






"""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, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = '''▁'''

lowerCamelCase_							      = {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCamelCase_							      = {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

lowerCamelCase_							      = {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']





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

				UpperCAmelCase_	:					int     	=   {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=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_      ,      )

				UpperCAmelCase_	:					Optional[Any]     	=   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(str(lowerCAmelCase_		)		)
				UpperCAmelCase_	:					Optional[int]     	=   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
				UpperCAmelCase_	:					Any     	=   {"<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
				UpperCAmelCase_	:					Tuple     	=   1

				UpperCAmelCase_	:					Dict     	=   len(self.sp_model		) + self.fairseq_offset
				UpperCAmelCase_	:					Any     	=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}





	def __getstate__( self   :					Optional[int]		)		->     Tuple:
				UpperCAmelCase_	:					Optional[int]     	=   self.__dict__.copy()
				UpperCAmelCase_	:					str     	=   None
				UpperCAmelCase_	:					Any     	=   self.sp_model.serialized_model_proto()
				return state





	def __setstate__( self   :					int      ,      lowerCAmelCase_   :					Any		)		->     List[str]:
				UpperCAmelCase_	:					str     	=   d

				# for backward compatibility
				if not hasattr(self      ,      "sp_model_kwargs"		):
							UpperCAmelCase_	:					Optional[int]     	=   {}

				UpperCAmelCase_	:					Dict     	=   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_	:					Any     	=   [self.cls_token_id]
				UpperCAmelCase_	:					Union[str, Any]     	=   [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					bool = False		)		->     List[int]:

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

				if token_ids_a is None:
							return [1] + ([0] * len(lowerCAmelCase_		)) + [1]
				return [1] + ([0] * len(lowerCAmelCase_		)) + [1, 1] + ([0] * len(lowerCAmelCase_		)) + [1]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:
				UpperCAmelCase_	:					List[str]     	=   [self.sep_token_id]
				UpperCAmelCase_	:					str     	=   [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]





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     List[str]:
				return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     List[Any]:
				UpperCAmelCase_	:					int     	=   {self.convert_ids_to_tokens(lowerCAmelCase_		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					str		)		->     List[str]:
				return self.sp_model.encode(lowerCAmelCase_      ,      out_type=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Dict		)		->     str:
				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				UpperCAmelCase_	:					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 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Any		)		->     Any:
				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 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					int		)		->     List[str]:
				UpperCAmelCase_	:					int     	=   "".join(lowerCAmelCase_		).replace(lowerCAmelCase_      ,      " "		).strip()
				return out_string





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Tuple     	=   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:
										UpperCAmelCase_	:					Optional[int]     	=   self.sp_model.serialized_model_proto()
										fi.write(lowerCAmelCase_		)

				return (out_vocab_file,)











"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class      UpperCamelCase_			(__A	):
	__magic_name__       =							['''image_processor''', '''tokenizer''']
	__magic_name__       =							'''FlavaImageProcessor'''
	__magic_name__       =							('''BertTokenizer''', '''BertTokenizerFast''')





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple=None      ,      lowerCAmelCase_   :					List[str]=None      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     List[Any]:
				UpperCAmelCase_	:					Any     	=   None
				if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."      ,      lowerCAmelCase_      ,      )
							UpperCAmelCase_	:					Tuple     	=   kwargs.pop("feature_extractor"		)

				UpperCAmelCase_	:					Optional[Any]     	=   image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
							raise ValueError("You need to specify an `image_processor`."		)
				if tokenizer is None:
							raise ValueError("You need to specify a `tokenizer`."		)

				super().__init__(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   self.image_processor





	def __call__( self   :					List[Any]      ,      lowerCAmelCase_   :					Optional[ImageInput] = None      ,      lowerCAmelCase_   :					Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Union[bool, str, PaddingStrategy] = False      ,      lowerCAmelCase_   :					Union[bool, str, TruncationStrategy] = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      **lowerCAmelCase_   :					Optional[int]      ,      )		->     List[Any]:

				if text is None and images is None:
							raise ValueError("You have to specify either text or images. Both cannot be none."		)

				if text is not None:
							UpperCAmelCase_	:					Union[str, Any]     	=   self.tokenizer(
							    text=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      stride=lowerCAmelCase_      ,      pad_to_multiple_of=lowerCAmelCase_      ,      return_token_type_ids=lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      return_overflowing_tokens=lowerCAmelCase_      ,      return_special_tokens_mask=lowerCAmelCase_      ,      return_offsets_mapping=lowerCAmelCase_      ,      return_length=lowerCAmelCase_      ,      verbose=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )
				if images is not None:
							UpperCAmelCase_	:					Optional[Any]     	=   self.image_processor(
							    lowerCAmelCase_      ,      return_image_mask=lowerCAmelCase_      ,      return_codebook_pixels=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				if text is not None and images is not None:
							encoding.update(lowerCAmelCase_		)
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowerCAmelCase_		)      ,      tensor_type=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      *lowerCAmelCase_   :					int      ,      **lowerCAmelCase_   :					List[str]		)		->     Any:
				return self.tokenizer.batch_decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					Optional[int]		)		->     List[Any]:
				return self.tokenizer.decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   self.tokenizer.model_input_names
				UpperCAmelCase_	:					Any     	=   self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor_class





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor



