


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_mbart import MBartTokenizer
else:
 _SCREAMING_SNAKE_CASE      :			int      				=   None


_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger(__name__)


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

_SCREAMING_SNAKE_CASE      :			str      				=   {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   {
    "facebook/mbart-large-en-ro": 10_24,
    "facebook/mbart-large-cc25": 10_24,
}

# fmt: off
_SCREAMING_SNAKE_CASE      :			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"]

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		VOCAB_FILES_NAMES
    __magic_name__        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__        =		PRETRAINED_VOCAB_FILES_MAP
    __magic_name__        =		['input_ids', 'attention_mask']
    __magic_name__        =		MBartTokenizer

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

         super().__init__(
             vocab_file=__snake_case      , tokenizer_file=__snake_case      , bos_token=__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      , src_lang=__snake_case      , tgt_lang=__snake_case      , additional_special_tokens=__snake_case      , **__snake_case      , )

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

         snake_case					=    FAIRSEQ_LANGUAGE_CODES.copy()

         if additional_special_tokens is not None:
              # Only add those special tokens if they are not already there.
              _additional_special_tokens.extend(
                  [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

         self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}   )
         snake_case					=    {
             lang_code: self.convert_tokens_to_ids(__snake_case   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         snake_case					=    src_lang if src_lang is not None else '''en_XX'''
         snake_case					=    self.convert_tokens_to_ids(self._src_lang   )
         snake_case					=    tgt_lang
         self.set_src_lang_special_tokens(self._src_lang   )
    @property
    def 		a_      (	self   ):
         return self._src_lang
    @src_lang.setter
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    new_src_lang
         self.set_src_lang_special_tokens(self._src_lang   )
    def 		a_      (	self      , __snake_case      , __snake_case = 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 		a_      (	self      , __snake_case      , __snake_case = None   ):
         snake_case					=    [self.sep_token_id]
         snake_case					=    [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]
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , **__snake_case   ):
         if src_lang is None or tgt_lang is None:
              raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''   )
         snake_case					=    src_lang
         snake_case					=    self(__snake_case      , add_special_tokens=__snake_case      , return_tensors=__snake_case      , **__snake_case   )
         snake_case					=    self.convert_tokens_to_ids(__snake_case   )
         snake_case					=    tgt_lang_id
         return inputs
    def 		a_      (	self      , __snake_case      , __snake_case = "en_XX"      , __snake_case = None      , __snake_case = "ro_RO"      , **__snake_case      , ):
         snake_case					=    src_lang
         snake_case					=    tgt_lang
         return super().prepare_seqaseq_batch(__snake_case      , __snake_case      , **__snake_case   )
    def 		a_      (	self   ):
         return self.set_src_lang_special_tokens(self.src_lang   )
    def 		a_      (	self   ):
         return self.set_tgt_lang_special_tokens(self.tgt_lang   )
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    self.convert_tokens_to_ids(__snake_case   )
         snake_case					=    []
         snake_case					=    [self.eos_token_id, self.cur_lang_code]

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

         snake_case					=    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      , __snake_case   ):
         snake_case					=    self.convert_tokens_to_ids(__snake_case   )
         snake_case					=    []
         snake_case					=    [self.eos_token_id, self.cur_lang_code]

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

         snake_case					=    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      , __snake_case      , __snake_case = 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(__snake_case   ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''   )
              return
         snake_case					=    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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'mgp-str'
    def __init__(	self      , __snake_case=[3_2, 1_2_8]      , __snake_case=4      , __snake_case=3      , __snake_case=2_7      , __snake_case=3_8      , __snake_case=5_0_2_5_7      , __snake_case=3_0_5_2_2      , __snake_case=7_6_8      , __snake_case=1_2      , __snake_case=1_2      , __snake_case=4.0      , __snake_case=True      , __snake_case=False      , __snake_case=1E-5      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=False      , __snake_case=0.02      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    image_size
         snake_case					=    patch_size
         snake_case					=    num_channels
         snake_case					=    max_token_length
         snake_case					=    num_character_labels
         snake_case					=    num_bpe_labels
         snake_case					=    num_wordpiece_labels
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    mlp_ratio
         snake_case					=    distilled
         snake_case					=    layer_norm_eps
         snake_case					=    drop_rate
         snake_case					=    qkv_bias
         snake_case					=    attn_drop_rate
         snake_case					=    drop_path_rate
         snake_case					=    output_aa_attentions
         snake_case					=    initializer_range




