



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     :




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



    '''simple docstring'''
    __lowerCamelCase													=    parent
    __lowerCamelCase													=    batch_size
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    act_dim
    __lowerCamelCase													=    state_dim
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    max_length
    __lowerCamelCase													=    is_training




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    floats_tensor((self.batch_size, self.seq_length, self.state_dim)				)
    __lowerCamelCase													=    floats_tensor((self.batch_size, self.seq_length, self.act_dim)				)
    __lowerCamelCase													=    floats_tensor((self.batch_size, self.seq_length, 1)				)
    __lowerCamelCase													=    floats_tensor((self.batch_size, self.seq_length, 1)				)
    __lowerCamelCase													=    ids_tensor((self.batch_size, self.seq_length)	,				vocab_size=1000				)
    __lowerCamelCase													=    random_attention_mask((self.batch_size, self.seq_length)				)

    __lowerCamelCase													=    self.get_config()

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




 def        lowerCamelCase      (						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        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    DecisionTransformerModel(config=__UpperCAmelCase				)
    model.to(__UpperCAmelCase				)
    model.eval()
    __lowerCamelCase													=    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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.prepare_config_and_inputs()
    (
        (
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,
    )													=    config_and_inputs
    __lowerCamelCase													=    {
        '''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     (	lowerCAmelCase__  ,	lowerCAmelCase__  ,	lowerCAmelCase__  ,	unittest.TestCase   ):
 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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    DecisionTransformerModelTester(self				)
    __lowerCamelCase													=    ConfigTester(self	,				config_class=__UpperCAmelCase	,				hidden_size=37				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__UpperCAmelCase				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCamelCase													=    DecisionTransformerModel.from_pretrained(__UpperCAmelCase				)
       self.assertIsNotNone(__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()

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

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

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



@require_torch
class  __lowerCAmelCase     (	unittest.TestCase   ):




 @slow
 def        lowerCamelCase      (						self				):



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

    __lowerCamelCase													=    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=__UpperCAmelCase				)

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

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

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

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

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











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     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  RoFormerTokenizer
 lowerCAmelCase__       					=  RoFormerTokenizerFast
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  True




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().setUp()




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''永和服装饰品有限公司,今天天气非常好'''
    __lowerCamelCase													=    '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
    return input_text, output_text




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_tokenizer()
    __lowerCamelCase							,__lowerCamelCase													=    self.get_chinese_input_output_texts()
    __lowerCamelCase													=    tokenizer.tokenize(__UpperCAmelCase				)

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

    __lowerCamelCase													=    tokens + [tokenizer.unk_token]
    __lowerCamelCase													=    [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_rust_tokenizer()
    __lowerCamelCase							,__lowerCamelCase													=    self.get_chinese_input_output_texts()
    __lowerCamelCase													=    tokenizer.tokenize(__UpperCAmelCase				)
    self.assertListEqual(__UpperCAmelCase	,				output_text.split()				)
    __lowerCamelCase													=    tokens + [tokenizer.unk_token]
    __lowerCamelCase													=    [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass






