






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							"▁"

_lowerCAmelCase         =							{"vocab_file": "sentencepiece.bpe.model"}

_lowerCAmelCase         =							{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

_lowerCAmelCase         =							{
    "facebook/xglm-564M": 2_048,
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[Any]    =				VOCAB_FILES_NAMES
	_A       :				Dict    =				PRETRAINED_VOCAB_FILES_MAP
	_A       :				Optional[Any]    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A       :				str    =				["""input_ids""", """attention_mask"""]





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_="<s>"     ,     lowerCAmelCase_="</s>"     ,     lowerCAmelCase_="</s>"     ,     lowerCAmelCase_="<s>"     ,     lowerCAmelCase_="<unk>"     ,     lowerCAmelCase_="<pad>"     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		A_		:						Any	    =   {} if sp_model_kwargs is None else sp_model_kwargs

		# Compatibility with the original tokenizer
		A_		:						List[str]	    =   7
		A_		:						int	    =   [f"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

		A_		:						Union[str, Any]	    =   kwargs.get("""additional_special_tokens"""     ,     []							)
		kwargs["additional_special_tokens"] += [
		    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
		]

		super().__init__(
		    bos_token=lowerCAmelCase_     ,     eos_token=lowerCAmelCase_     ,     unk_token=lowerCAmelCase_     ,     sep_token=lowerCAmelCase_     ,     cls_token=lowerCAmelCase_     ,     pad_token=lowerCAmelCase_     ,     sp_model_kwargs=self.sp_model_kwargs     ,     **lowerCAmelCase_     ,     )

		A_		:						Optional[Any]	    =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.Load(str(lowerCAmelCase_							)							)
		A_		:						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'

		# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
		A_		:						int	    =   1

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

		A_		:						Optional[Any]	    =   len(self.sp_model							)
		A_		:						int	    =   {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
		self.fairseq_tokens_to_ids.update(lowerCAmelCase_							)

		A_		:						Tuple	    =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}





	def __getstate__(self							):
		A_		:						Optional[int]	    =   self.__dict__.copy()
		A_		:						int	    =   None
		A_		:						Optional[Any]	    =   self.sp_model.serialized_model_proto()
		return state





	def __setstate__(self     ,     lowerCAmelCase_							):
		A_		:						Dict	    =   d

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

		A_		:						List[Any]	    =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):

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





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = 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_							)

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





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						List[str]	    =   [self.sep_token_id]

		if token_ids_a is None:
			return len(sep + token_ids_a							) * [0]
		return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]





	@property
	def    lowerCamelCase(self							):
		return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words





	def    lowerCamelCase(self							):
		A_		:						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    lowerCamelCase(self     ,     lowerCAmelCase_							):
		return self.sp_model.encode(lowerCAmelCase_     ,     out_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if token in self.fairseq_tokens_to_ids:
			return self.fairseq_tokens_to_ids[token]
		A_		:						Union[str, Any]	    =   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    lowerCamelCase(self     ,     lowerCAmelCase_							):
		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    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						str	    =   """""".join(lowerCAmelCase_							).replace(lowerCAmelCase_     ,     """ """							).strip()
		return out_string





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

		return (out_vocab_file,)









"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				str    =				"""AutoImageProcessor"""
	_A       :				Union[str, Any]    =				"""AutoTokenizer"""





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   self.image_processor





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):

		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:
			A_		:						Optional[int]	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						List[Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

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





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return ["input_ids", "attention_mask", "pixel_values"]

