


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_barthez import BarthezTokenizer
else:
		snake_case__			 =						None

snake_case__			 =						logging.get_logger(__name__)

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

snake_case__			 =						{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case__			 =						{
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

snake_case__			 =						'''▁'''


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     VOCAB_FILES_NAMES
						lowerCamelCase_         =     PRETRAINED_VOCAB_FILES_MAP
						lowerCamelCase_         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCamelCase_         =     ['input_ids', 'attention_mask']
						lowerCamelCase_         =     BarthezTokenizer




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






									"""simple docstring"""






									a__ :Tuple						  =    AddedToken(__A   ,     lstrip=__A   ,     rstrip=__A						) if isinstance(__A   ,     __A						) else mask_token

									super().__init__(
									    __A   ,     tokenizer_file=__A   ,     bos_token=__A   ,     eos_token=__A   ,     unk_token=__A   ,     sep_token=__A   ,     cls_token=__A   ,     pad_token=__A   ,     mask_token=__A   ,     **__A   ,     )

									a__ :str						  =    vocab_file
									a__ :Union[str, Any]						  =    False if not self.vocab_file else True




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   List[int]   ,     __A						:   Optional[List[int]] = None						)      ->List[int]:






									"""simple docstring"""






									if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									a__ :Dict						  =    [self.cls_token_id]
									a__ :List[str]						  =    [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep




						def  _snake_case					(	self						:   int   ,     __A						:   List[int]   ,     __A						:   Optional[List[int]] = None						)      ->List[int]:






									"""simple docstring"""






									a__ :Tuple						  =    [self.sep_token_id]
									a__ :Tuple						  =    [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						:   List[str]   ,     __A						:   str   ,     __A						:   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(__A						):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
												return
									a__ :str						  =    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,)






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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''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 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'speech_to_text_2'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Dict   ,     __A						:   Union[str, Any]=10000   ,     __A						:   Tuple=6   ,     __A						:   Tuple=2048   ,     __A						:   Any=4   ,     __A						:   str=0.0   ,     __A						:   Tuple=True   ,     __A						:   List[Any]="relu"   ,     __A						:   Union[str, Any]=256   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]=0.0   ,     __A						:   Tuple=0.0   ,     __A						:   Any=0.02   ,     __A						:   int=2   ,     __A						:   Union[str, Any]=True   ,     __A						:   Tuple=1   ,     __A						:   List[str]=0   ,     __A						:   List[str]=2   ,     __A						:   Union[str, Any]=1024   ,     **__A						:   Tuple   ,     )      ->List[str]:






									"""simple docstring"""






									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    d_model
									a__ :Optional[int]						  =    decoder_ffn_dim
									a__ :Optional[int]						  =    decoder_layers
									a__ :Optional[int]						  =    decoder_attention_heads
									a__ :Dict						  =    dropout
									a__ :Optional[Any]						  =    attention_dropout
									a__ :Any						  =    activation_dropout
									a__ :int						  =    activation_function
									a__ :Optional[int]						  =    init_std
									a__ :List[Any]						  =    decoder_layerdrop
									a__ :Optional[Any]						  =    use_cache
									a__ :List[str]						  =    decoder_layers
									a__ :int						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									a__ :Optional[int]						  =    max_target_positions

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


