
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:
    lowerCamelCase      =None


lowerCamelCase      =logging.get_logger(__name__)


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

lowerCamelCase      ={
    "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",
    },
}

lowerCamelCase      ={
    "facebook/mbart-large-en-ro": 1_0_2_4,
    "facebook/mbart-large-cc25": 1_0_2_4,
}

# fmt: off
lowerCamelCase      =["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 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







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

     SCREAMING_SNAKE_CASE_		   =  []
     SCREAMING_SNAKE_CASE_		   =  []






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE="<s>"						,						__SCREAMING_SNAKE_CASE="</s>"						,						__SCREAMING_SNAKE_CASE="</s>"						,						__SCREAMING_SNAKE_CASE="<s>"						,						__SCREAMING_SNAKE_CASE="<unk>"						,						__SCREAMING_SNAKE_CASE="<pad>"						,						__SCREAMING_SNAKE_CASE="<mask>"						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				AddedToken(__SCREAMING_SNAKE_CASE						,						lstrip=__SCREAMING_SNAKE_CASE						,						rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) else mask_token

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

           UpperCamelCase__    :   Any					=				vocab_file
           UpperCamelCase__    :   int					=				False if not self.vocab_file else True

           UpperCamelCase__    :   Optional[int]					=				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}  )
           UpperCamelCase__    :   Union[str, Any]					=				{
               lang_code: self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
           }

           UpperCamelCase__    :   List[Any]					=				src_lang if src_lang is not None else '''en_XX'''
           UpperCamelCase__    :   List[Any]					=				self.convert_tokens_to_ids(self._src_lang  )
           UpperCamelCase__    :   Optional[Any]					=				tgt_lang
           self.set_src_lang_special_tokens(self._src_lang  )






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           return self._src_lang






     @src_lang.setter
     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				new_src_lang
           self.set_src_lang_special_tokens(self._src_lang  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   List[int]:




           """simple docstring"""







           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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   List[int]:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				[self.sep_token_id]
           UpperCamelCase__    :   Dict					=				[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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           if src_lang is None or tgt_lang is None:
                 raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
           UpperCamelCase__    :   Optional[Any]					=				src_lang
           UpperCamelCase__    :   Optional[Any]					=				self(__SCREAMING_SNAKE_CASE						,						add_special_tokens=__SCREAMING_SNAKE_CASE						,						return_tensors=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				tgt_lang_id
           return inputs






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = "en_XX"						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = "ro_RO"						,						**__SCREAMING_SNAKE_CASE						,						)  ->   BatchEncoding:




           """simple docstring"""







           UpperCamelCase__    :   int					=				src_lang
           UpperCamelCase__    :   List[str]					=				tgt_lang
           return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           return self.set_src_lang_special_tokens(self.src_lang  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           return self.set_tgt_lang_special_tokens(self.tgt_lang  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				[]
           UpperCamelCase__    :   int					=				[self.eos_token_id, self.cur_lang_code]

           UpperCamelCase__    :   List[str]					=				self.convert_ids_to_tokens(self.prefix_tokens  )
           UpperCamelCase__    :   Optional[Any]					=				self.convert_ids_to_tokens(self.suffix_tokens  )

           UpperCamelCase__    :   List[str]					=				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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   str					=				[]
           UpperCamelCase__    :   List[str]					=				[self.eos_token_id, self.cur_lang_code]

           UpperCamelCase__    :   str					=				self.convert_ids_to_tokens(self.prefix_tokens  )
           UpperCamelCase__    :   int					=				self.convert_ids_to_tokens(self.suffix_tokens  )

           UpperCamelCase__    :   Tuple					=				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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = 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(__SCREAMING_SNAKE_CASE  ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''  )
                 return
           UpperCamelCase__    :   Union[str, Any]					=				os.path.join(
               __SCREAMING_SNAKE_CASE						,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

           if os.path.abspath(self.vocab_file  ) != os.path.abspath(__SCREAMING_SNAKE_CASE  ):
                 copyfile(self.vocab_file						,						__SCREAMING_SNAKE_CASE  )

           return (out_vocab_file,)




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''M-CLIP'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=1_0_2_4						,						__SCREAMING_SNAKE_CASE=7_6_8						,						**__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				transformerDimSize
           UpperCamelCase__    :   Any					=				imageDimSize
           super().__init__(**__SCREAMING_SNAKE_CASE  )




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  MCLIPConfig






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           super().__init__(__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				XLMRobertaModel(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.Linear(
               in_features=config.transformerDimensions						,						out_features=config.numDims  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.transformer(input_ids=__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Tuple					=				(embs * attention_mask.unsqueeze(2  )).sum(dim=1  ) / attention_mask.sum(dim=1  )[:, None]
           return self.LinearTransformation(__SCREAMING_SNAKE_CASE  ), embs


