
"""simple docstring"""





import os
import re
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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{"vocab_file": "spiece.model"}

__magic_name__       =			{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

__magic_name__       =			{
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		VOCAB_FILES_NAMES
		__lowercase							:  List[Any]						   =		PRETRAINED_VOCAB_FILES_MAP
		__lowercase							:  Any						   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase							:  Union[str, Any]						   =		['''input_ids''', '''attention_mask''']
		__lowercase							:  List[int]						   =		[]




		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__="<unk>"							,  lowerCAmelCase__="<s>"							,  lowerCAmelCase__="</s>"							,  lowerCAmelCase__="<pad>"							,  lowerCAmelCase__="[SEP]"							,  lowerCAmelCase__="[MASK]"							,  lowerCAmelCase__="[CLS]"							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else bos_token
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else eos_token
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else unk_token
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else pad_token
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else cls_token
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else sep_token

				# 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            = {} if sp_model_kwargs is None else sp_model_kwargs

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

				__SCREAMING_SNAKE_CASE            = vocab_file

				__SCREAMING_SNAKE_CASE            = spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(lowerCAmelCase__)




		@property
		def 	snake_case_     (     self):
				return self.sp_model.get_piece_size()




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = {self.convert_ids_to_tokens(lowerCAmelCase__): i for i in range(self.vocab_size)}
				vocab.update(self.added_tokens_encoder)
				return vocab




		def __getstate__(     self):
				__SCREAMING_SNAKE_CASE            = self.__dict__.copy()
				__SCREAMING_SNAKE_CASE            = None
				return state




		def __setstate__(     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = d

				# for backward compatibility
				if not hasattr(self							,  """sp_model_kwargs"""):
						__SCREAMING_SNAKE_CASE            = {}

				__SCREAMING_SNAKE_CASE            = spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(self.vocab_file)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				return self.sp_model.encode(lowerCAmelCase__							,  out_type=lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				return self.sp_model.piece_to_id(lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.sp_model.IdToPiece(lowerCAmelCase__)
				return token




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = """"""
				__SCREAMING_SNAKE_CASE            = 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
								__SCREAMING_SNAKE_CASE            = True
								__SCREAMING_SNAKE_CASE            = []
						else:
								current_sub_tokens.append(lowerCAmelCase__)
								__SCREAMING_SNAKE_CASE            = False
				out_string += self.sp_model.decode(lowerCAmelCase__)
				return out_string.strip()




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = True							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""use_source_tokenizer"""							,  lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.convert_ids_to_tokens(lowerCAmelCase__							,  skip_special_tokens=lowerCAmelCase__)

				# To avoid mixing byte-level and unicode for byte-level BPT
				# we need to build string separately for added tokens and byte-level tokens
				# cf. https://github.com/huggingface/transformers/issues/1133
				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = []
				for token in filtered_tokens:
						if skip_special_tokens and token in self.all_special_ids:
								continue
						if token in self.added_tokens_encoder:
								if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__))
										__SCREAMING_SNAKE_CASE            = []
								sub_texts.append(lowerCAmelCase__)
						else:
								current_sub_text.append(lowerCAmelCase__)
				if current_sub_text:
						sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__))

				# Mimic the behavior of the Rust tokenizer:
				# No space before [MASK] and [SEP]
				if spaces_between_special_tokens:
						__SCREAMING_SNAKE_CASE            = re.sub(R""" (\[(MASK|SEP)\])"""							,  R"""\1"""							,  """ """.join(lowerCAmelCase__))
				else:
						__SCREAMING_SNAKE_CASE            = """""".join(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = (
				    clean_up_tokenization_spaces
				    if clean_up_tokenization_spaces is not None
				    else self.clean_up_tokenization_spaces
				)
				if clean_up_tokenization_spaces:
						__SCREAMING_SNAKE_CASE            = self.clean_up_tokenization(lowerCAmelCase__)
						return clean_text
				else:
						return text




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None):
				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__) 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:
								__SCREAMING_SNAKE_CASE            = self.sp_model.serialized_model_proto()
								fi.write(lowerCAmelCase__)

				return (out_vocab_file,)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None):
				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__SCREAMING_SNAKE_CASE            = [self.cls_token_id]
				__SCREAMING_SNAKE_CASE            = [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep




		def 	snake_case_     (     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__)) + [1]
				return [1] + ([0] * len(lowerCAmelCase__)) + [1] + ([0] * len(lowerCAmelCase__)) + [1]




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = 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) * [0] + len(token_ids_a + sep) * [1]






"""simple docstring"""





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[Any]						   =		['''image_processor''', '''tokenizer''']
		__lowercase							:  List[Any]						   =		'''BridgeTowerImageProcessor'''
		__lowercase							:  Tuple						   =		('''RobertaTokenizer''', '''RobertaTokenizerFast''')




		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				super().__init__(lowerCAmelCase__							,  lowerCAmelCase__)




		def __call__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = 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__							,  )
				# add pixel_values + pixel_mask
				__SCREAMING_SNAKE_CASE            = self.image_processor(
				    lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  do_normalize=lowerCAmelCase__							,  do_center_crop=lowerCAmelCase__							,  **lowerCAmelCase__)
				encoding.update(lowerCAmelCase__)

				return encoding




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.batch_decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		@property
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.tokenizer.model_input_names
				__SCREAMING_SNAKE_CASE            = self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




