



import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter



class 		_lowercase		(							TensorFormatter[Mapping, np.ndarray, Mapping]						):

   def __init__(   self      :							Optional[Any]				, __lowerCAmelCase      :							List[str]=None				, **__lowerCAmelCase      :							List[str]				)							->					Tuple:


    """simple docstring"""






    super().__init__(features=__lowerCAmelCase				)
    a							=		np_array_kwargs

   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							int				)							->					Optional[Any]:


    """simple docstring"""






    if isinstance(__lowerCAmelCase				, __lowerCAmelCase				):
     if column and all(
         isinstance(__lowerCAmelCase				, np.ndarray				) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column				):
      return np.stack(__lowerCAmelCase				)
     else:
      # don't use np.array(column, dtype=object)
      # since it fails in certain cases
      # see https://stackoverflow.com/q/51005699
      a							=		np.empty(len(__lowerCAmelCase				)				, dtype=__lowerCAmelCase				)
      a							=		column
      return out
    return column

   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							Dict				)							->					List[Any]:


    """simple docstring"""






    if isinstance(__lowerCAmelCase				, (str, bytes, type(__lowerCAmelCase				))				):
     return value
    elif isinstance(__lowerCAmelCase				, (np.character, np.ndarray)				) and np.issubdtype(value.dtype				, np.character				):
     return value
    elif isinstance(__lowerCAmelCase				, np.number				):
     return value

    a							=		{}

    if isinstance(__lowerCAmelCase				, np.ndarray				) and np.issubdtype(value.dtype				, np.integer				):
     a							=		{"dtype": np.intaa}
    elif isinstance(__lowerCAmelCase				, np.ndarray				) and np.issubdtype(value.dtype				, np.floating				):
     a							=		{"dtype": np.floataa}
    elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(__lowerCAmelCase				, PIL.Image.Image				):
      return np.asarray(__lowerCAmelCase				, **self.np_array_kwargs				)

    return np.asarray(__lowerCAmelCase				, **{**default_dtype, **self.np_array_kwargs}				)

   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							Union[str, Any]				)							->					Any:


    """simple docstring"""






    if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(__lowerCAmelCase				, torch.Tensor				):
      return self._tensorize(data_struct.detach().cpu().numpy()[()]				)
    if hasattr(__lowerCAmelCase				, "__array__"				) and not isinstance(__lowerCAmelCase				, (np.ndarray, np.character, np.number)				):
     a							=		data_struct.__array__()
    # support for nested types like struct of list of struct
    if isinstance(__lowerCAmelCase				, np.ndarray				):
     if data_struct.dtype == object:
      return self._consolidate([self.recursive_tensorize(__lowerCAmelCase				) for substruct in data_struct]				)
    if isinstance(__lowerCAmelCase				, (list, tuple)				):
     return self._consolidate([self.recursive_tensorize(__lowerCAmelCase				) for substruct in data_struct]				)
    return self._tensorize(__lowerCAmelCase				)

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							dict				)							->					Optional[int]:


    """simple docstring"""






    return map_nested(self._recursive_tensorize				, __lowerCAmelCase				, map_list=__lowerCAmelCase				)

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							pa.Table				)							->					Mapping:


    """simple docstring"""






    a							=		self.numpy_arrow_extractor().extract_row(__lowerCAmelCase				)
    a							=		self.python_features_decoder.decode_row(__lowerCAmelCase				)
    return self.recursive_tensorize(__lowerCAmelCase				)

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							pa.Table				)							->					np.ndarray:


    """simple docstring"""






    a							=		self.numpy_arrow_extractor().extract_column(__lowerCAmelCase				)
    a							=		self.python_features_decoder.decode_column(__lowerCAmelCase				, pa_table.column_names[0]				)
    a							=		self.recursive_tensorize(__lowerCAmelCase				)
    a							=		self._consolidate(__lowerCAmelCase				)
    return column




   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							pa.Table				)							->					Mapping:


    """simple docstring"""






    a							=		self.numpy_arrow_extractor().extract_batch(__lowerCAmelCase				)
    a							=		self.python_features_decoder.decode_batch(__lowerCAmelCase				)
    a							=		self.recursive_tensorize(__lowerCAmelCase				)
    for column_name in batch:
     a							=		self._consolidate(batch[column_name]				)
    return batch






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:
     A_		:  Any										=  None


A_		:  Tuple										=  logging.get_logger(__name__)

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

A_		:  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'''
        ),
    },
}

A_		:  Optional[Any]										=  {
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
A_		:  List[str]										=  ['''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 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      VOCAB_FILES_NAMES
   _UpperCAmelCase  						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase  						=      PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase  						=      ['''input_ids''', '''attention_mask''']
   _UpperCAmelCase  						=      MBartaaTokenizer

   _UpperCAmelCase  						=      []
   _UpperCAmelCase  						=      []

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


    """simple docstring"""






    a							=		AddedToken(__lowerCAmelCase				, lstrip=__lowerCAmelCase				, rstrip=__lowerCAmelCase				) if isinstance(__lowerCAmelCase				, __lowerCAmelCase				) else mask_token

    a							=		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				, )

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

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

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

   @property
   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    return self._src_lang

   @src_lang.setter
   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							str				)							->					None:


    """simple docstring"""






    a							=		new_src_lang
    self.set_src_lang_special_tokens(self._src_lang				)

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							List[int]				, __lowerCAmelCase      :							Optional[List[int]] = None				)							->					List[int]:


    """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 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							str = "en_XX"				, __lowerCAmelCase      :							Optional[List[str]] = None				, __lowerCAmelCase      :							str = "ro_RO"				, **__lowerCAmelCase      :							Dict				, )							->					BatchEncoding:


    """simple docstring"""






    a							=		src_lang
    a							=		tgt_lang
    return super().prepare_seqaseq_batch(__lowerCAmelCase				, __lowerCAmelCase				, **__lowerCAmelCase				)

   def 		A				(   self      :							List[str]				)							->					List[str]:


    """simple docstring"""






    return self.set_src_lang_special_tokens(self.src_lang				)

   def 		A				(   self      :							str				)							->					int:


    """simple docstring"""






    return self.set_tgt_lang_special_tokens(self.tgt_lang				)

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							str				)							->					None:


    """simple docstring"""






    a							=		self.convert_tokens_to_ids(__lowerCAmelCase				)
    a							=		[self.cur_lang_code_id]
    a							=		[self.eos_token_id]

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

    a							=		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 		A				(   self      :							List[str]				, __lowerCAmelCase      :							str				)							->					None:


    """simple docstring"""






    a							=		self.convert_tokens_to_ids(__lowerCAmelCase				)
    a							=		[self.cur_lang_code_id]
    a							=		[self.eos_token_id]

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

    a							=		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 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Optional[str]				, __lowerCAmelCase      :							Optional[str]				, **__lowerCAmelCase      :							Any				)							->					Union[str, Any]:


    """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"				)
    a							=		src_lang
    a							=		self(__lowerCAmelCase				, add_special_tokens=__lowerCAmelCase				, return_tensors=__lowerCAmelCase				, **__lowerCAmelCase				)
    a							=		self.convert_tokens_to_ids(__lowerCAmelCase				)
    a							=		tgt_lang_id
    return inputs




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


    """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
    a							=		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,)

