





import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


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

_lowerCamelCase :    int  							= {
    '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
    # See all BART models at https://huggingface.co/models?filter=bart
}


class 						lowerCamelCase       (A__      ):







       """simple docstring"""




       UpperCAmelCase_      						=   "bart"
       UpperCAmelCase_      						=   ["past_key_values"]
       UpperCAmelCase_      						=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






       def __init__( self     :					Any,			_UpperCAmelCase     :					Union[str, Any]=5_0_2_6_5,			_UpperCAmelCase     :					Tuple=1_0_2_4,			_UpperCAmelCase     :					Union[str, Any]=1_2,			_UpperCAmelCase     :					List[Any]=4_0_9_6,			_UpperCAmelCase     :					Any=1_6,			_UpperCAmelCase     :					Union[str, Any]=1_2,			_UpperCAmelCase     :					List[str]=4_0_9_6,			_UpperCAmelCase     :					Union[str, Any]=1_6,			_UpperCAmelCase     :					Tuple=0.0,			_UpperCAmelCase     :					List[Any]=0.0,			_UpperCAmelCase     :					int="gelu",			_UpperCAmelCase     :					Union[str, Any]=1_0_2_4,			_UpperCAmelCase     :					Optional[Any]=0.1,			_UpperCAmelCase     :					List[str]=0.0,			_UpperCAmelCase     :					Optional[Any]=0.0,			_UpperCAmelCase     :					List[str]=0.02,			_UpperCAmelCase     :					str=0.0,			_UpperCAmelCase     :					Any=False,			_UpperCAmelCase     :					List[str]=True,			_UpperCAmelCase     :					Dict=3,			_UpperCAmelCase     :					Union[str, Any]=1,			_UpperCAmelCase     :					List[str]=0,			_UpperCAmelCase     :					Any=2,			_UpperCAmelCase     :					Any=True,			_UpperCAmelCase     :					List[str]=2,			_UpperCAmelCase     :					Tuple=2,			**_UpperCAmelCase     :					List[str],			)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Dict     =						vocab_size
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						max_position_embeddings
              SCREAMING_SNAKE_CASE__       :      Any     =						d_model
              SCREAMING_SNAKE_CASE__       :      List[str]     =						encoder_ffn_dim
              SCREAMING_SNAKE_CASE__       :      Any     =						encoder_layers
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						encoder_attention_heads
              SCREAMING_SNAKE_CASE__       :      str     =						decoder_ffn_dim
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						decoder_layers
              SCREAMING_SNAKE_CASE__       :      int     =						decoder_attention_heads
              SCREAMING_SNAKE_CASE__       :      List[str]     =						dropout
              SCREAMING_SNAKE_CASE__       :      List[str]     =						attention_dropout
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						activation_dropout
              SCREAMING_SNAKE_CASE__       :      Tuple     =						activation_function
              SCREAMING_SNAKE_CASE__       :      List[str]     =						init_std
              SCREAMING_SNAKE_CASE__       :      Tuple     =						encoder_layerdrop
              SCREAMING_SNAKE_CASE__       :      Any     =						decoder_layerdrop
              SCREAMING_SNAKE_CASE__       :      int     =						classifier_dropout
              SCREAMING_SNAKE_CASE__       :      Tuple     =						use_cache
              SCREAMING_SNAKE_CASE__       :      Any     =						encoder_layers
              SCREAMING_SNAKE_CASE__       :      str     =						scale_embedding  # scale factor will be sqrt(d_model) if True

              super().__init__(
                  num_labels=__snake_case,			pad_token_id=__snake_case,			bos_token_id=__snake_case,			eos_token_id=__snake_case,			is_encoder_decoder=__snake_case,			decoder_start_token_id=__snake_case,			forced_eos_token_id=__snake_case,			**__snake_case,			)

              # ensure backward compatibility for BART CNN models
              if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated",			__snake_case						):
                     SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.bos_token_id
                     warnings.warn(
                         F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                         "The config can simply be saved and uploaded again to be fixed."						)


class 						lowerCamelCase       (A__      ):







       """simple docstring"""




       @property
       def  A_     ( self     :					int						)				->			Mapping[str, Mapping[int, str]]:





