





from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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      _UpperCamelCase  (a__ :str ,      a__ :Optional[int] ,      a__ :List[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      _UpperCamelCase  (a__ :np.ndarray ,      a__ :Optional[str] ,      a__ :Optional[str]							):






							"""simple docstring"""





							UpperCamelCase__					  =      to_pil_image(a__							)
							UpperCamelCase__  ,  UpperCamelCase__					  =      pil_image.size
							UpperCamelCase__					  =      pytesseract.image_to_data(a__ ,      lang=a__ ,      output_type="""dict""" ,      config=a__							)
							UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

							# filter empty words and corresponding coordinates
							UpperCamelCase__					  =      [idx for idx, word in enumerate(a__							) if not word.strip()]
							UpperCamelCase__					  =      [word for idx, word in enumerate(a__							) if idx not in irrelevant_indices]
							UpperCamelCase__					  =      [coord for idx, coord in enumerate(a__							) if idx not in irrelevant_indices]
							UpperCamelCase__					  =      [coord for idx, coord in enumerate(a__							) if idx not in irrelevant_indices]
							UpperCamelCase__					  =      [coord for idx, coord in enumerate(a__							) if idx not in irrelevant_indices]
							UpperCamelCase__					  =      [coord for idx, coord in enumerate(a__							) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							UpperCamelCase__					  =      []
							for x, y, w, h in zip(a__ ,      a__ ,      a__ ,      a__							):
														UpperCamelCase__					  =      [x, y, x + w, y + h]
														actual_boxes.append(a__							)

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

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

							return words, normalized_boxes

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[int]      =							["""pixel_values"""]






							def __init__(    self	,			__lowerCAmelCase = True	,			__lowerCAmelCase = None	,			__lowerCAmelCase = PILImageResampling.BILINEAR	,			__lowerCAmelCase = True	,			__lowerCAmelCase = 1 / 255	,			__lowerCAmelCase = True	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = True	,			__lowerCAmelCase = None	,			__lowerCAmelCase = ""	,			**__lowerCAmelCase	,			):
														super().__init__(**__lowerCAmelCase	)
														UpperCamelCase__					  =      size if size is not None else {"""height""": 224, """width""": 224}
														UpperCamelCase__					  =      get_size_dict(__lowerCAmelCase	)

														UpperCamelCase__					  =      do_resize
														UpperCamelCase__					  =      size
														UpperCamelCase__					  =      resample
														UpperCamelCase__					  =      do_rescale
														UpperCamelCase__					  =      rescale_value
														UpperCamelCase__					  =      do_normalize
														UpperCamelCase__					  =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														UpperCamelCase__					  =      image_std if image_std is not None else IMAGENET_STANDARD_STD
														UpperCamelCase__					  =      apply_ocr
														UpperCamelCase__					  =      ocr_lang
														UpperCamelCase__					  =      tesseract_config






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = PILImageResampling.BILINEAR	,			__lowerCAmelCase = None	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      get_size_dict(__lowerCAmelCase	)
														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()}"""	)
														UpperCamelCase__					  =      (size["""height"""], size["""width"""])
														return resize(__lowerCAmelCase	,			size=__lowerCAmelCase	,			resample=__lowerCAmelCase	,			data_format=__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			**__lowerCAmelCase	,			):
														return rescale(__lowerCAmelCase	,			scale=__lowerCAmelCase	,			data_format=__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			**__lowerCAmelCase	,			):
														return normalize(__lowerCAmelCase	,			mean=__lowerCAmelCase	,			std=__lowerCAmelCase	,			data_format=__lowerCAmelCase	,			**__lowerCAmelCase	)







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase=None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = ChannelDimension.FIRST	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      do_resize if do_resize is not None else self.do_resize
														UpperCamelCase__					  =      size if size is not None else self.size
														UpperCamelCase__					  =      get_size_dict(__lowerCAmelCase	)
														UpperCamelCase__					  =      resample if resample is not None else self.resample
														UpperCamelCase__					  =      do_rescale if do_rescale is not None else self.do_rescale
														UpperCamelCase__					  =      rescale_factor if rescale_factor is not None else self.rescale_factor
														UpperCamelCase__					  =      do_normalize if do_normalize is not None else self.do_normalize
														UpperCamelCase__					  =      image_mean if image_mean is not None else self.image_mean
														UpperCamelCase__					  =      image_std if image_std is not None else self.image_std
														UpperCamelCase__					  =      apply_ocr if apply_ocr is not None else self.apply_ocr
														UpperCamelCase__					  =      ocr_lang if ocr_lang is not None else self.ocr_lang
														UpperCamelCase__					  =      tesseract_config if tesseract_config is not None else self.tesseract_config

