






'''simple docstring'''

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

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


if is_sentencepiece_available():
      from .tokenization_pegasus import PegasusTokenizer
else:
      UpperCamelCase      					=None


UpperCamelCase      					=logging.get_logger(__name__)


UpperCamelCase      					="▁"

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

UpperCamelCase      					={
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

UpperCamelCase      					={
    "google/pegasus-xsum": 512,
}




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    int			  =    VOCAB_FILES_NAMES
 __a      :    int			  =    PRETRAINED_VOCAB_FILES_MAP
 __a      :    Dict			  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __a      :    Optional[int]			  =    PegasusTokenizer
 __a      :    Optional[Any]			  =    ['''input_ids''', '''attention_mask''']
 def __init__(				self      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase="<pad>"      ,  __lowerCAmelCase="</s>"      ,  __lowerCAmelCase="<unk>"      ,  __lowerCAmelCase="<mask_2>"      ,  __lowerCAmelCase="<mask_1>"      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=1_03      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: Tuple            =				offset

       if additional_special_tokens is not None:
             if not isinstance(__lowerCAmelCase      ,  __lowerCAmelCase			):
                   raise TypeError(
                       F"additional_special_tokens should be of type {type(__lowerCAmelCase			)}, but is"
                       F" {type(__lowerCAmelCase			)}"			)

             UpperCamelCase_					: List[Any]            =				(
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 F"<unk_{i}>" for i in range(len(__lowerCAmelCase			)      ,  self.offset - 1			)
             ]

             if len(set(__lowerCAmelCase			)			) != len(__lowerCAmelCase			):
                   raise ValueError(
                       """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                       F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."			)
             UpperCamelCase_					: Any            =				additional_special_tokens_extended
       else:
             UpperCamelCase_					: Any            =				[mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [F"<unk_{i}>" for i in range(2      ,  self.offset			)]

       super().__init__(
           __lowerCAmelCase      ,  tokenizer_file=__lowerCAmelCase      ,  pad_token=__lowerCAmelCase      ,  eos_token=__lowerCAmelCase      ,  unk_token=__lowerCAmelCase      ,  mask_token=__lowerCAmelCase      ,  mask_token_sent=__lowerCAmelCase      ,  offset=__lowerCAmelCase      ,  additional_special_tokens=__lowerCAmelCase      ,  **__lowerCAmelCase      ,  )
       UpperCamelCase_					: Optional[Any]            =				vocab_file
       UpperCamelCase_					: int            =				False if not self.vocab_file else True
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Optional[int]            =				set(self.all_special_ids			)  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id			)  # <unk> is only sometimes special

       if all_special_ids != set(range(len(self.additional_special_tokens			) + 3			)			):
             raise ValueError(
                 """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
                 F" {len(self.additional_special_tokens			)} additional_special_tokens, but got {all_special_ids}"			)

       return [1 if x in all_special_ids else 0 for x in seq]
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = False			):
       if already_has_special_tokens:
             return self._special_token_mask(__lowerCAmelCase			)
       elif token_ids_a is None:
             return self._special_token_mask(__lowerCAmelCase			) + [1]
       else:
             return self._special_token_mask(token_ids_a + token_ids_a			) + [1]
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=None			):
       if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None			):
       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
       UpperCamelCase_					: Tuple            =				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,)














'''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     :






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=13      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=6      ,  __lowerCAmelCase=17      ,  __lowerCAmelCase=23      ,  __lowerCAmelCase=11      ,  __lowerCAmelCase=True      ,  ):
       UpperCamelCase_					: Tuple            =				parent
       UpperCamelCase_					: List[Any]            =				batch_size
       UpperCamelCase_					: List[str]            =				seq_length
       UpperCamelCase_					: Dict            =				act_dim
       UpperCamelCase_					: Optional[int]            =				state_dim
       UpperCamelCase_					: Dict            =				hidden_size
       UpperCamelCase_					: str            =				max_length
       UpperCamelCase_					: int            =				is_training
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[str]            =				floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
       UpperCamelCase_					: Dict            =				floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
       UpperCamelCase_					: Union[str, Any]            =				floats_tensor((self.batch_size, self.seq_length, 1)			)
       UpperCamelCase_					: List[str]            =				floats_tensor((self.batch_size, self.seq_length, 1)			)
       UpperCamelCase_					: Optional[Any]            =				ids_tensor((self.batch_size, self.seq_length)      ,  vocab_size=10_00			)
       UpperCamelCase_					: Optional[Any]            =				random_attention_mask((self.batch_size, self.seq_length)			)

       UpperCamelCase_					: Tuple            =				self.get_config()

       return (
           config,
           states,
           actions,
           rewards,
           returns_to_go,
           timesteps,
           attention_mask,
       )
 def 	_UpperCAmelCase							(				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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  ):
       UpperCamelCase_					: Tuple            =				DecisionTransformerModel(config=__lowerCAmelCase			)
       model.to(__lowerCAmelCase			)
       model.eval()
       UpperCamelCase_					: str            =				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			):
       UpperCamelCase_					: Dict            =				self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      
       )					: str            =				config_and_inputs
       UpperCamelCase_					: 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     (   SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    Tuple			  =    (DecisionTransformerModel,) if is_torch_available() else ()
 __a      :    Union[str, Any]			  =    ()
 __a      :    int			  =    {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __a      :    int			  =    False
 __a      :    Tuple			  =    False
 __a      :    Dict			  =    False
 __a      :    Union[str, Any]			  =    False
 __a      :    int			  =    False
 __a      :    List[Any]			  =    False
 __a      :    List[str]			  =    False
 __a      :    Dict			  =    False
 __a      :    Optional[Any]			  =    False
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				DecisionTransformerModelTester(self			)
       UpperCamelCase_					: Dict            =				ConfigTester(self      ,  config_class=__lowerCAmelCase      ,  hidden_size=37			)
 def 	_UpperCAmelCase							(				self			):
       self.config_tester.run_common_tests()
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[int]            =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCAmelCase			)
 @slow
 def 	_UpperCAmelCase							(				self			):
       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_					: Union[str, Any]            =				DecisionTransformerModel.from_pretrained(__lowerCAmelCase			)
             self.assertIsNotNone(__lowerCAmelCase			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_     ,      UpperCamelCase_					: str            =				self.model_tester.prepare_config_and_inputs_for_common()

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

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

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






@require_torch
class      A     (   unittest.TestCase    ):






 """simple docstring"""





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

       UpperCamelCase_					: List[str]            =				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			)

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

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

             UpperCamelCase_					: Union[str, Any]            =				torch.ones(1      ,  states.shape[1]			).to(dtype=torch.long      ,  device=states.device			)

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

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






