"""simple docstring"""




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_a				:		Optional[int]	   =       logging.get_logger(__name__)

_a				:		Dict	   =       '▁'

_a				:		Dict	   =       {'vocab_file': 'sentencepiece.bpe.model'}

_a				:		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'
        ),
    }
}

_a				:		Union[str, Any]	   =       {
    'facebook/mbart-large-50-one-to-many-mmt': 1_024,
}

# fmt: off
_a				:		Optional[int]	   =       ['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 						__A							(			__snake_case		):
     _UpperCamelCase							:      List[Any]		 =		VOCAB_FILES_NAMES
     _UpperCamelCase							:      Any		 =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:      Tuple		 =		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:      int		 =		["input_ids", "attention_mask"]

     _UpperCamelCase							:      List[int]		 =		[]
     _UpperCamelCase							:      List[int]		 =		[]






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

       _lowerCAmelCase						:	str        =					{} if sp_model_kwargs is None else sp_model_kwargs

       _lowerCAmelCase						:	List[str]        =					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__(
           src_lang=lowerCamelCase_    ,					tgt_lang=lowerCamelCase_    ,					eos_token=lowerCamelCase_    ,					unk_token=lowerCamelCase_    ,					sep_token=lowerCamelCase_    ,					cls_token=lowerCamelCase_    ,					pad_token=lowerCamelCase_    ,					mask_token=lowerCamelCase_    ,					sp_model_kwargs=self.sp_model_kwargs    ,					**lowerCamelCase_    ,					)

       _lowerCAmelCase						:	int        =					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(lowerCamelCase_  )  )
       _lowerCAmelCase						:	List[str]        =					vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # Mimic fairseq token-to-id alignment for the first 4 token
       _lowerCAmelCase						:	Tuple        =					{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       _lowerCAmelCase						:	List[str]        =					1

       _lowerCAmelCase						:	Dict        =					len(self.sp_model  )
       _lowerCAmelCase						:	Any        =					{
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCamelCase_  )
       }
       _lowerCAmelCase						:	str        =					{v: k for k, v in self.lang_code_to_id.items()}
       _lowerCAmelCase						:	List[str]        =					len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
       _lowerCAmelCase						:	Dict        =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}

       _lowerCAmelCase						:	str        =					src_lang if src_lang is not None else """en_XX"""
       _lowerCAmelCase						:	Tuple        =					self.lang_code_to_id[self._src_lang]
       _lowerCAmelCase						:	List[str]        =					tgt_lang
       self.set_src_lang_special_tokens(self._src_lang  )






     @property
     def    __A  (		self  ):
       return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token






     @property
     def    __A  (		self  ):
       return self._src_lang






     @src_lang.setter
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					new_src_lang
       self.set_src_lang_special_tokens(self._src_lang  )






     def __getstate__(		self  ):
       _lowerCAmelCase						:	int        =					self.__dict__.copy()
       _lowerCAmelCase						:	Optional[int]        =					None
       return state






     def __setstate__(		self    ,					a__  ):
       _lowerCAmelCase						:	List[Any]        =					d

       # for backward compatibility
       if not hasattr(self    ,					"""sp_model_kwargs"""  ):
         _lowerCAmelCase						:	List[Any]        =					{}

       _lowerCAmelCase						:	Any        =					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(self.vocab_file  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					{self.convert_ids_to_tokens(lowerCamelCase_  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab






     def    __A  (		self    ,					a__  ):
       return self.sp_model.encode(lowerCamelCase_    ,					out_type=lowerCamelCase_  )






     def    __A  (		self    ,					a__  ):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       _lowerCAmelCase						:	Optional[int]        =					self.sp_model.PieceToId(lowerCamelCase_  )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






     def    __A  (		self    ,					a__  ):
       if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					[]
       _lowerCAmelCase						:	Dict        =					""""""
       _lowerCAmelCase						:	List[str]        =					False
       for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
           if not prev_is_special:
             out_string += " "
           out_string += self.sp_model.decode(lowerCamelCase_  ) + token
           _lowerCAmelCase						:	Tuple        =					True
           _lowerCAmelCase						:	Tuple        =					[]
         else:
           current_sub_tokens.append(lowerCamelCase_  )
           _lowerCAmelCase						:	int        =					False
       out_string += self.sp_model.decode(lowerCamelCase_  )
       return out_string.strip()






     def    __A  (		self    ,					a__    ,					a__ = None  ):
       if not os.path.isdir(lowerCamelCase_  ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
         return
       _lowerCAmelCase						:	Union[str, Any]        =					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_  ) and os.path.isfile(self.vocab_file  ):
         copyfile(self.vocab_file    ,					lowerCamelCase_  )
       elif not os.path.isfile(self.vocab_file  ):
         with open(lowerCamelCase_    ,					"""wb"""  ) as fi:
           _lowerCAmelCase						:	List[str]        =					self.sp_model.serialized_model_proto()
           fi.write(lowerCamelCase_  )

       return (out_vocab_file,)






     def    __A  (		self    ,					a__    ,					a__ = None    ,					a__ = False  ):

