'''simple docstring'''


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						['''image_processor''', '''tokenizer''']
  A_    							=						'''BlipImageProcessor'''
  A_    							=						'''AutoTokenizer'''




  def __init__(		self      :       Optional[Any]   ,				lowercase_      :       int   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       int      ):







    '''simple docstring'''


    super().__init__(lowercase_   ,				lowercase_      )

    # add QFormer tokenizer
    lowercase_     =       qformer_tokenizer




  def __call__(		self      :       Dict   ,				lowercase_      :       ImageInput = None   ,				lowercase_      :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				lowercase_      :       bool = True   ,				lowercase_      :       Union[bool, str, PaddingStrategy] = False   ,				lowercase_      :       Union[bool, str, TruncationStrategy] = None   ,				lowercase_      :       Optional[int] = None   ,				lowercase_      :       int = 0   ,				lowercase_      :       Optional[int] = None   ,				lowercase_      :       Optional[bool] = None   ,				lowercase_      :       bool = False   ,				lowercase_      :       bool = False   ,				lowercase_      :       bool = False   ,				lowercase_      :       bool = False   ,				lowercase_      :       bool = False   ,				lowercase_      :       bool = True   ,				lowercase_      :       Optional[Union[str, TensorType]] = None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


    if images is None and text is None:
      raise ValueError("""You have to specify at least images or text."""      )

    lowercase_     =       BatchFeature()

    if text is not None:
      lowercase_     =       self.tokenizer(
          text=lowercase_   ,				add_special_tokens=lowercase_   ,				padding=lowercase_   ,				truncation=lowercase_   ,				max_length=lowercase_   ,				stride=lowercase_   ,				pad_to_multiple_of=lowercase_   ,				return_attention_mask=lowercase_   ,				return_overflowing_tokens=lowercase_   ,				return_special_tokens_mask=lowercase_   ,				return_offsets_mapping=lowercase_   ,				return_token_type_ids=lowercase_   ,				return_length=lowercase_   ,				verbose=lowercase_   ,				return_tensors=lowercase_   ,				**lowercase_   ,				)
      encoding.update(lowercase_      )
      lowercase_     =       self.qformer_tokenizer(
          text=lowercase_   ,				add_special_tokens=lowercase_   ,				padding=lowercase_   ,				truncation=lowercase_   ,				max_length=lowercase_   ,				stride=lowercase_   ,				pad_to_multiple_of=lowercase_   ,				return_attention_mask=lowercase_   ,				return_overflowing_tokens=lowercase_   ,				return_special_tokens_mask=lowercase_   ,				return_offsets_mapping=lowercase_   ,				return_token_type_ids=lowercase_   ,				return_length=lowercase_   ,				verbose=lowercase_   ,				return_tensors=lowercase_   ,				**lowercase_   ,				)
      lowercase_     =       qformer_text_encoding.pop("""input_ids"""      )
      lowercase_     =       qformer_text_encoding.pop("""attention_mask"""      )

    if images is not None:
      lowercase_     =       self.image_processor(lowercase_   ,				return_tensors=lowercase_      )
      encoding.update(lowercase_      )

    return encoding




  def 		lowerCamelCase__  (		self      :       str   ,				*lowercase_      :       List[Any]   ,				**lowercase_      :       str      ):







    '''simple docstring'''


    return self.tokenizer.batch_decode(*lowercase_   ,				**lowercase_      )




  def 		lowerCamelCase__  (		self      :       int   ,				*lowercase_      :       Optional[int]   ,				**lowercase_      :       str      ):







    '''simple docstring'''


    return self.tokenizer.decode(*lowercase_   ,				**lowercase_      )




  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       self.tokenizer.model_input_names
    lowercase_     =       self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       List[str]   ,				**lowercase_      :       Tuple      ):







    '''simple docstring'''


    if os.path.isfile(lowercase_      ):
      raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
    os.makedirs(lowercase_   ,				exist_ok=lowercase_      )
    lowercase_     =       os.path.join(lowercase_   ,				"""qformer_tokenizer"""      )
    self.qformer_tokenizer.save_pretrained(lowercase_      )
    return super().save_pretrained(lowercase_   ,				**lowercase_      )





  @classmethod
  def 		lowerCamelCase__  (		cls      :       str   ,				lowercase_      :       Tuple   ,				**lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       AutoTokenizer.from_pretrained(lowercase_   ,				subfolder="""qformer_tokenizer"""      )
    lowercase_     =       cls._get_arguments_from_pretrained(lowercase_   ,				**lowercase_      )
    args.append(lowercase_      )
    return cls(*lowercase_      )


'''simple docstring'''


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

__snake_case						  =		logging.get_logger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  return [
      int(10_00 * (box[0] / width)						),
      int(10_00 * (box[1] / height)						),
      int(10_00 * (box[2] / width)						),
      int(10_00 * (box[3] / height)						),
  ]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = None						)			->List[Any]:
  lowercase_     =       tesseract_config if tesseract_config is not None else """"""

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

  # filter empty words and corresponding coordinates
  lowercase_     =       [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE_						) if not word.strip()]
  lowercase_     =       [word for idx, word in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [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
  lowercase_     =       []
  for x, y, w, h in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       [x, y, x + w, y + h]
    actual_boxes.append(SCREAMING_SNAKE_CASE_						)

  # finally, normalize the bounding boxes
  lowercase_     =       []
  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"""

  A_    							=						['''pixel_values''']




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







    '''simple docstring'''


    super().__init__(**lowercase_      )
    lowercase_     =       size if size is not None else {"""height""": 224, """width""": 224}
    lowercase_     =       get_size_dict(lowercase_      )

    lowercase_     =       do_resize
    lowercase_     =       size
    lowercase_     =       resample
    lowercase_     =       apply_ocr
    lowercase_     =       ocr_lang
    lowercase_     =       tesseract_config




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







    '''simple docstring'''


    lowercase_     =       get_size_dict(lowercase_      )
    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()}"""      )
    lowercase_     =       (size["""height"""], size["""width"""])
    return resize(lowercase_   ,				size=lowercase_   ,				resample=lowercase_   ,				data_format=lowercase_   ,				**lowercase_      )





  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       ImageInput   ,				lowercase_      :       bool = None   ,				lowercase_      :       Dict[str, int] = None   ,				lowercase_      :       PILImageResampling = None   ,				lowercase_      :       bool = None   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[Union[str, TensorType]] = None   ,				lowercase_      :       ChannelDimension = ChannelDimension.FIRST   ,				**lowercase_      :       Dict   ,				):







    '''simple docstring'''


    lowercase_     =       do_resize if do_resize is not None else self.do_resize
    lowercase_     =       size if size is not None else self.size
    lowercase_     =       get_size_dict(lowercase_      )
    lowercase_     =       resample if resample is not None else self.resample
    lowercase_     =       apply_ocr if apply_ocr is not None else self.apply_ocr
    lowercase_     =       ocr_lang if ocr_lang is not None else self.ocr_lang
    lowercase_     =       tesseract_config if tesseract_config is not None else self.tesseract_config

    lowercase_     =       make_list_of_images(lowercase_      )

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

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

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

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

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

    if apply_ocr:
      lowercase_     =       words_batch
      lowercase_     =       boxes_batch
    return data