														UpperCamelCase__					  =      make_list_of_images(__lowerCAmelCase	)

														if not valid_images(__lowerCAmelCase	):
																					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."""	)

														if do_rescale and rescale_factor is None:
																					raise ValueError("""Rescale factor must be specified if do_rescale is True."""	)

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError("""If do_normalize is True, image_mean and image_std must be specified."""	)

														# All transformations expect numpy arrays.
														UpperCamelCase__					  =      [to_numpy_array(__lowerCAmelCase	) for image in images]

														# Tesseract OCR to get words + normalized bounding boxes
														if apply_ocr:
																					requires_backends(self	,			"""pytesseract"""	)
																					UpperCamelCase__					  =      []
																					UpperCamelCase__					  =      []
																					for image in images:
																												UpperCamelCase__  ,  UpperCamelCase__					  =      apply_tesseract(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	)
																												words_batch.append(__lowerCAmelCase	)
																												boxes_batch.append(__lowerCAmelCase	)

														if do_resize:
																					UpperCamelCase__					  =      [self.resize(image=__lowerCAmelCase	,			size=__lowerCAmelCase	,			resample=__lowerCAmelCase	) for image in images]

														if do_rescale:
																					UpperCamelCase__					  =      [self.rescale(image=__lowerCAmelCase	,			scale=__lowerCAmelCase	) for image in images]

														if do_normalize:
																					UpperCamelCase__					  =      [self.normalize(image=__lowerCAmelCase	,			mean=__lowerCAmelCase	,			std=__lowerCAmelCase	) for image in images]

														UpperCamelCase__					  =      [to_channel_dimension_format(__lowerCAmelCase	,			__lowerCAmelCase	) for image in images]

														UpperCamelCase__					  =      BatchFeature(data={"""pixel_values""": images}	,			tensor_type=__lowerCAmelCase	)

														if apply_ocr:
																					UpperCamelCase__					  =      words_batch
																					UpperCamelCase__					  =      boxes_batch
														return data











import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_big_bird import BigBirdTokenizer
else:
					UpperCamelCase__           =			None

UpperCamelCase__           =			logging.get_logger(__name__)
UpperCamelCase__           =			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

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"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__           =			{
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}


UpperCamelCase__           =			"▁"

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							VOCAB_FILES_NAMES
							snake_case  :   List[str]      =							PRETRAINED_VOCAB_FILES_MAP
							snake_case  :   List[str]      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case  :   int      =							BigBirdTokenizer
							snake_case  :   List[Any]      =							["""input_ids""", """attention_mask"""]
							snake_case  :   List[int]      =							[]






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

														super().__init__(
														    __lowerCAmelCase	,			tokenizer_file=__lowerCAmelCase	,			bos_token=__lowerCAmelCase	,			eos_token=__lowerCAmelCase	,			unk_token=__lowerCAmelCase	,			sep_token=__lowerCAmelCase	,			pad_token=__lowerCAmelCase	,			cls_token=__lowerCAmelCase	,			mask_token=__lowerCAmelCase	,			**__lowerCAmelCase	,			)

														UpperCamelCase__					  =      vocab_file
														UpperCamelCase__					  =      False if not self.vocab_file else True






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														UpperCamelCase__					  =      [self.sep_token_id]
														UpperCamelCase__					  =      [self.cls_token_id]
														if token_ids_a is None:
																					return cls + token_ids_a + sep
														return cls + token_ids_a + sep + token_ids_a + sep






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			__lowerCAmelCase = False	):

														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    """You should not supply a second sequence if the provided sequence of """
																												    """ids is already formatted with special tokens for the model."""	)
																					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														UpperCamelCase__					  =      [self.sep_token_id]
														UpperCamelCase__					  =      [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]







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														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(__lowerCAmelCase	):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
																					return
														UpperCamelCase__					  =      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	):
																					copyfile(self.vocab_file	,			__lowerCAmelCase	)

														return (out_vocab_file,)




