





"""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 	lowerCAmelCase					:

       '''simple docstring'''





       def __init__(   self    ,  a__    ,  a__=13    ,  a__=7    ,  a__=6    ,  a__=17    ,  a__=23    ,  a__=11    ,  a__=True    ,  ):
              _UpperCAmelCase     =     parent
              _UpperCAmelCase     =     batch_size
              _UpperCAmelCase     =     seq_length
              _UpperCAmelCase     =     act_dim
              _UpperCAmelCase     =     state_dim
              _UpperCAmelCase     =     hidden_size
              _UpperCAmelCase     =     max_length
              _UpperCAmelCase     =     is_training






       def 							__A (   self     ):
              _UpperCAmelCase     =     floats_tensor((self.batch_size, self.seq_length, self.state_dim)     )
              _UpperCAmelCase     =     floats_tensor((self.batch_size, self.seq_length, self.act_dim)     )
              _UpperCAmelCase     =     floats_tensor((self.batch_size, self.seq_length, 1)     )
              _UpperCAmelCase     =     floats_tensor((self.batch_size, self.seq_length, 1)     )
              _UpperCAmelCase     =     ids_tensor((self.batch_size, self.seq_length)    ,  vocab_size=10_00     )
              _UpperCAmelCase     =     random_attention_mask((self.batch_size, self.seq_length)     )

              _UpperCAmelCase     =     self.get_config()

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






       def 							__A (   self     ):
              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 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  ):
              _UpperCAmelCase     =     DecisionTransformerModel(config=__A     )
              model.to(__A     )
              model.eval()
              _UpperCAmelCase     =     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 							__A (   self     ):
              _UpperCAmelCase     =     self.prepare_config_and_inputs()
              (
                  (
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							
              )     =     config_and_inputs
              _UpperCAmelCase     =     {
                  '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 	lowerCAmelCase					(      snake_case			,	snake_case			,	snake_case			,	unittest.TestCase							):

       '''simple docstring'''





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

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

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






       def 							__A (   self     ):
              _UpperCAmelCase     =     DecisionTransformerModelTester(self     )
              _UpperCAmelCase     =     ConfigTester(self    ,  config_class=__A    ,  hidden_size=37     )






       def 							__A (   self     ):
              self.config_tester.run_common_tests()






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*__A     )






       @slow
       def 							__A (   self     ):
              for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _UpperCAmelCase     =     DecisionTransformerModel.from_pretrained(__A     )
                     self.assertIsNotNone(__A     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

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



@require_torch
class 	lowerCAmelCase					(      unittest.TestCase							):

       '''simple docstring'''





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

              _UpperCAmelCase     =     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     )

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

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

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

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

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













"""simple docstring"""






import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_rjieba
@require_tokenizers
class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  RoFormerTokenizer
       lowerCAmelCase__						 =  RoFormerTokenizerFast
       lowerCAmelCase__						 =  True
       lowerCAmelCase__						 =  True






       def 							__A (   self     ):
              super().setUp()






       def 							__A (   self    ,  **a__     ):
              return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'    ,  **a__     )






       def 							__A (   self    ,  **a__     ):
              return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'    ,  **a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     '永和服装饰品有限公司,今天天气非常好'
              _UpperCAmelCase     =     '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
              return input_text, output_text






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.get_tokenizer()
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.get_chinese_input_output_texts()
              _UpperCAmelCase     =     tokenizer.tokenize(a__     )

              self.assertListEqual(a__    ,  output_text.split()     )

              _UpperCAmelCase     =     tokens + [tokenizer.unk_token]
              _UpperCAmelCase     =     [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(a__     )    ,  a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.get_rust_tokenizer()
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.get_chinese_input_output_texts()
              _UpperCAmelCase     =     tokenizer.tokenize(a__     )
              self.assertListEqual(a__    ,  output_text.split()     )
              _UpperCAmelCase     =     tokens + [tokenizer.unk_token]
              _UpperCAmelCase     =     [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(a__     )    ,  a__     )






       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              pass