              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     SCREAMING_SNAKE_CASE__       :      Any     =						OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                         ]						)

                     if self.use_past:
                            SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{0: "batch"}
                            SCREAMING_SNAKE_CASE__       :      Dict     =						{0: "batch", 1: "past_decoder_sequence + sequence"}
                     else:
                            SCREAMING_SNAKE_CASE__       :      int     =						{0: "batch", 1: "decoder_sequence"}
                            SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{0: "batch", 1: "decoder_sequence"}

                     if self.use_past:
                            self.fill_with_past_key_values_(__snake_case,			direction="inputs"						)
              elif self.task == "causal-lm":
                     # TODO: figure this case out.
                     SCREAMING_SNAKE_CASE__       :      str     =						OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                         ]						)
                     if self.use_past:
                            SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.num_layers
                            for i in range(__snake_case						):
                                   SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{0: "batch", 2: "past_sequence + sequence"}
                                   SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{0: "batch", 2: "past_sequence + sequence"}
              else:
                     SCREAMING_SNAKE_CASE__       :      Dict     =						OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                             ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                             ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                         ]						)

              return common_inputs






       @property
       def  A_     ( self     :					int						)				->			Mapping[str, Mapping[int, str]]:





              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     SCREAMING_SNAKE_CASE__       :      Dict     =						super().outputs
              else:
                     SCREAMING_SNAKE_CASE__       :      List[str]     =						super(__snake_case,			self						).outputs
                     if self.use_past:
                            SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.num_layers
                            for i in range(__snake_case						):
                                   SCREAMING_SNAKE_CASE__       :      List[str]     =						{0: "batch", 2: "past_sequence + sequence"}
                                   SCREAMING_SNAKE_CASE__       :      List[Any]     =						{0: "batch", 2: "past_sequence + sequence"}
              return common_outputs






       def  A_     ( self     :					int,			_UpperCAmelCase     :					PreTrainedTokenizer,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					Optional[TensorType] = None,			)				->			Mapping[str, Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  __snake_case,			__snake_case,			__snake_case,			__snake_case,			__snake_case						)

              # Generate decoder inputs
              SCREAMING_SNAKE_CASE__       :      Dict     =						seq_length if not self.use_past else 1
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  __snake_case,			__snake_case,			__snake_case,			__snake_case,			__snake_case						)
              SCREAMING_SNAKE_CASE__       :      Tuple     =						{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						dict(**__snake_case,			**__snake_case						)

              if self.use_past:
                     if not is_torch_available():
                            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
                     else:
                            import torch
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						common_inputs["input_ids"].shape
                     SCREAMING_SNAKE_CASE__       :      str     =						common_inputs["decoder_input_ids"].shape[1]
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						self.num_attention_heads
                     SCREAMING_SNAKE_CASE__       :      List[str]     =						(
                         batch,
                         num_encoder_attention_heads,
                         encoder_seq_length,
                         self._config.hidden_size // num_encoder_attention_heads,
                     )
                     SCREAMING_SNAKE_CASE__       :      List[str]     =						decoder_seq_length + 3
                     SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						(
                         batch,
                         num_decoder_attention_heads,
                         decoder_past_length,
                         self._config.hidden_size // num_decoder_attention_heads,
                     )

                     SCREAMING_SNAKE_CASE__       :      str     =						torch.cat(
                         [common_inputs["decoder_attention_mask"], torch.ones(__snake_case,			__snake_case						)],			dim=1						)

                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						[]
                     # If the number of encoder and decoder layers are present in the model configuration, both are considered
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.num_layers
                     SCREAMING_SNAKE_CASE__       :      Dict     =						min(__snake_case,			__snake_case						)
                     SCREAMING_SNAKE_CASE__       :      str     =						max(__snake_case,			__snake_case						) - min_num_layers
                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

                     for _ in range(__snake_case						):
                            common_inputs["past_key_values"].append(
                                (
                                    torch.zeros(__snake_case						),
                                    torch.zeros(__snake_case						),
                                    torch.zeros(__snake_case						),
                                    torch.zeros(__snake_case						),
                                )						)
                     # TODO: test this.
                     SCREAMING_SNAKE_CASE__       :      Tuple     =						encoder_shape if remaining_side_name == "encoder" else decoder_shape
                     for _ in range(__snake_case,			__snake_case						):
                            common_inputs["past_key_values"].append((torch.zeros(__snake_case						), torch.zeros(__snake_case						))						)
              return common_inputs






