low - 1				,		-1    ):
          summ += arr[i]
          if summ > left_sum:
               SCREAMING_SNAKE_CASE__   =   summ
               SCREAMING_SNAKE_CASE__   =   i

     SCREAMING_SNAKE_CASE__   =   0
     for i in range(mid + 1				,		high + 1    ):
          summ += arr[i]
          if summ > right_sum:
               SCREAMING_SNAKE_CASE__   =   summ
               SCREAMING_SNAKE_CASE__   =   i

     return max_left, max_right, (left_sum + right_sum)
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   [randint(1				,		_A    ) for _ in range(_A    )]
     SCREAMING_SNAKE_CASE__   =   time.time()
     max_subarray(_A				,		0				,		input_size - 1    )
     SCREAMING_SNAKE_CASE__   =   time.time()
     return end - start
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   [10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
     SCREAMING_SNAKE_CASE__   =   [time_max_subarray(_A    ) for input_size in input_sizes]
     print('''No of Inputs\t\tTime Taken'''    )
     for input_size, runtime in zip(_A				,		_A    ):
          print(_A				,		'''\t\t'''				,		_A    )
     plt.plot(_A				,		_A    )
     plt.xlabel('''Number of Inputs'''    )
     plt.ylabel('''Time taken in seconds'''    )
     plt.show()


if __name__ == "__main__":
       from doctest import testmod

       testmod()


