






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				,		_UpperCamelCase				,		_UpperCamelCase=1_3				,		_UpperCamelCase=7				,		_UpperCamelCase=6				,		_UpperCamelCase=1_7				,		_UpperCamelCase=2_3				,		_UpperCamelCase=1_1				,		_UpperCamelCase=True				,		)	->							int:
							UpperCAmelCase_    :      Dict            =						parent
							UpperCAmelCase_    :      str            =						batch_size
							UpperCAmelCase_    :      List[Any]            =						seq_length
							UpperCAmelCase_    :      Any            =						act_dim
							UpperCAmelCase_    :      Dict            =						state_dim
							UpperCAmelCase_    :      List[str]            =						hidden_size
							UpperCAmelCase_    :      List[Any]            =						max_length
							UpperCAmelCase_    :      Optional[Any]            =						is_training



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      Union[str, Any]            =						floats_tensor((self.batch_size, self.seq_length, self.state_dim)   )
							UpperCAmelCase_    :      Optional[Any]            =						floats_tensor((self.batch_size, self.seq_length, self.act_dim)   )
							UpperCAmelCase_    :      Tuple            =						floats_tensor((self.batch_size, self.seq_length, 1)   )
							UpperCAmelCase_    :      Optional[Any]            =						floats_tensor((self.batch_size, self.seq_length, 1)   )
							UpperCAmelCase_    :      str            =						ids_tensor((self.batch_size, self.seq_length)				,		vocab_size=1_0_0_0   )
							UpperCAmelCase_    :      Optional[Any]            =						random_attention_mask((self.batch_size, self.seq_length)   )

							UpperCAmelCase_    :      Dict            =						self.get_config()

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



		def 					__UpperCAmelCase			(							self   )	->							Optional[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 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							List[str]:
							UpperCAmelCase_    :      List[str]            =						DecisionTransformerModel(config=_UpperCamelCase   )
							model.to(_UpperCamelCase   )
							model.eval()
							UpperCAmelCase_    :      Dict            =						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 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[Any]            =						self.prepare_config_and_inputs()
							(
							    (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  
							)    :      List[str]            =						config_and_inputs
							UpperCAmelCase_    :      Any            =						{
							    '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'''



		_snake_case    :    int       						=  (DecisionTransformerModel,) if is_torch_available() else ()
		_snake_case    :    Tuple       						=  ()
		_snake_case    :    Any       						=  {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

		# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
		_snake_case    :    List[Any]       						=  False

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



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      str            =						DecisionTransformerModelTester(self   )
							UpperCAmelCase_    :      str            =						ConfigTester(self				,		config_class=_UpperCamelCase				,		hidden_size=3_7   )



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							self.config_tester.run_common_tests()



		def 					__UpperCAmelCase			(							self   )	->							Any:
							UpperCAmelCase_    :      Any            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase   )



		@slow
		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_    :      int            =						DecisionTransformerModel.from_pretrained(_UpperCamelCase   )
												self.assertIsNotNone(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCAmelCase_    :      List[Any]            =						model_class(_UpperCamelCase   )
												UpperCAmelCase_    :      Optional[int]            =						inspect.signature(model.forward   )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCAmelCase_    :      Union[str, Any]            =						[*signature.parameters.keys()]

												UpperCAmelCase_    :      List[str]            =						[
												    'states',
												    'actions',
												    'rewards',
												    'returns_to_go',
												    'timesteps',
												    'attention_mask',
												]

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





@require_torch
class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



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

							UpperCAmelCase_    :      Tuple            =						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=_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						torch.tensor(_UpperCamelCase				,		device=_UpperCamelCase				,		dtype=torch.floataa   ).reshape(1				,		1				,		1   )
							UpperCAmelCase_    :      Optional[int]            =						state
							UpperCAmelCase_    :      str            =						torch.zeros(1				,		0				,		config.act_dim				,		device=_UpperCamelCase				,		dtype=torch.floataa   )
							UpperCAmelCase_    :      List[Any]            =						torch.zeros(1				,		0				,		device=_UpperCamelCase				,		dtype=torch.floataa   )
							UpperCAmelCase_    :      Optional[int]            =						torch.tensor(0				,		device=_UpperCamelCase				,		dtype=torch.long   ).reshape(1				,		1   )

							for step in range(_UpperCamelCase   ):
												UpperCAmelCase_    :      Union[str, Any]            =						torch.cat([actions, torch.zeros(1				,		1				,		config.act_dim				,		device=_UpperCamelCase   )]				,		dim=1   )
												UpperCAmelCase_    :      Tuple            =						torch.cat([rewards, torch.zeros(1				,		1				,		device=_UpperCamelCase   )]				,		dim=1   )

