





'''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       a_   :





     def __init__( self		:					Dict	,      lowercase		:					Tuple	,      lowercase		:					str=13	,      lowercase		:					Optional[int]=7	,      lowercase		:					str=6	,      lowercase		:					Union[str, Any]=17	,      lowercase		:					Optional[Any]=23	,      lowercase		:					List[str]=11	,      lowercase		:					List[str]=True	,      ):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= parent
        lowercase_			:int    			= batch_size
        lowercase_			:List[str]    			= seq_length
        lowercase_			:int    			= act_dim
        lowercase_			:Optional[int]    			= state_dim
        lowercase_			:int    			= hidden_size
        lowercase_			:Optional[Any]    			= max_length
        lowercase_			:List[str]    			= is_training





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:str    			= floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
        lowercase_			:Tuple    			= floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
        lowercase_			:Tuple    			= floats_tensor((self.batch_size, self.seq_length, 1)						)
        lowercase_			:Optional[Any]    			= floats_tensor((self.batch_size, self.seq_length, 1)						)
        lowercase_			:str    			= ids_tensor((self.batch_size, self.seq_length)	,      vocab_size=1_000						)
        lowercase_			:int    			= random_attention_mask((self.batch_size, self.seq_length)						)

        lowercase_			:str    			= self.get_config()

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





     def 							lowercase__      ( self		:					Optional[int]						):





        """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 							lowercase__      ( self		:					Dict	,      lowercase		:					Optional[Any]	,      lowercase		:					Optional[Any]	,      lowercase		:					Any	,      lowercase		:					Tuple	,      lowercase		:					str	,      lowercase		:					List[Any]	,      lowercase		:					str	,      ):





        """simple docstring"""




        lowercase_			:Tuple    			= DecisionTransformerModel(config=__lowerCAmelCase						)
        model.to(__lowerCAmelCase						)
        model.eval()
        lowercase_			:Optional[int]    			= 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 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:Tuple    			= self.prepare_config_and_inputs()
        (
            (
            lowercase_	
        )			,							(
            lowercase_	
        )			,							(
            lowercase_	
        )			,							(
            lowercase_	
        )			,							(
            lowercase_	
        )			,							(
            lowercase_	
        )			,							(
            lowercase_	
        )			,								
        )			:Any    			= config_and_inputs
        lowercase_			:int    			= {
            "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       a_   (				_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			(DecisionTransformerModel,) if is_torch_available() else ()
     __A      =			()
     __A      =			{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= DecisionTransformerModelTester(self						)
        lowercase_			:Optional[Any]    			= ConfigTester(self	,      config_class=__lowerCAmelCase	,      hidden_size=37						)





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        self.config_tester.run_common_tests()





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase						)





     @slow
     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase_			:Tuple    			= DecisionTransformerModel.from_pretrained(__lowerCAmelCase						)
           self.assertIsNotNone(__lowerCAmelCase						)







     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			,							lowercase_			:List[Any]    			= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowercase_			:int    			= model_class(__lowerCAmelCase						)
           lowercase_			:List[str]    			= inspect.signature(model.forward						)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowercase_			:Tuple    			= [*signature.parameters.keys()]

           lowercase_			:str    			= [
               "states",
               "actions",
               "rewards",
               "returns_to_go",
               "timesteps",
               "attention_mask",
           ]

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







@require_torch
class       a_   (				unittest.TestCase							):





     @slow
     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:List[str]    			= 2  # number of steps of autoregressive prediction we will perform
        lowercase_			:List[str]    			= 10  # defined by the RL environment, may be normalized
        lowercase_			:Union[str, Any]    			= DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"						)
        lowercase_			:List[Any]    			= model.to(__lowerCAmelCase						)
        lowercase_			:List[str]    			= model.config
        torch.manual_seed(0						)
        lowercase_			:Tuple    			= torch.randn(1	,      1	,      config.state_dim						).to(device=__lowerCAmelCase	,      dtype=torch.floataa						)  # env.reset()

        lowercase_			: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=__lowerCAmelCase						)

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

        for step in range(__lowerCAmelCase						):
           lowercase_			:List[Any]    			= torch.cat([actions, torch.zeros(1	,      1	,      config.act_dim	,      device=__lowerCAmelCase						)]	,      dim=1						)
           lowercase_			:Dict    			= torch.cat([rewards, torch.zeros(1	,      1	,      device=__lowerCAmelCase						)]	,      dim=1						)

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

           with torch.no_grad():
              lowercase_			,							lowercase_			,							lowercase_			:Any    			= 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_			:int    			= (  # env.step(action)
               torch.randn(1	,      1	,      config.state_dim						).to(device=__lowerCAmelCase	,      dtype=torch.floataa						),
               1.0,
               False,
               {},
           )

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









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_xlnet import XLNetTokenizer
else:
			_a 						=      None


_a 						=      logging.get_logger(__name__)

_a 						=      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_a 						=      {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

_a 						=      {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

_a 						=      "▁"

# Segments (not really needed)
_a 						=      0
_a 						=      1
_a 						=      2
_a 						=      3
_a 						=      4






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						VOCAB_FILES_NAMES
			lowerCAmelCase_							     =						PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_							     =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_							     =						"""left"""
			lowerCAmelCase_							     =						XLNetTokenizer





			def __init__(    self       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase="<s>"       ,  __lowerCAmelCase="</s>"       ,  __lowerCAmelCase="<unk>"       ,  __lowerCAmelCase="<sep>"       ,  __lowerCAmelCase="<pad>"       ,  __lowerCAmelCase="<cls>"       ,  __lowerCAmelCase="<mask>"       ,  __lowerCAmelCase=["<eop>", "<eod>"]       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		AddedToken(__lowerCAmelCase       ,  lstrip=__lowerCAmelCase       ,  rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase       ,  __lowerCAmelCase							) else mask_token

				super().__init__(
				    vocab_file=__lowerCAmelCase       ,  tokenizer_file=__lowerCAmelCase       ,  do_lower_case=__lowerCAmelCase       ,  remove_space=__lowerCAmelCase       ,  keep_accents=__lowerCAmelCase       ,  bos_token=__lowerCAmelCase       ,  eos_token=__lowerCAmelCase       ,  unk_token=__lowerCAmelCase       ,  sep_token=__lowerCAmelCase       ,  pad_token=__lowerCAmelCase       ,  cls_token=__lowerCAmelCase       ,  mask_token=__lowerCAmelCase       ,  additional_special_tokens=__lowerCAmelCase       ,  **__lowerCAmelCase       ,  )

				lowerCamelCase__								=		3
				lowerCamelCase__								=		do_lower_case
				lowerCamelCase__								=		remove_space
				lowerCamelCase__								=		keep_accents
				lowerCamelCase__								=		vocab_file
				lowerCamelCase__								=		False if not self.vocab_file else True





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__								=		[self.sep_token_id]
				lowerCamelCase__								=		[self.cls_token_id]
				if token_ids_a is None:
					return token_ids_a + sep + cls
				return token_ids_a + sep + token_ids_a + sep + cls





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__								=		[self.sep_token_id]
				lowerCamelCase__								=		[2]

				if token_ids_a is None:
					return len(token_ids_a + sep							) * [0] + cls_segment_id
				return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				if not self.can_save_slow_tokenizer:
					raise ValueError(
					    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
					    '''tokenizer.'''							)

				if not os.path.isdir(__lowerCAmelCase							):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
					return
				lowerCamelCase__								=		os.path.join(
				    __lowerCAmelCase       ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

				if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowerCAmelCase							):
					copyfile(self.vocab_file       ,  __lowerCAmelCase							)

				return (out_vocab_file,)