       def  A_     ( self     :					Any,			_UpperCAmelCase     :					PreTrainedTokenizer,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					Optional[TensorType] = None,			)				->			Mapping[str, Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      str     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  __snake_case,			__snake_case,			__snake_case,			__snake_case,			__snake_case						)

              if self.use_past:
                     if not is_torch_available():
                            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
                     else:
                            import torch
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						common_inputs["input_ids"].shape
                     # Not using the same length for past_key_values
                     SCREAMING_SNAKE_CASE__       :      Dict     =						seqlen + 2
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      str     =						self.num_layers
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.num_attention_heads
                     SCREAMING_SNAKE_CASE__       :      str     =						(
                         batch,
                         num_encoder_attention_heads,
                         past_key_values_length,
                         self._config.hidden_size // num_encoder_attention_heads,
                     )

                     SCREAMING_SNAKE_CASE__       :      Any     =						common_inputs["attention_mask"].dtype
                     SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.cat(
                         [common_inputs["attention_mask"], torch.ones(__snake_case,			__snake_case,			dtype=__snake_case						)],			dim=1						)
                     SCREAMING_SNAKE_CASE__       :      int     =						[
                         (torch.zeros(__snake_case						), torch.zeros(__snake_case						)) for _ in range(__snake_case						)
                     ]
              return common_inputs






       def  A_     ( self     :					List[str],			_UpperCAmelCase     :					PreTrainedTokenizer,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					Optional[TensorType] = None,			)				->			Mapping[str, Any]:





              """simple docstring"""
              # Copied from OnnxConfig.generate_dummy_inputs
              # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
              # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						compute_effective_axis_dimension(
                  __snake_case,			fixed_dimension=OnnxConfig.default_fixed_batch,			num_token_to_add=0						)

              # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
              SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer.num_special_tokens_to_add(__snake_case						)
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						compute_effective_axis_dimension(
                  __snake_case,			fixed_dimension=OnnxConfig.default_fixed_sequence,			num_token_to_add=__snake_case						)

              # Generate dummy inputs according to compute batch and sequence
              SCREAMING_SNAKE_CASE__       :      List[str]     =						[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
              SCREAMING_SNAKE_CASE__       :      int     =						dict(tokenizer(__snake_case,			return_tensors=__snake_case						)						)
              return common_inputs






       def  A_     ( self     :					Dict,			_UpperCAmelCase     :					PreTrainedTokenizer,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					Optional[TensorType] = None,			)				->			Mapping[str, Any]:





              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     SCREAMING_SNAKE_CASE__       :      List[str]     =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                         __snake_case,			batch_size=__snake_case,			seq_length=__snake_case,			is_pair=__snake_case,			framework=__snake_case						)

              elif self.task == "causal-lm":
                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						self._generate_dummy_inputs_for_causal_lm(
                         __snake_case,			batch_size=__snake_case,			seq_length=__snake_case,			is_pair=__snake_case,			framework=__snake_case						)
              else:
                     SCREAMING_SNAKE_CASE__       :      Tuple     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                         __snake_case,			batch_size=__snake_case,			seq_length=__snake_case,			is_pair=__snake_case,			framework=__snake_case						)

              return common_inputs






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





              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     SCREAMING_SNAKE_CASE__       :      Tuple     =						super()._flatten_past_key_values_(__snake_case,			__snake_case,			__snake_case,			__snake_case						)
              else:
                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						super(__snake_case,			self						)._flatten_past_key_values_(
                         __snake_case,			__snake_case,			__snake_case,			__snake_case						)













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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase :    int  							= logging.get_logger(__name__)

_lowerCamelCase :    Optional[Any]  							= '''▁'''

_lowerCamelCase :    Dict  							= {'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowerCamelCase :    int  							= {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowerCamelCase :    Optional[Any]  							= {
    '''xlm-roberta-base''': 5_1_2,
    '''xlm-roberta-large''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_1_2,
}


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   VOCAB_FILES_NAMES
							UpperCAmelCase_      						=   PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_      						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_      						=   ["input_ids", "attention_mask"]






							def __init__( self     :					Dict,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[int]="<s>",			_UpperCAmelCase     :					Optional[int]="</s>",			_UpperCAmelCase     :					Dict="</s>",			_UpperCAmelCase     :					List[Any]="<s>",			_UpperCAmelCase     :					Union[str, Any]="<unk>",			_UpperCAmelCase     :					List[Any]="<pad>",			_UpperCAmelCase     :					str="<mask>",			_UpperCAmelCase     :					Optional[Dict[str, Any]] = None,			**_UpperCAmelCase     :					List[Any],			)				->			None:





