


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:
			_snake_case					:		Dict      =    None


_snake_case					:		List[str]      =    logging.get_logger(__name__)


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

_snake_case					:		Tuple      =    {
    "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",
    },
}

_snake_case					:		Optional[Any]      =    {
    "facebook/mbart-large-en-ro": 1_024,
    "facebook/mbart-large-cc25": 1_024,
}

# fmt: off
_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		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any					  =  VOCAB_FILES_NAMES
							__UpperCAmelCase   :							List[str]					  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase   :							List[str]					  =  PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase   :							Optional[int]					  =  ["input_ids", "attention_mask"]
							__UpperCAmelCase   :							Tuple					  =  MBartTokenizer

							__UpperCAmelCase   :							List[int]					  =  []
							__UpperCAmelCase   :							List[int]					  =  []

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

														super().__init__(
														    vocab_file=lowerCamelCase   ,     tokenizer_file=lowerCamelCase   ,     bos_token=lowerCamelCase   ,     eos_token=lowerCamelCase   ,     sep_token=lowerCamelCase   ,     cls_token=lowerCamelCase   ,     unk_token=lowerCamelCase   ,     pad_token=lowerCamelCase   ,     mask_token=lowerCamelCase   ,     src_lang=lowerCamelCase   ,     tgt_lang=lowerCamelCase   ,     additional_special_tokens=lowerCamelCase   ,     **lowerCamelCase   ,     )

														__snake_case		:      Optional[int]										=       vocab_file
														__snake_case		:      Tuple										=       False if not self.vocab_file else True

														__snake_case		:      Tuple										=       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		:      Tuple										=       {
														    lang_code: self.convert_tokens_to_ids(lowerCamelCase    ) for lang_code in FAIRSEQ_LANGUAGE_CODES
														}

														__snake_case		:      Dict										=       src_lang if src_lang is not None else "en_XX"
														__snake_case		:      int										=       self.convert_tokens_to_ids(self._src_lang    )
														__snake_case		:      str										=       tgt_lang
														self.set_src_lang_special_tokens(self._src_lang    )

							@property
							def    __snake_case (							self	:  Optional[int]    )     ->   str:
														return self._src_lang

							@src_lang.setter
							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  str    )     ->   None:
														__snake_case		:      Any										=       new_src_lang
														self.set_src_lang_special_tokens(self._src_lang    )

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  Optional[List[int]] = None    )     ->   List[int]:
														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	:  Any   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  Optional[List[int]] = None    )     ->   List[int]:
														__snake_case		:      List[str]										=       [self.sep_token_id]
														__snake_case		:      Optional[Any]										=       [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    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  Optional[str]   ,     lowerCamelCase	:  Optional[str]   ,     **lowerCamelCase	:  List[Any]    )     ->   int:
														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		:      str										=       src_lang
														__snake_case		:      Optional[int]										=       self(lowerCamelCase   ,     add_special_tokens=lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )
														__snake_case		:      Dict										=       self.convert_tokens_to_ids(lowerCamelCase    )
														__snake_case		:      Dict										=       tgt_lang_id
														return inputs

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  str = "en_XX"   ,     lowerCamelCase	:  Optional[List[str]] = None   ,     lowerCamelCase	:  str = "ro_RO"   ,     **lowerCamelCase	:  Any   ,     )     ->   BatchEncoding:
														__snake_case		:      Any										=       src_lang
														__snake_case		:      Optional[int]										=       tgt_lang
														return super().prepare_seqaseq_batch(lowerCamelCase   ,     lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Dict    )     ->   int:
														return self.set_src_lang_special_tokens(self.src_lang    )

							def    __snake_case (							self	:  Tuple    )     ->   List[Any]:
														return self.set_tgt_lang_special_tokens(self.tgt_lang    )

							def    __snake_case (							self	:  Union[str, Any]   ,     lowerCamelCase	:  List[str]    )     ->   None:
														__snake_case		:      str										=       self.convert_tokens_to_ids(lowerCamelCase    )
														__snake_case		:      List[Any]										=       []
														__snake_case		:      str										=       [self.eos_token_id, self.cur_lang_code]

														__snake_case		:      Union[str, Any]										=       self.convert_ids_to_tokens(self.prefix_tokens    )
														__snake_case		:      Tuple										=       self.convert_ids_to_tokens(self.suffix_tokens    )

														__snake_case		:      Union[str, Any]										=       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	:  Optional[int]   ,     lowerCamelCase	:  str    )     ->   None:
														__snake_case		:      List[Any]										=       self.convert_tokens_to_ids(lowerCamelCase    )
														__snake_case		:      Dict										=       []
														__snake_case		:      List[Any]										=       [self.eos_token_id, self.cur_lang_code]

														__snake_case		:      Optional[Any]										=       self.convert_ids_to_tokens(self.prefix_tokens    )
														__snake_case		:      Dict										=       self.convert_ids_to_tokens(self.suffix_tokens    )

														__snake_case		:      Optional[int]										=       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	:  Tuple   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  Optional[str] = None    )     ->   Tuple[str]:
														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
														__snake_case		:      int										=       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,)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Any      =    logging.get_logger(__name__)

_snake_case					:		Dict      =    {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  "speech_to_text_2"
							__UpperCAmelCase   :							Dict					  =  ["past_key_values"]
							__UpperCAmelCase   :							str					  =  {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}

							def __init__(							self	:  str   ,     lowerCamelCase	:  List[Any]=10000   ,     lowerCamelCase	:  Tuple=6   ,     lowerCamelCase	:  Optional[Any]=2048   ,     lowerCamelCase	:  Optional[int]=4   ,     lowerCamelCase	:  Optional[int]=0.0   ,     lowerCamelCase	:  str=True   ,     lowerCamelCase	:  str="relu"   ,     lowerCamelCase	:  Tuple=256   ,     lowerCamelCase	:  Union[str, Any]=0.1   ,     lowerCamelCase	:  Optional[int]=0.0   ,     lowerCamelCase	:  Any=0.0   ,     lowerCamelCase	:  Optional[int]=0.02   ,     lowerCamelCase	:  Dict=2   ,     lowerCamelCase	:  List[str]=True   ,     lowerCamelCase	:  str=1   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  List[Any]=2   ,     lowerCamelCase	:  List[Any]=1024   ,     **lowerCamelCase	:  List[str]   ,     )     ->   Any:
														__snake_case		:      Tuple										=       vocab_size
														__snake_case		:      int										=       d_model
														__snake_case		:      List[str]										=       decoder_ffn_dim
														__snake_case		:      Union[str, Any]										=       decoder_layers
														__snake_case		:      Dict										=       decoder_attention_heads
														__snake_case		:      Any										=       dropout
														__snake_case		:      List[str]										=       attention_dropout
														__snake_case		:      Optional[Any]										=       activation_dropout
														__snake_case		:      Union[str, Any]										=       activation_function
														__snake_case		:      Union[str, Any]										=       init_std
														__snake_case		:      Union[str, Any]										=       decoder_layerdrop
														__snake_case		:      Optional[int]										=       use_cache
														__snake_case		:      Optional[int]										=       decoder_layers
														__snake_case		:      str										=       scale_embedding  # scale factor will be sqrt(d_model) if True
														__snake_case		:      Dict										=       max_target_positions

														super().__init__(
														    pad_token_id=lowerCamelCase   ,     bos_token_id=lowerCamelCase   ,     eos_token_id=lowerCamelCase   ,     decoder_start_token_id=lowerCamelCase   ,     **lowerCamelCase   ,     )

