




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_nllb import NllbTokenizer
else:
   lowerCamelCase__					:  int      							=				None


lowerCamelCase__					:  Any      							=				logging.get_logger(__name__)


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

lowerCamelCase__					:  Union[str, Any]      							=				{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase__					:  Optional[Any]      							=				{
    'facebook/nllb-large-en-ro': 1_024,
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
lowerCamelCase__					:  List[Any]      							=				['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       VOCAB_FILES_NAMES
 lowercase_							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_							       =       PRETRAINED_VOCAB_FILES_MAP
 lowercase_							       =       ["input_ids", "attention_mask"]
 lowercase_							       =       NllbTokenizer

 lowercase_							       =       []
 lowercase_							       =       []



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

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

       SCREAMING_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}		)
       SCREAMING_SNAKE_CASE_      		=     {
           lang_code: self.convert_tokens_to_ids(_lowerCAmelCase		) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       SCREAMING_SNAKE_CASE_      		=     src_lang if src_lang is not None else 'eng_Latn'
       SCREAMING_SNAKE_CASE_      		=     self.convert_tokens_to_ids(self._src_lang		)
       SCREAMING_SNAKE_CASE_      		=     tgt_lang
       self.set_src_lang_special_tokens(self._src_lang		)



 @property
 def  lowerCAmelCase_    (				self    :						Tuple		):
       return self._src_lang



 @src_lang.setter
 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     new_src_lang
       self.set_src_lang_special_tokens(self._src_lang		)



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = 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  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_      		=     [self.sep_token_id]
       SCREAMING_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  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Optional[str]							,							_lowerCAmelCase    :						Optional[str]							,							**_lowerCAmelCase    :						Optional[Any]		):
       if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'		)
       SCREAMING_SNAKE_CASE_      		=     src_lang
       SCREAMING_SNAKE_CASE_      		=     self(_lowerCAmelCase							,							add_special_tokens=_lowerCAmelCase							,							return_tensors=_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.convert_tokens_to_ids(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     tgt_lang_id
       return inputs



 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						List[str]							,							_lowerCAmelCase    :						str = "eng_Latn"							,							_lowerCAmelCase    :						Optional[List[str]] = None							,							_lowerCAmelCase    :						str = "fra_Latn"							,							**_lowerCAmelCase    :						List[str]							,							):
       SCREAMING_SNAKE_CASE_      		=     src_lang
       SCREAMING_SNAKE_CASE_      		=     tgt_lang
       return super().prepare_seqaseq_batch(_lowerCAmelCase							,							_lowerCAmelCase							,							**_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						List[str]		):
       return self.set_src_lang_special_tokens(self.src_lang		)



 def  lowerCAmelCase_    (				self    :						Any		):
       return self.set_tgt_lang_special_tokens(self.tgt_lang		)



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.convert_tokens_to_ids(_lowerCAmelCase		)

       if self.legacy_behaviour:
             SCREAMING_SNAKE_CASE_      		=     []
             SCREAMING_SNAKE_CASE_      		=     [self.eos_token_id, self.cur_lang_code]
       else:
             SCREAMING_SNAKE_CASE_      		=     [self.cur_lang_code]
             SCREAMING_SNAKE_CASE_      		=     [self.eos_token_id]

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

       SCREAMING_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  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     self.convert_tokens_to_ids(_lowerCAmelCase		)
       if self.legacy_behaviour:
             SCREAMING_SNAKE_CASE_      		=     []
             SCREAMING_SNAKE_CASE_      		=     [self.eos_token_id, self.cur_lang_code]
       else:
             SCREAMING_SNAKE_CASE_      		=     [self.cur_lang_code]
             SCREAMING_SNAKE_CASE_      		=     [self.eos_token_id]

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

       SCREAMING_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  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Optional[str] = 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(_lowerCAmelCase		):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory."		)
             return
       SCREAMING_SNAKE_CASE_      		=     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


lowerCamelCase__					:  str      							=				logging.get_logger(__name__)

lowerCamelCase__					:  Tuple      							=				{
    'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
    'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json',
    'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/config.json',
    'funnel-transformer/medium-base': 'https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json',
    'funnel-transformer/intermediate': (
        'https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'
    ),
    'funnel-transformer/intermediate-base': (
        'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'
    ),
    'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/config.json',
    'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json',
    'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json',
    'funnel-transformer/xlarge-base': 'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json',
}





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "funnel"
 lowercase_							       =       {
     "hidden_size": "d_model",
     "num_attention_heads": "n_head",
 }



 def __init__(				self    :						int							,							_lowerCAmelCase    :						Optional[int]=30_522							,							_lowerCAmelCase    :						List[str]=[4, 4, 4]							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						int=768							,							_lowerCAmelCase    :						Optional[Any]=12							,							_lowerCAmelCase    :						Optional[Any]=64							,							_lowerCAmelCase    :						Optional[Any]=3_072							,							_lowerCAmelCase    :						List[str]="gelu_new"							,							_lowerCAmelCase    :						Any=0.1							,							_lowerCAmelCase    :						int=0.1							,							_lowerCAmelCase    :						Tuple=0.0							,							_lowerCAmelCase    :						List[Any]=0.1							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						str=1E-9							,							_lowerCAmelCase    :						Any="mean"							,							_lowerCAmelCase    :						Union[str, Any]="relative_shift"							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						Dict=True							,							_lowerCAmelCase    :						Tuple=True							,							**_lowerCAmelCase    :						Optional[Any]							,							):
       SCREAMING_SNAKE_CASE_      		=     vocab_size
       SCREAMING_SNAKE_CASE_      		=     block_sizes
       SCREAMING_SNAKE_CASE_      		=     [1] * len(_lowerCAmelCase		) if block_repeats is None else block_repeats
       assert len(_lowerCAmelCase		) == len(
           self.block_repeats		), "`block_sizes` and `block_repeats` should have the same length."
       SCREAMING_SNAKE_CASE_      		=     num_decoder_layers
       SCREAMING_SNAKE_CASE_      		=     d_model
       SCREAMING_SNAKE_CASE_      		=     n_head
       SCREAMING_SNAKE_CASE_      		=     d_head
       SCREAMING_SNAKE_CASE_      		=     d_inner
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout
       SCREAMING_SNAKE_CASE_      		=     attention_dropout
       SCREAMING_SNAKE_CASE_      		=     activation_dropout
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     initializer_std
       SCREAMING_SNAKE_CASE_      		=     layer_norm_eps
       assert pooling_type in [
           "mean",
           "max",
       ], F"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
       SCREAMING_SNAKE_CASE_      		=     pooling_type
       assert attention_type in [
           "relative_shift",
           "factorized",
       ], F"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
       SCREAMING_SNAKE_CASE_      		=     attention_type
       SCREAMING_SNAKE_CASE_      		=     separate_cls
       SCREAMING_SNAKE_CASE_      		=     truncate_seq
       SCREAMING_SNAKE_CASE_      		=     pool_q_only

       super().__init__(**_lowerCAmelCase		)



 @property
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       return sum(self.block_sizes		)



 @num_hidden_layers.setter
 def  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						List[Any]		):
       raise NotImplementedError(
           'This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'		)



 @property
 def  lowerCAmelCase_    (				self    :						List[Any]		):
       return len(self.block_sizes		)




 @num_blocks.setter
 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]		):
       raise NotImplementedError('This model does not support the setting of `num_blocks`. Please set `block_sizes`.'		)