														"""simple docstring"""
														# Mask token behave like a normal word, i.e. include the space before it
														SCREAMING_SNAKE_CASE__       :      int     =						AddedToken(_UpperCAmelCase,			lstrip=_UpperCAmelCase,			rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase,			_UpperCAmelCase						) else mask_token

														SCREAMING_SNAKE_CASE__       :      List[str]     =						{} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=_UpperCAmelCase,			eos_token=_UpperCAmelCase,			unk_token=_UpperCAmelCase,			sep_token=_UpperCAmelCase,			cls_token=_UpperCAmelCase,			pad_token=_UpperCAmelCase,			mask_token=_UpperCAmelCase,			sp_model_kwargs=self.sp_model_kwargs,			**_UpperCAmelCase,			)

														SCREAMING_SNAKE_CASE__       :      str     =						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
														self.sp_model.Load(str(_UpperCAmelCase						)						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						vocab_file

														# Original fairseq vocab and spm vocab must be "aligned":
														# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
														# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
														# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
														# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

														# Mimic fairseq token-to-id alignment for the first 4 token
														SCREAMING_SNAKE_CASE__       :      List[str]     =						{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

														# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
														SCREAMING_SNAKE_CASE__       :      Dict     =						1

														SCREAMING_SNAKE_CASE__       :      int     =						len(self.sp_model						) + self.fairseq_offset
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}






							def __getstate__( self     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.__dict__.copy()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						None
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.sp_model.serialized_model_proto()
														return state






							def __setstate__( self     :					int,			_UpperCAmelCase     :					List[Any]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						d

														# for backward compatibility
														if not hasattr(self,			"sp_model_kwargs"						):
																					SCREAMING_SNAKE_CASE__       :      Dict     =						{}

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
														self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""

														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[self.cls_token_id]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[self.sep_token_id]
														return cls + token_ids_a + sep + sep + token_ids_a + sep






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None,			_UpperCAmelCase     :					bool = False						)				->			List[int]:





														"""simple docstring"""

														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=_UpperCAmelCase,			token_ids_a=_UpperCAmelCase,			already_has_special_tokens=_UpperCAmelCase						)

														if token_ids_a is None:
																					return [1] + ([0] * len(_UpperCAmelCase						)) + [1]
														return [1] + ([0] * len(_UpperCAmelCase						)) + [1, 1] + ([0] * len(_UpperCAmelCase						)) + [1]






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						[self.sep_token_id]
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[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 + sep + token_ids_a + sep						) * [0]






							@property
							def  A_     ( self     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														return len(self.sp_model						) + self.fairseq_offset + 1  # Add the <mask> token






							def  A_     ( self     :					List[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						{self.convert_ids_to_tokens(_UpperCAmelCase						): i for i in range(self.vocab_size						)}
														vocab.update(self.added_tokens_encoder						)
														return vocab






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					str						)				->			List[str]:





														"""simple docstring"""
														return self.sp_model.encode(_UpperCAmelCase,			out_type=_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Union[str, Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														if token in self.fairseq_tokens_to_ids:
																					return self.fairseq_tokens_to_ids[token]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.sp_model.PieceToId(_UpperCAmelCase						)

														# Need to return unknown token if the SP model returned 0
														return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														if index in self.fairseq_ids_to_tokens:
																					return self.fairseq_ids_to_tokens[index]
														return self.sp_model.IdToPiece(index - self.fairseq_offset						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					int						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						"".join(_UpperCAmelCase						).replace(_UpperCAmelCase,			" "						).strip()
														return out_string






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[str] = None						)				->			Tuple[str]:





														"""simple docstring"""
														if not os.path.isdir(_UpperCAmelCase						):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
																					return
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						os.path.join(
														    _UpperCAmelCase,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

														if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						) and os.path.isfile(self.vocab_file						):
																					copyfile(self.vocab_file,			_UpperCAmelCase						)
														elif not os.path.isfile(self.vocab_file						):
																					with open(_UpperCAmelCase,			"wb"						) as fi:
																												SCREAMING_SNAKE_CASE__       :      Any     =						self.sp_model.serialized_model_proto()
																												fi.write(_UpperCAmelCase						)

														return (out_vocab_file,)






