


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

from tokenizers import processors

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


if is_sentencepiece_available():
						from .tokenization_mbartaa import MBartaaTokenizer
else:
						__UpperCamelCase      						=    None


__UpperCamelCase      						=    logging.get_logger(__name__)

__UpperCamelCase      						=    {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase      						=    {
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

__UpperCamelCase      						=    {
    'facebook/mbart-large-50-one-to-many-mmt': 1_0_2_4,
}

# fmt: off
__UpperCamelCase      						=    ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			_UpperCamelCase						:   List[str]					=						VOCAB_FILES_NAMES
			_UpperCamelCase						:   int					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase						:   Optional[int]					=						PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase						:   Any					=						['input_ids', 'attention_mask']
			_UpperCamelCase						:   Optional[Any]					=						MBartaaTokenizer

			_UpperCamelCase						:   List[int]					=						[]
			_UpperCamelCase						:   List[int]					=						[]




			def __init__(							self ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  snake_case="</s>" ,  snake_case="</s>" ,  snake_case="<s>" ,  snake_case="<unk>" ,  snake_case="<pad>" ,  snake_case="<mask>" ,  **snake_case ,  ):

						'''simple docstring'''


						UpperCamelCase__   =    AddedToken(snake_case ,  lstrip=snake_case ,  rstrip=snake_case       ) if isinstance(snake_case ,  snake_case       ) else mask_token

						UpperCamelCase__   =    kwargs.get("additional_special_tokens" ,  []       )
						kwargs["additional_special_tokens"] += [
						    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
						]

						super().__init__(
						    snake_case ,  src_lang=snake_case ,  tgt_lang=snake_case ,  tokenizer_file=snake_case ,  eos_token=snake_case ,  sep_token=snake_case ,  cls_token=snake_case ,  unk_token=snake_case ,  pad_token=snake_case ,  mask_token=snake_case ,  **snake_case ,  )

						UpperCamelCase__   =    vocab_file
						UpperCamelCase__   =    False if not self.vocab_file else True

						UpperCamelCase__   =    {
						    lang_code: self.convert_tokens_to_ids(snake_case       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						UpperCamelCase__   =    src_lang if src_lang is not None else "en_XX"
						UpperCamelCase__   =    tgt_lang
						UpperCamelCase__   =    self.lang_code_to_id[self._src_lang]
						self.set_src_lang_special_tokens(self._src_lang       )




			@property
			def 			snake_case__      (							self       ):

						'''simple docstring'''


						return self._src_lang




			@src_lang.setter
			def 			snake_case__      (							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    new_src_lang
						self.set_src_lang_special_tokens(self._src_lang       )




			def 			snake_case__      (							self ,  snake_case ,  snake_case = None       ):

						'''simple docstring'''


						if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + self.suffix_tokens
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




			def 			snake_case__      (							self ,  snake_case ,  snake_case = "en_XX" ,  snake_case = None ,  snake_case = "ro_RO" ,  **snake_case ,  ):

						'''simple docstring'''


						UpperCamelCase__   =    src_lang
						UpperCamelCase__   =    tgt_lang
						return super().prepare_seqaseq_batch(snake_case ,  snake_case ,  **snake_case       )




			def 			snake_case__      (							self       ):

						'''simple docstring'''


						return self.set_src_lang_special_tokens(self.src_lang       )




			def 			snake_case__      (							self       ):

						'''simple docstring'''


						return self.set_tgt_lang_special_tokens(self.tgt_lang       )




			def 			snake_case__      (							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.convert_tokens_to_ids(snake_case       )
						UpperCamelCase__   =    [self.cur_lang_code_id]
						UpperCamelCase__   =    [self.eos_token_id]

						UpperCamelCase__   =    self.convert_ids_to_tokens(self.prefix_tokens       )
						UpperCamelCase__   =    self.convert_ids_to_tokens(self.suffix_tokens       )

						UpperCamelCase__   =    processors.TemplateProcessing(
						    single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,  self.prefix_tokens + self.suffix_tokens       )       ) ,  )




			def 			snake_case__      (							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.convert_tokens_to_ids(snake_case       )
						UpperCamelCase__   =    [self.cur_lang_code_id]
						UpperCamelCase__   =    [self.eos_token_id]

						UpperCamelCase__   =    self.convert_ids_to_tokens(self.prefix_tokens       )
						UpperCamelCase__   =    self.convert_ids_to_tokens(self.suffix_tokens       )

						UpperCamelCase__   =    processors.TemplateProcessing(
						    single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,  self.prefix_tokens + self.suffix_tokens       )       ) ,  )




			def 			snake_case__      (							self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  **snake_case       ):

						'''simple docstring'''


						if src_lang is None or tgt_lang is None:
									raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"       )
						UpperCamelCase__   =    src_lang
						UpperCamelCase__   =    self(snake_case ,  add_special_tokens=snake_case ,  return_tensors=snake_case ,  **snake_case       )
						UpperCamelCase__   =    self.convert_tokens_to_ids(snake_case       )
						UpperCamelCase__   =    tgt_lang_id
						return inputs







			def 			snake_case__      (							self ,  snake_case ,  snake_case = 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(snake_case       ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
									return
						UpperCamelCase__   =    os.path.join(
						    snake_case ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

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

						return (out_vocab_file,)






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


__UpperCamelCase      						=    [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

__UpperCamelCase      						=    get_logger(__name__)


__UpperCamelCase      						=    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}
def     UpperCamelCase_(			_A				:dict					)->						Optional[TaskTemplate]:
			UpperCamelCase__   =    task_template_dict.get("task"					)
			if task_name is None:
						logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_A					)}'''					)
						return None
			UpperCamelCase__   =    NAMEaTEMPLATE.get(_A					)
			return template.from_dict(_A					)


