

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


__UpperCAmelCase    				=     logging.get_logger(__name__)

__UpperCAmelCase    				=     {'vocab_file': 'spiece.model'}

__UpperCAmelCase    				=     {
    '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'
        ),
    }
}

__UpperCAmelCase    				=     {
    'google/bigbird-roberta-base': 4_0_9_6,
    'google/bigbird-roberta-large': 4_0_9_6,
    'google/bigbird-base-trivia-itc': 4_0_9_6,
}





class A__    ( A ):



 """simple docstring"""







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

 def __init__(		self :	int     ,						A_ :	Any     ,						A_ :	Union[str, Any]="<unk>"     ,						A_ :	Tuple="<s>"     ,						A_ :	int="</s>"     ,						A_ :	Tuple="<pad>"     ,						A_ :	Dict="[SEP]"     ,						A_ :	Optional[int]="[MASK]"     ,						A_ :	str="[CLS]"     ,						A_ :	Optional[Dict[str, Any]] = None     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else bos_token
      _lowerCAmelCase   :					int        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else eos_token
      _lowerCAmelCase   :					Dict        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else unk_token
      _lowerCAmelCase   :					Any        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else pad_token
      _lowerCAmelCase   :					List[Any]        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else cls_token
      _lowerCAmelCase   :					Union[str, Any]        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else sep_token

      # Mask token behave like a normal word, i.e. include the space before it
      _lowerCAmelCase   :					Any        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else mask_token

      _lowerCAmelCase   :					Optional[Any]        =    {} if sp_model_kwargs is None else sp_model_kwargs

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

      _lowerCAmelCase   :					Tuple        =    vocab_file

      _lowerCAmelCase   :					Union[str, Any]        =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(A_       )

 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      return self.sp_model.get_piece_size()

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    {self.convert_ids_to_tokens(A_       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab

 def __getstate__(		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.__dict__.copy()
      _lowerCAmelCase   :					List[str]        =    None
      return state

 def __setstate__(		self :	Dict     ,						A_ :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    d

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

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

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	str       ):






      '''simple docstring'''




      return self.sp_model.encode(A_     ,						out_type=A_       )

 def    __magic_name__       (		self :	Any     ,						A_ :	int       ):






      '''simple docstring'''




      return self.sp_model.piece_to_id(A_       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.sp_model.IdToPiece(A_       )
      return token

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    []
      _lowerCAmelCase   :					Optional[int]        =    ""
      _lowerCAmelCase   :					Optional[Any]        =    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(A_       ) + token
                _lowerCAmelCase   :					List[Any]        =    True
                _lowerCAmelCase   :					str        =    []
           else:
                current_sub_tokens.append(A_       )
                _lowerCAmelCase   :					int        =    False
      out_string += self.sp_model.decode(A_       )
      return out_string.strip()

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	List[int]     ,						A_ :	bool = False     ,						A_ :	bool = None     ,						A_ :	bool = True     ,						**A_ :	Any     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    kwargs.pop("use_source_tokenizer"     ,						A_       )

      _lowerCAmelCase   :					Tuple        =    self.convert_ids_to_tokens(A_     ,						skip_special_tokens=A_       )

      # 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
      _lowerCAmelCase   :					str        =    []
      _lowerCAmelCase   :					int        =    []
      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(A_       )       )
                     _lowerCAmelCase   :					Any        =    []
                sub_texts.append(A_       )
           else:
                current_sub_text.append(A_       )
      if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(A_       )       )

      # Mimic the behavior of the Rust tokenizer:
      # No space before [MASK] and [SEP]
      if spaces_between_special_tokens:
           _lowerCAmelCase   :					List[Any]        =    re.sub(r" (\[(MASK|SEP)\])"     ,						r"\1"     ,						" ".join(A_       )       )
      else:
           _lowerCAmelCase   :					int        =    "".join(A_       )

      _lowerCAmelCase   :					List[str]        =    (
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
           _lowerCAmelCase   :					Optional[Any]        =    self.clean_up_tokenization(A_       )
           return clean_text
      else:
           return text

 def    __magic_name__       (		self :	Any     ,						A_ :	str     ,						A_ :	Optional[str] = None       ):






