

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		:   int										=       logging.get_logger(__name__)





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = None      ):







							'''simple docstring'''






							snake_case_							=    tesseract_config if tesseract_config is not None else ''

							# apply OCR
							snake_case_							=    to_pil_image(UpperCamelCase__      )
							snake_case_				,    snake_case_							=    pil_image.size
							snake_case_							=    pytesseract.image_to_data(UpperCamelCase__   ,							lang=UpperCamelCase__   ,							output_type='dict'   ,							config=UpperCamelCase__      )
							snake_case_				,    snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

							return words, normalized_boxes







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Any       			=			['''pixel_values''']




				def __init__(					self      ,   snake_case = True      ,   snake_case = None      ,   snake_case = PILImageResampling.BILINEAR      ,   snake_case = True      ,   snake_case = None      ,   snake_case = ""      ,   **snake_case      ,   ):
											super().__init__(**snake_case       )
											snake_case_							=    size if size is not None else {'height': 224, 'width': 224}
											snake_case_							=    get_size_dict(snake_case       )

											snake_case_							=    do_resize
											snake_case_							=    size
											snake_case_							=    resample
											snake_case_							=    apply_ocr
											snake_case_							=    ocr_lang
											snake_case_							=    tesseract_config




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case = PILImageResampling.BILINEAR      ,   snake_case = None      ,   **snake_case      ,   ):
											snake_case_							=    get_size_dict(snake_case       )
											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()}'''       )
											snake_case_							=    (size['height'], size['width'])
											return resize(snake_case      ,   size=snake_case      ,   resample=snake_case      ,   data_format=snake_case      ,   **snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = ChannelDimension.FIRST      ,   **snake_case      ,   ):
											snake_case_							=    do_resize if do_resize is not None else self.do_resize
											snake_case_							=    size if size is not None else self.size
											snake_case_							=    get_size_dict(snake_case       )
											snake_case_							=    resample if resample is not None else self.resample
											snake_case_							=    apply_ocr if apply_ocr is not None else self.apply_ocr
											snake_case_							=    ocr_lang if ocr_lang is not None else self.ocr_lang
											snake_case_							=    tesseract_config if tesseract_config is not None else self.tesseract_config

											snake_case_							=    make_list_of_images(snake_case       )

											if not valid_images(snake_case       ):
																		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.
											snake_case_							=    [to_numpy_array(snake_case       ) for image in images]

											if apply_ocr:
																		requires_backends(self      ,   'pytesseract'       )
																		snake_case_							=    []
																		snake_case_							=    []
																		for image in images:
																									snake_case_				,    snake_case_							=    apply_tesseract(snake_case      ,   snake_case      ,   snake_case       )
																									words_batch.append(snake_case       )
																									boxes_batch.append(snake_case       )

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

											# flip color channels from RGB to BGR (as Detectron2 requires this)
											snake_case_							=    [flip_channel_order(snake_case       ) for image in images]
											snake_case_							=    [to_channel_dimension_format(snake_case      ,   snake_case       ) for image in images]

											snake_case_							=    BatchFeature(data={'pixel_values': images}      ,   tensor_type=snake_case       )

											if apply_ocr:
																		snake_case_							=    words_batch
																		snake_case_							=    boxes_batch
											return data







from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase		:   Any										=       logging.get_logger(__name__)


_UpperCAmelCase		:   Dict										=       {
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_UpperCAmelCase		:   Tuple										=       111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_UpperCAmelCase		:   List[str]										=       0

_UpperCAmelCase		:   Any										=       0xE000
_UpperCAmelCase		:   Dict										=       0xE001
_UpperCAmelCase		:   Optional[int]										=       0xE002
_UpperCAmelCase		:   Tuple										=       0xE003
_UpperCAmelCase		:   Tuple										=       0xE004

# Maps special codepoints to human-readable names.
_UpperCAmelCase		:   Dict[int, str]										=       {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_UpperCAmelCase		:   Dict[str, int]										=       {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: str       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




				def __init__(					self      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=False      ,   snake_case=2048      ,   **snake_case      ,   ):
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else bos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else eos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else sep_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else cls_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else pad_token

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

											super().__init__(
											    bos_token=snake_case      ,   eos_token=snake_case      ,   sep_token=snake_case      ,   cls_token=snake_case      ,   pad_token=snake_case      ,   mask_token=snake_case      ,   add_prefix_space=snake_case      ,   model_max_length=snake_case      ,   **snake_case      ,   )

											# Creates a mapping for looking up the IDs of special symbols.
											snake_case_							=    {}
											for codepoint, name in SPECIAL_CODEPOINTS.items():
																		snake_case_							=    codepoint

											# Creates a mapping for looking up the string forms of special symbol IDs.
											snake_case_							=    {
											    codepoint: name for name, codepoint in self._special_codepoints.items()
											}

											snake_case_							=    UNICODE_VOCAB_SIZE
											snake_case_							=    len(self._special_codepoints       )




				@property
				def     a							(					self       ):
											return self._unicode_vocab_size




				def     a							(					self      ,   snake_case       ):
											return list(snake_case       )




				def     a							(					self      ,   snake_case       ):
											try:
																		return ord(snake_case       )
											except TypeError:
																		raise ValueError(F'''invalid token: \'{token}\''''       )




				def     a							(					self      ,   snake_case       ):
											try:
																		if index in SPECIAL_CODEPOINTS:
																									return SPECIAL_CODEPOINTS[index]
																		return chr(snake_case       )
											except TypeError:
																		raise ValueError(F'''invalid id: {index}'''       )




				def     a							(					self      ,   snake_case       ):
											return "".join(snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]

											snake_case_							=    cls + token_ids_a + sep
											if token_ids_a is not None:
																		result += token_ids_a + sep
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = False       ):
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=snake_case      ,   token_ids_a=snake_case      ,   already_has_special_tokens=snake_case       )

											snake_case_							=    [1] + ([0] * len(snake_case       )) + [1]
											if token_ids_a is not None:
																		result += ([0] * len(snake_case       )) + [1]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]

											snake_case_							=    len(cls + token_ids_a + sep       ) * [0]
											if token_ids_a is not None:
																		result += len(token_ids_a + sep       ) * [1]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											return ()




