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

SCREAMING_SNAKE_CASE :	Optional[Any]							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


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



def   __A		(			_A							,				_A							,				_A = None					):



 """simple docstring"""


 __a       =  tesseract_config if tesseract_config is not None else ""

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

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

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

 # finally, normalize the bounding boxes
 __a       =  []
 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    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			["""pixel_values"""]

  def __init__(		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int] = None     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = PILImageResampling.BILINEAR     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Optional[str] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[str] = ""     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
   super().__init__(**__SCREAMING_SNAKE_CASE       )
   __a       =  size if size is not None else {"height": 2_24, "width": 2_24}
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE       )

   __a       =  do_resize
   __a       =  size
   __a       =  resample
   __a       =  apply_ocr
   __a       =  ocr_lang
   __a       =  tesseract_config

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      np.ndarray     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int]     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = PILImageResampling.BILINEAR     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, ChannelDimension]] = None     ,							**__SCREAMING_SNAKE_CASE :      Optional[int]     ,							):
   __a       =  get_size_dict(__SCREAMING_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()}"""       )
   __a       =  (size["height"], size["width"])
   return resize(__SCREAMING_SNAKE_CASE     ,							size=__SCREAMING_SNAKE_CASE     ,							resample=__SCREAMING_SNAKE_CASE     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      ImageInput     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int] = None     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = None     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      Optional[str] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[str] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, TensorType]] = None     ,							__SCREAMING_SNAKE_CASE :      ChannelDimension = ChannelDimension.FIRST     ,							**__SCREAMING_SNAKE_CASE :      Any     ,							):
   __a       =  do_resize if do_resize is not None else self.do_resize
   __a       =  size if size is not None else self.size
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE       )
   __a       =  resample if resample is not None else self.resample
   __a       =  apply_ocr if apply_ocr is not None else self.apply_ocr
   __a       =  ocr_lang if ocr_lang is not None else self.ocr_lang
   __a       =  tesseract_config if tesseract_config is not None else self.tesseract_config

   __a       =  make_list_of_images(__SCREAMING_SNAKE_CASE       )

   if not valid_images(__SCREAMING_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.
   __a       =  [to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

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

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

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

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

   if apply_ocr:
    __a       =  words_batch
    __a       =  boxes_batch
   return data







from __future__ import annotations
class    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  order

   # a_{0} ... a_{k}
   __a       =  [1.0] + [0.0] * order
   # b_{0} ... b_{k}
   __a       =  [1.0] + [0.0] * order

   # x[n-1] ... x[n-k]
   __a       =  [0.0] * self.order
   # y[n-1] ... y[n-k]
   __a       =  [0.0] * self.order

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      list[float]     ,							__SCREAMING_SNAKE_CASE :      list[float]       ):
   if len(__SCREAMING_SNAKE_CASE       ) < self.order:
    __a       =  [1.0, *a_coeffs]

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected a_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected b_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   __a       =  a_coeffs
   __a       =  b_coeffs

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      float       ):
   __a       =  0.0

   # Start at index 1 and do index 0 at the end.
   for i in range(1     ,							self.order + 1       ):
    result += (
        self.b_coeffs[i] * self.input_history[i - 1]
        - self.a_coeffs[i] * self.output_history[i - 1]
    )

   __a       =  (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

   __a       =  self.input_history[:-1]
   __a       =  self.output_history[:-1]

   __a       =  sample
   __a       =  result

   return result


