

'''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     :  Optional[int]      ,lowercase__     :  str      ,lowercase__     :  Union[str, Any]=1_3      ,lowercase__     :  Union[str, Any]=7      ,lowercase__     :  Optional[int]=6      ,lowercase__     :  List[Any]=1_7      ,lowercase__     :  Any=2_3      ,lowercase__     :  Union[str, Any]=1_1      ,lowercase__     :  Optional[Any]=True      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				seq_length
   __lowercase													=				act_dim
   __lowercase													=				state_dim
   __lowercase													=				hidden_size
   __lowercase													=				max_length
   __lowercase													=				is_training






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				floats_tensor((self.batch_size, self.seq_length, self.state_dim)    )
   __lowercase													=				floats_tensor((self.batch_size, self.seq_length, self.act_dim)    )
   __lowercase													=				floats_tensor((self.batch_size, self.seq_length, 1)    )
   __lowercase													=				floats_tensor((self.batch_size, self.seq_length, 1)    )
   __lowercase													=				ids_tensor((self.batch_size, self.seq_length)      ,vocab_size=1_0_0_0    )
   __lowercase													=				random_attention_mask((self.batch_size, self.seq_length)    )

   __lowercase													=				self.get_config()

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






  def       SCREAMING_SNAKE_CASE   (   self     :  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     :  Tuple      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  int      ,lowercase__     :  Optional[Any]      ,):
   __lowercase													=				DecisionTransformerModel(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,lowercase__      ,lowercase__      ,lowercase__      ,lowercase__      ,lowercase__    )

   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     :  int    ):
   __lowercase													=				self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			
   )													=				config_and_inputs
   __lowercase													=				{
       '''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_			(lowerCamelCase__       , lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    (DecisionTransformerModel,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    ()
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

  # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
  SCREAMING_SNAKE_CASE					:						int           =    False
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    False
  SCREAMING_SNAKE_CASE					:						Any           =    False
  SCREAMING_SNAKE_CASE					:						Tuple           =    False
  SCREAMING_SNAKE_CASE					:						List[str]           =    False
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    False
  SCREAMING_SNAKE_CASE					:						Any           =    False
  SCREAMING_SNAKE_CASE					:						Tuple           =    False
  SCREAMING_SNAKE_CASE					:						Dict           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				DecisionTransformerModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,hidden_size=3_7    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase													=				DecisionTransformerModel.from_pretrained(lowercase__    )
    self.assertIsNotNone(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

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






@require_torch
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				2  # number of steps of autoregressive prediction we will perform
   __lowercase													=				1_0  # defined by the RL environment, may be normalized
   __lowercase													=				DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''    )
   __lowercase													=				model.to(lowercase__    )
   __lowercase													=				model.config
   torch.manual_seed(0    )
   __lowercase													=				torch.randn(1      ,1      ,config.state_dim    ).to(device=lowercase__      ,dtype=torch.floataa    )  # env.reset()

   __lowercase													=				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=lowercase__    )

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

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

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

    with torch.no_grad():
     __lowercase						,			__lowercase						,			__lowercase													=				model(
         states=lowercase__      ,actions=lowercase__      ,rewards=lowercase__      ,returns_to_go=lowercase__      ,timesteps=lowercase__      ,attention_mask=lowercase__      ,return_dict=lowercase__      ,)

    self.assertEqual(action_pred.shape      ,actions.shape    )
    self.assertTrue(torch.allclose(action_pred[0, -1]      ,expected_outputs[step]      ,atol=1e-4    )    )
    __lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				(  # env.step(action)
        torch.randn(1      ,1      ,config.state_dim    ).to(device=lowercase__      ,dtype=torch.floataa    ),
        1.0,
        False,
        {},
    )

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




'''simple docstring'''






import random
from typing import Any


def 	_A  (					A__   ):






 """simple docstring"""



 for _ in range(len(A__   )   ):
  __lowercase													=				random.randint(0  ,       len(A__   ) - 1   )
  __lowercase													=				random.randint(0  ,       len(A__   ) - 1   )
  __lowercase						,			__lowercase													=				data[b], data[a]
 return data


if __name__ == "__main__":
 lowerCAmelCase__      		=			[0, 1, 2, 3, 4, 5, 6, 7]
 lowerCAmelCase__      		=			['''python''', '''says''', '''hello''', '''!''']
 print('''Fisher-Yates Shuffle:''')
 print('''List''', integers, strings)
 print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))

