


'''simple docstring'''

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					:			Optional[Any]				     =					None


_UpperCAmelCase					:			Tuple				     =					logging.get_logger(__name__)

_UpperCAmelCase					:			Any				     =					{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase					:			List[Any]				     =					{
    """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					:			Any				     =					{
    """facebook/mbart-large-50-one-to-many-mmt""": 1_0_2_4,
}

# fmt: off
_UpperCAmelCase					:			Union[str, Any]				     =					["""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    a__					(			_snake_case   ):







				"""simple docstring"""



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

				__UpperCamelCase     :		List[int]						=						[]
				__UpperCamelCase     :		List[int]						=						[]




				def __init__(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase="</s>"							,      __lowercase="</s>"							,      __lowercase="<s>"							,      __lowercase="<unk>"							,      __lowercase="<pad>"							,      __lowercase="<mask>"							,      **__lowercase							,      ):
										# Mask token behave like a normal word, i.e. include the space before it
										__lowerCAmelCase											=					AddedToken(_a							,      lstrip=_a							,      rstrip=_a		) if isinstance(_a							,      _a		) else mask_token

										__lowerCAmelCase											=					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__(
										    _a							,      src_lang=_a							,      tgt_lang=_a							,      tokenizer_file=_a							,      eos_token=_a							,      sep_token=_a							,      cls_token=_a							,      unk_token=_a							,      pad_token=_a							,      mask_token=_a							,      **_a							,      )

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

										__lowerCAmelCase											=					{
										    lang_code: self.convert_tokens_to_ids(_a		) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

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




				@property
				def        _snake_case	(self		):
										return self._src_lang




				@src_lang.setter
				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					new_src_lang
										self.set_src_lang_special_tokens(self._src_lang		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase = None		):
										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							,      __lowercase							,      __lowercase = "en_XX"							,      __lowercase = None							,      __lowercase = "ro_RO"							,      **__lowercase							,      ):
										__lowerCAmelCase											=					src_lang
										__lowerCAmelCase											=					tgt_lang
										return super().prepare_seqaseq_batch(_a							,      _a							,      **_a		)




				def        _snake_case	(self		):
										return self.set_src_lang_special_tokens(self.src_lang		)




				def        _snake_case	(self		):
										return self.set_tgt_lang_special_tokens(self.tgt_lang		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					self.convert_tokens_to_ids(_a		)
										__lowerCAmelCase											=					[self.cur_lang_code_id]
										__lowerCAmelCase											=					[self.eos_token_id]

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

										__lowerCAmelCase											=					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							,      __lowercase		):
										__lowerCAmelCase											=					self.convert_tokens_to_ids(_a		)
										__lowerCAmelCase											=					[self.cur_lang_code_id]
										__lowerCAmelCase											=					[self.eos_token_id]

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

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




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

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

										return (out_vocab_file,)


'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_A										= {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= [
     'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RwkvForCausalLM',
     'RwkvModel',
     'RwkvPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rwkv import (
      RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
      RwkvForCausalLM,
      RwkvModel,
      RwkvPreTrainedModel,
  )
else:
 import sys

 _A										= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




