



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	:      Any      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Tuple=13      ,					lowerCAmelCase	:      int=7      ,					lowerCAmelCase	:      Tuple=6      ,					lowerCAmelCase	:      Any=17      ,					lowerCAmelCase	:      List[str]=23      ,					lowerCAmelCase	:      Dict=11      ,					lowerCAmelCase	:      Dict=True      ,					)						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	parent
         lowercase__  =	batch_size
         lowercase__  =	seq_length
         lowercase__  =	act_dim
         lowercase__  =	state_dim
         lowercase__  =	hidden_size
         lowercase__  =	max_length
         lowercase__  =	is_training


   def       UpperCAmelCase		(    self	:      str)						->					Union[str, Any]:




         """simple docstring"""

         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=10_00)
         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       UpperCAmelCase		(    self	:      str)						->					Tuple:




         """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       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Any      ,					)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	DecisionTransformerModel(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	model(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

         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       UpperCAmelCase		(    self	:      Tuple)						->					Any:




         """simple docstring"""

         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    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Optional[Any]												=     (DecisionTransformerModel,) if is_torch_available() else ()
   A     :	Union[str, Any]												=     ()
   A     :	Tuple												=     {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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


   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	DecisionTransformerModelTester(self)
         lowercase__  =	ConfigTester(self      ,					config_class=lowerCAmelCase      ,					hidden_size=37)


   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					Optional[int]:




         """simple docstring"""

         self.config_tester.run_common_tests()


   def       UpperCAmelCase		(    self	:      str)						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase)


   @slow
   def       UpperCAmelCase		(    self	:      Any)						->					Union[str, Any]:




         """simple docstring"""

         for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__  =	DecisionTransformerModel.from_pretrained(lowerCAmelCase)
               self.assertIsNotNone(lowerCAmelCase)



   def       UpperCAmelCase		(    self	:      Any)						->					List[Any]:




         """simple docstring"""

         lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase__  =	model_class(lowerCAmelCase)
               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(lowerCAmelCase)]      ,					lowerCAmelCase)



@require_torch
class    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   @slow
   def       UpperCAmelCase		(    self	:      Optional[int])						->					Union[str, Any]:




         """simple docstring"""

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

         lowercase__  =	torch.tensor(
             [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]      ,					device=lowerCAmelCase)

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

         for step in range(lowerCAmelCase):
               lowercase__  =	torch.cat([actions, torch.zeros(1      ,					1      ,					config.act_dim      ,					device=lowerCAmelCase)]      ,					dim=1)
               lowercase__  =	torch.cat([rewards, torch.zeros(1      ,					1      ,					device=lowerCAmelCase)]      ,					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=lowerCAmelCase      ,					actions=lowerCAmelCase      ,					rewards=lowerCAmelCase      ,					returns_to_go=lowerCAmelCase      ,					timesteps=lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					return_dict=lowerCAmelCase      ,					)

               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=lowerCAmelCase      ,					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=lowerCAmelCase      ,					dtype=torch.long) * (step + 1)]      ,					dim=1)





import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin



@require_jieba
class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Optional[int]												=     CpmAntTokenizer
   A     :	Optional[int]												=     False


   def       UpperCAmelCase		(    self	:      List[str])						->					str:




         """simple docstring"""

         super().setUp()

         lowercase__  =	[
             '<d>',
             '</d>',
             '<s>',
             '</s>',
             '</_>',
             '<unk>',
             '<pad>',
             '</n>',
             '我',
             '是',
             'C',
             'P',
             'M',
             'A',
             'n',
             't',
         ]
         lowercase__  =	os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['vocab_file'])
         with open(self.vocab_file      ,					'w'      ,					encoding='utf-8') as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))



   @tooslow
   def       UpperCAmelCase		(    self	:      str)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b')
         lowercase__  =	'今天天气真好！'
         lowercase__  =	['今天', '天气', '真', '好', '！']
         lowercase__  =	tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)
         lowercase__  =	'今天天气真好！'
         lowercase__  =	[tokenizer.bos_token] + tokens

         lowercase__  =	[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					lowerCAmelCase)

         lowercase__  =	tokenizer.decode(lowerCAmelCase)
         self.assertEqual(lowerCAmelCase      ,					lowerCAmelCase)
