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:
     _SCREAMING_SNAKE_CASE   		=			None


_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE   		=			{
    """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"""
        ),
    },
}

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

# fmt: off
_SCREAMING_SNAKE_CASE   		=			["""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 SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							VOCAB_FILES_NAMES
   __lowerCAmelCase											=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase											=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase											=							["""input_ids""", """attention_mask"""]
   __lowerCAmelCase											=							MBartaaTokenizer

   __lowerCAmelCase											=							[]
   __lowerCAmelCase											=							[]







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





    """simple docstring"""


    UpperCamelCase      =			AddedToken(lowerCamelCase_      ,      lstrip=lowerCamelCase_      ,      rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_      ,      lowerCamelCase_							) 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__(
        lowerCamelCase_      ,      src_lang=lowerCamelCase_      ,      tgt_lang=lowerCamelCase_      ,      tokenizer_file=lowerCamelCase_      ,      eos_token=lowerCamelCase_      ,      sep_token=lowerCamelCase_      ,      cls_token=lowerCamelCase_      ,      unk_token=lowerCamelCase_      ,      pad_token=lowerCamelCase_      ,      mask_token=lowerCamelCase_      ,      **lowerCamelCase_      ,      )

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

    UpperCamelCase      =			{
        lang_code: self.convert_tokens_to_ids(lowerCamelCase_							) 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 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    return self._src_lang







   @src_lang.setter
   def 	lowerCamelCase_  (    self	:	List[Any]      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			new_src_lang
    self.set_src_lang_special_tokens(self._src_lang							)







   def 	lowerCamelCase_  (    self	:	str      ,      lowerCamelCase_	:	List[int]      ,      lowerCamelCase_	:	Optional[List[int]] = 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 	lowerCamelCase_  (    self	:	str      ,      lowerCamelCase_	:	List[str]      ,      lowerCamelCase_	:	str = "en_XX"      ,      lowerCamelCase_	:	Optional[List[str]] = None      ,      lowerCamelCase_	:	str = "ro_RO"      ,      **lowerCamelCase_	:	Any      ,      ):





    """simple docstring"""


    UpperCamelCase      =			src_lang
    UpperCamelCase      =			tgt_lang
    return super().prepare_seqaseq_batch(lowerCamelCase_      ,      lowerCamelCase_      ,      **lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    return self.set_src_lang_special_tokens(self.src_lang							)







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    return self.set_tgt_lang_special_tokens(self.tgt_lang							)







   def 	lowerCamelCase_  (    self	:	Any      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			self.convert_tokens_to_ids(lowerCamelCase_							)
    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 	lowerCamelCase_  (    self	:	Optional[Any]      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			self.convert_tokens_to_ids(lowerCamelCase_							)
    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 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	Tuple      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Optional[str]      ,      lowerCamelCase_	:	Optional[str]      ,      **lowerCamelCase_	:	int							):





    """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(lowerCamelCase_      ,      add_special_tokens=lowerCamelCase_      ,      return_tensors=lowerCamelCase_      ,      **lowerCamelCase_							)
    UpperCamelCase      =			self.convert_tokens_to_ids(lowerCamelCase_							)
    UpperCamelCase      =			tgt_lang_id
    return inputs







   def 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Optional[str] = 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
    UpperCamelCase      =			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,)

import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
     import transformer_engine.pytorch as te





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_=True       ,				UpperCamelCase_=True       ,				UpperCamelCase_=True					)		->  int:







 '''simple docstring'''
 if not is_fpa_available():
  raise ImportError("""Using `convert_model` requires transformer_engine to be installed."""					)
 for name, module in model.named_children():
  if isinstance(UpperCamelCase_       ,				nn.Linear					) and to_transformer_engine and _convert_linear:
   # Return early if the linear layer weights are not multiples of 16
   if any(p % 16 != 0 for p in module.weight.shape					):
    return
   UpperCamelCase      =			module.bias is not None
   UpperCamelCase      =			te.Linear(
       module.in_features       ,				module.out_features       ,				bias=UpperCamelCase_       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   if has_bias:
    UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				nn.LayerNorm					) and to_transformer_engine and _convert_ln:
   UpperCamelCase      =			te.LayerNorm(module.normalized_shape[0]       ,				eps=module.eps       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				te.Linear					) and not to_transformer_engine and _convert_linear:
   UpperCamelCase      =			module.bias is not None
   UpperCamelCase      =			nn.Linear(
       module.in_features       ,				module.out_features       ,				bias=UpperCamelCase_       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   if has_bias:
    UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				te.LayerNorm					) and not to_transformer_engine and _convert_ln:
   UpperCamelCase      =			nn.LayerNorm(module.normalized_shape[0]       ,				eps=module.eps       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  else:
   convert_model(
       UpperCamelCase_       ,				to_transformer_engine=UpperCamelCase_       ,				_convert_linear=_convert_linear       ,				_convert_ln=_convert_ln       ,				)





def  lowercase(  UpperCamelCase_					)		->  Any:







 '''simple docstring'''
 if not is_fpa_available():
  raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed."""					)
 for m in model.modules():
  if isinstance(UpperCamelCase_       ,				(te.LayerNorm, te.Linear)					):
   return True
 return False