       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowerCamelCase_    ,					token_ids_a=lowerCamelCase_    ,					already_has_special_tokens=lowerCamelCase_  )

       _lowerCAmelCase						:	List[Any]        =					[1] * len(self.prefix_tokens  )
       _lowerCAmelCase						:	str        =					[1] * len(self.suffix_tokens  )
       if token_ids_a is None:
         return prefix_ones + ([0] * len(lowerCamelCase_  )) + suffix_ones
       return prefix_ones + ([0] * len(lowerCamelCase_  )) + ([0] * len(lowerCamelCase_  )) + suffix_ones






     def    __A  (		self    ,					a__    ,					a__ = 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    ,					a__    ,					a__    ,					a__    ,					a__    ,					**a__  ):
       if src_lang is None or tgt_lang is None:
         raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""  )
       _lowerCAmelCase						:	Optional[Any]        =					src_lang
       _lowerCAmelCase						:	List[str]        =					self(lowerCamelCase_    ,					add_special_tokens=lowerCamelCase_    ,					return_tensors=lowerCamelCase_    ,					**lowerCamelCase_  )
       _lowerCAmelCase						:	Union[str, Any]        =					self.convert_tokens_to_ids(lowerCamelCase_  )
       _lowerCAmelCase						:	List[str]        =					tgt_lang_id
       return inputs






     def    __A  (		self    ,					a__    ,					a__ = "en_XX"    ,					a__ = None    ,					a__ = "ro_RO"    ,					**a__    ,					):
       _lowerCAmelCase						:	Optional[Any]        =					src_lang
       _lowerCAmelCase						:	Optional[int]        =					tgt_lang
       return super().prepare_seqaseq_batch(lowerCamelCase_    ,					lowerCamelCase_    ,					**lowerCamelCase_  )






     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    ,					a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.lang_code_to_id[src_lang]
       _lowerCAmelCase						:	Optional[Any]        =					[self.cur_lang_code_id]
       _lowerCAmelCase						:	Any        =					[self.eos_token_id]






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	int        =					self.lang_code_to_id[tgt_lang]
       _lowerCAmelCase						:	List[Any]        =					[self.cur_lang_code_id]
       _lowerCAmelCase						:	Optional[Any]        =					[self.eos_token_id]





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_a				:		List[Any]	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		"maskformer-swin"

     _UpperCamelCase							:      Union[str, Any]		 =		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self    ,					a__=224    ,					a__=4    ,					a__=3    ,					a__=96    ,					a__=[2, 2, 6, 2]    ,					a__=[3, 6, 12, 24]    ,					a__=7    ,					a__=4.0    ,					a__=True    ,					a__=0.0    ,					a__=0.0    ,					a__=0.1    ,					a__="gelu"    ,					a__=False    ,					a__=0.0_2    ,					a__=1e-5    ,					a__=None    ,					a__=None    ,					**a__    ,					):
       super().__init__(**a__  )

       _lowerCAmelCase						:	Dict        =					image_size
       _lowerCAmelCase						:	List[str]        =					patch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	int        =					embed_dim
       _lowerCAmelCase						:	Optional[Any]        =					depths
       _lowerCAmelCase						:	List[str]        =					len(a__  )
       _lowerCAmelCase						:	List[Any]        =					num_heads
       _lowerCAmelCase						:	Tuple        =					window_size
       _lowerCAmelCase						:	List[Any]        =					mlp_ratio
       _lowerCAmelCase						:	Optional[Any]        =					qkv_bias
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Union[str, Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					drop_path_rate
       _lowerCAmelCase						:	Optional[Any]        =					hidden_act
       _lowerCAmelCase						:	Tuple        =					use_absolute_embeddings
       _lowerCAmelCase						:	str        =					layer_norm_eps
       _lowerCAmelCase						:	Any        =					initializer_range
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:	Union[str, Any]        =					int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:	int        =					["""stem"""] + [F"stage{idx}" for idx in range(1    ,					len(a__  ) + 1  )]
       _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					get_aligned_output_features_output_indices(
           out_features=a__    ,					out_indices=a__    ,					stage_names=self.stage_names  )