      '''simple docstring'''




      if not os.path.isdir(A_       ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
      _lowerCAmelCase   :					Optional[Any]        =    os.path.join(
          A_     ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

      if os.path.abspath(self.vocab_file       ) != os.path.abspath(A_       ) and os.path.isfile(self.vocab_file       ):
           copyfile(self.vocab_file     ,						A_       )
      elif not os.path.isfile(self.vocab_file       ):
           with open(A_     ,						"wb"       ) as fi:
                _lowerCAmelCase   :					int        =    self.sp_model.serialized_model_proto()
                fi.write(A_       )

      return (out_vocab_file,)

 def    __magic_name__       (		self :	int     ,						A_ :	List[int]     ,						A_ :	Optional[List[int]] = None       ):






      '''simple docstring'''




      if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _lowerCAmelCase   :					Dict        =    [self.cls_token_id]
      _lowerCAmelCase   :					int        =    [self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep

 def    __magic_name__       (		self :	Tuple     ,						A_ :	List[int]     ,						A_ :	Optional[List[int]] = None     ,						A_ :	bool = False       ):






      '''simple docstring'''




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

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


 def    __magic_name__       (		self :	int     ,						A_ :	List[int]     ,						A_ :	Optional[List[int]] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    [self.sep_token_id]
      _lowerCAmelCase   :					Union[str, Any]        =    [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]









from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
  import PIL

# soft dependency
if is_pytesseract_available():
  import pytesseract

__UpperCAmelCase    				=     logging.get_logger(__name__)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     return [
         int(1000 * (box[0] / width)				),
         int(1000 * (box[1] / height)				),
         int(1000 * (box[2] / width)				),
         int(1000 * (box[3] / height)				),
     ]







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = None				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else ""

     # apply OCR
     _lowerCAmelCase   :					List[str]        =    to_pil_image(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    pil_image.size
     _lowerCAmelCase   :					Union[str, Any]        =    pytesseract.image_to_data(SCREAMING_SNAKE_CASE		,							lang=SCREAMING_SNAKE_CASE		,							output_type="dict"		,							config=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    data["text"], data["left"], data["top"], data["width"], data["height"]

     # filter empty words and corresponding coordinates
     _lowerCAmelCase   :					Dict        =    [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if not word.strip()]
     _lowerCAmelCase   :					Any        =    [word for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Dict        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[int]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     _lowerCAmelCase   :					Union[str, Any]        =    []
     for x, y, w, h in zip(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    [x, y, x + w, y + h]
          actual_boxes.append(SCREAMING_SNAKE_CASE				)

     # finally, normalize the bounding boxes
     _lowerCAmelCase   :					Dict        =    []
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)				)

     assert len(SCREAMING_SNAKE_CASE				) == len(SCREAMING_SNAKE_CASE				), "Not as many words as there are bounding boxes"

     return words, normalized_boxes





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = ""     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					int        =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Any        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Optional[int]        =    apply_ocr
      _lowerCAmelCase   :					Dict        =    ocr_lang
      _lowerCAmelCase   :					Optional[int]        =    tesseract_config

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Dict        =    (size["height"], size["width"])
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Dict     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[Any]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					Union[str, Any]        =    apply_ocr if apply_ocr is not None else self.apply_ocr
      _lowerCAmelCase   :					Tuple        =    ocr_lang if ocr_lang is not None else self.ocr_lang
      _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else self.tesseract_config

      _lowerCAmelCase   :					Union[str, Any]        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."       )

      if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Dict        =    [to_numpy_array(A_       ) for image in images]

      if apply_ocr:
           requires_backends(self     ,						"pytesseract"       )
           _lowerCAmelCase   :					Any        =    []
           _lowerCAmelCase   :					List[Any]        =    []
           for image in images:
                _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    apply_tesseract(A_     ,						A_     ,						A_       )
                words_batch.append(A_       )
                boxes_batch.append(A_       )

      if do_resize:
           _lowerCAmelCase   :					Union[str, Any]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      # flip color channels from RGB to BGR (as Detectron2 requires this)
      _lowerCAmelCase   :					Optional[int]        =    [flip_channel_order(A_       ) for image in images]
      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    BatchFeature(data={"pixel_values": images}     ,						tensor_type=A_       )

      if apply_ocr:
           _lowerCAmelCase   :					Tuple        =    words_batch
           _lowerCAmelCase   :					Any        =    boxes_batch
      return data