												UpperCAmelCase_    :      List[str]            =						torch.ones(1				,		states.shape[1]   ).to(dtype=torch.long				,		device=states.device   )

												with torch.no_grad():
																	UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						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   )   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						(  # env.step(action)
												    torch.randn(1				,		1				,		config.state_dim   ).to(device=_UpperCamelCase				,		dtype=torch.floataa   ),
												    1.0,
												    False,
												    {},
												)

												UpperCAmelCase_    :      List[str]            =						action_pred[0, -1]
												UpperCAmelCase_    :      Tuple            =						torch.cat([states, state]				,		dim=1   )
												UpperCAmelCase_    :      Tuple            =						returns_to_go[0, -1] - reward
												UpperCAmelCase_    :      Optional[Any]            =						torch.cat([returns_to_go, pred_return.reshape(1				,		1				,		1   )]				,		dim=1   )
												UpperCAmelCase_    :      Optional[Any]            =						torch.cat(
												    [timesteps, torch.ones((1, 1)				,		device=_UpperCamelCase				,		dtype=torch.long   ) * (step + 1)]				,		dim=1   )







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__lowerCamelCase					:	Optional[Any]       =  logging.get_logger(__name__)

__lowerCamelCase					:	Dict       =  {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase					:	int       =  {
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

__lowerCamelCase					:	Any       =  {
    '''junnyu/roformer_chinese_small''': 15_36,
    '''junnyu/roformer_chinese_base''': 15_36,
    '''junnyu/roformer_chinese_char_small''': 5_12,
    '''junnyu/roformer_chinese_char_base''': 5_12,
    '''junnyu/roformer_small_discriminator''': 1_28,
    '''junnyu/roformer_small_generator''': 1_28,
}


__lowerCamelCase					:	Union[str, Any]       =  {
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}






class 		a__						(					A__					):
					A	       =							VOCAB_FILES_NAMES
					A	       =							PRETRAINED_VOCAB_FILES_MAP
					A	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A	       =							PRETRAINED_INIT_CONFIGURATION
					A	       =							RoFormerTokenizer

					def __init__(    self      :       List[str],_A      :       int=None,_A      :       int=None,_A      :       int=True,_A      :       List[Any]="[UNK]",_A      :       Tuple="[SEP]",_A      :       List[Any]="[PAD]",_A      :       Optional[int]="[CLS]",_A      :       Optional[Any]="[MASK]",_A      :       Optional[int]=True,_A      :       List[str]=None,**_A      :       List[Any],):







							"""simple docstring"""






							super().__init__(
							    _A,tokenizer_file=_A,do_lower_case=_A,unk_token=_A,sep_token=_A,pad_token=_A,cls_token=_A,mask_token=_A,tokenize_chinese_chars=_A,strip_accents=_A,**_A,)

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
							if (
							    pre_tok_state.get("lowercase",_A    ) != do_lower_case
							    or pre_tok_state.get("strip_accents",_A    ) != strip_accents
							):
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    getattr(_A,pre_tok_state.pop("type"    )    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    do_lower_case
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    strip_accents
									SCREAMING_SNAKE_CASE_  :		str				      =    pre_tok_class(**_A    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    do_lower_case

					def __getstate__(    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.__dict__.copy()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    BertPreTokenizer()
							return state

					def __setstate__(    self      :       List[Any],_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    d
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.__dict__["_tokenizer"].get_vocab()
							SCREAMING_SNAKE_CASE_  :		Any				      =    PreTokenizer.custom(JiebaPreTokenizer(_A    )    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       List[Any],_A      :       str=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

							return output

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [self.sep_token_id]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [self.cls_token_id]
							if token_ids_a is None:
									return len(cls + token_ids_a + sep    ) * [0]
							return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]

					def 				__UpperCamelCase   (    self      :       int,_A      :       str,_A      :       Optional[str] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._tokenizer.model.save(_A,name=_A    )
							return tuple(_A    )






					def 				__UpperCamelCase   (    self      :       int,_A      :       Optional[int],_A      :       List[Any]=None,_A      :       Tuple=None,_A      :       str=False,**_A      :       List[Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    BertPreTokenizer()
							return super().save_pretrained(_A,_A,_A,_A,**_A    )
