




'''simple docstring'''





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_						:      List[str]       =						logging.get_logger(__name__)





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   int ,						_UpperCamelCase:   List[Any]   )						->			Tuple:

     """simple docstring"""




     return [
         int(1_000 * (box[0] / width)   ),
         int(1_000 * (box[1] / height)   ),
         int(1_000 * (box[2] / width)   ),
         int(1_000 * (box[3] / height)   ),
     ]





def      __a     (       _UpperCamelCase:   np.ndarray ,						_UpperCamelCase:   Optional[str] ,						_UpperCamelCase:   Optional[str]   )						->			str:

     """simple docstring"""




     _snake_case							=				to_pil_image(_SCREAMING_SNAKE_CASE   )
     _snake_case							=				pil_image.size
     _snake_case							=				pytesseract.image_to_data(_SCREAMING_SNAKE_CASE ,						lang=_SCREAMING_SNAKE_CASE ,						output_type="dict" ,						config=_SCREAMING_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(_SCREAMING_SNAKE_CASE   ) if not word.strip()]
     _snake_case							=				[word for idx, word in enumerate(_SCREAMING_SNAKE_CASE   ) if idx not in irrelevant_indices]
     _snake_case							=				[coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE   ) if idx not in irrelevant_indices]
     _snake_case							=				[coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE   ) if idx not in irrelevant_indices]
     _snake_case							=				[coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE   ) if idx not in irrelevant_indices]
     _snake_case							=				[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
     _snake_case							=				[]
     for x, y, w, h in zip(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE   ):
          _snake_case							=				[x, y, x + w, y + h]
          actual_boxes.append(_SCREAMING_SNAKE_CASE   )

     # finally, normalize the bounding boxes
     _snake_case							=				[]
     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    (						__SCREAMING_SNAKE_CASE							):
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						["pixel_values"]






     def __init__(			self ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = 1 / 255 ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = "" ,**_SCREAMING_SNAKE_CASE ,)							->      Tuple:
          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							=				do_rescale
          _snake_case							=				rescale_value
          _snake_case							=				do_normalize
          _snake_case							=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _snake_case							=				image_std if image_std is not None else IMAGENET_STANDARD_STD
          _snake_case							=				apply_ocr
          _snake_case							=				ocr_lang
          _snake_case							=				tesseract_config






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      Optional[Any]:
          _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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      List[Any]:
          return rescale(_snake_case ,scale=_snake_case ,data_format=_snake_case ,**_snake_case   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      Dict:
          return normalize(_snake_case ,mean=_snake_case ,std=_snake_case ,data_format=_snake_case ,**_snake_case   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,**_SCREAMING_SNAKE_CASE ,)							->      Union[str, Any]:
          _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							=				do_rescale if do_rescale is not None else self.do_rescale
          _snake_case							=				rescale_factor if rescale_factor is not None else self.rescale_factor
          _snake_case							=				do_normalize if do_normalize is not None else self.do_normalize
          _snake_case							=				image_mean if image_mean is not None else self.image_mean
          _snake_case							=				image_std if image_std is not None else self.image_std
          _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."   )

          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.
          _snake_case							=				[to_numpy_array(_snake_case   ) for image in images]

          # Tesseract OCR to get words + normalized bounding boxes
          if apply_ocr:
               requires_backends(self ,"pytesseract"   )
               _snake_case							=				[]
               _snake_case							=				[]
               for image in images:
                    _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]

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

          if do_normalize:
               _snake_case							=				[self.normalize(image=_snake_case ,mean=_snake_case ,std=_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







'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCamelCase_						:      int       =						TypeVar('''T''')
UpperCamelCase_						:      Dict       =						TypeVar('''U''')

class        _a    (						Generic[T, U]							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          _snake_case							=				key
          _snake_case							=				val
          _snake_case							=				None
          _snake_case							=				None






     def __repr__(			self   )							->      str:
          return (
              f"""Node: key: {self.key}, val: {self.val}, """
              f"""has next: {bool(self.next   )}, has prev: {bool(self.prev   )}"""
          )







class        _a    (						Generic[T, U]							):






     def __init__(			self   )							->      None:
          _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							, _snake_case							=				self.rear, self.head






     def __repr__(			self   )							->      str:
          _snake_case							=				["DoubleLinkedList"]
          _snake_case							=				self.head
          while node.next is not None:
               rep.append(str(_SCREAMING_SNAKE_CASE   )   )
               _snake_case							=				node.next
          rep.append(str(self.rear   )   )
          return ",\n    ".join(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      None:
          _snake_case							=				self.rear.prev

          # All nodes other than self.head are guaranteed to have non-None previous
          assert previous is not None

          _snake_case							=				node
          _snake_case							=				previous
          _snake_case							=				node
          _snake_case							=				self.rear






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      DoubleLinkedListNode[T, U] | None:

          if node.prev is None or node.next is None:
               return None

          _snake_case							=				node.next
          _snake_case							=				node.prev
          _snake_case							=				None
          _snake_case							=				None
          return node







class        _a    (						Generic[T, U]							):
     SCREAMING_SNAKE_CASE_							:   dict[Callable[[T], U], LRUCache[T, U]]       =						{}






     def __init__(			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				DoubleLinkedList()
          _snake_case							=				capacity
          _snake_case							=				0
          _snake_case							=				0
          _snake_case							=				0
          _snake_case							=				{}






     def __repr__(			self   )							->      str:
          return (
              f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
              f"""capacity={self.capacity}, current size={self.num_keys})"""
          )






     def __contains__(			self ,_SCREAMING_SNAKE_CASE   )							->      bool:
          return key in self.cache






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      U | None:
          # Note: pythonic interface would throw KeyError rather than return None

          if key in self.cache:
               self.hits += 1
               _snake_case							=				self.cache[key]
               _snake_case							=				self.list.remove(self.cache[key]   )
               assert node == value_node

               # node is guaranteed not None because it is in self.cache
               assert node is not None
               self.list.add(_SCREAMING_SNAKE_CASE   )
               return node.val
          self.miss += 1
          return None






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      None:

          if key not in self.cache:
               if self.num_keys >= self.capacity:
                    # delete first node (oldest) when over capacity
                    _snake_case							=				self.list.head.next

                    # guaranteed to have a non-None first node when num_keys > 0
                    # explain to type checker via assertions
                    assert first_node is not None
                    assert first_node.key is not None
                    assert (
                        self.list.remove(_SCREAMING_SNAKE_CASE   ) is not None
                    )  # node guaranteed to be in list assert node.key is not None

                    del self.cache[first_node.key]
                    self.num_keys -= 1
               _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.list.add(self.cache[key]   )
               self.num_keys += 1

          else:
               # bump node to the end of the list, update value
               _snake_case							=				self.list.remove(self.cache[key]   )
               assert node is not None  # node guaranteed to be in list
               _snake_case							=				value
               self.list.add(_SCREAMING_SNAKE_CASE   )






     @classmethod
     def     _lowercase     (			cls ,_SCREAMING_SNAKE_CASE = 128   )							->      Callable[[Callable[[T], U]], Callable[..., U]]:

          def cache_decorator_inner(_SCREAMING_SNAKE_CASE   ) -> Callable[..., U]:
               def cache_decorator_wrapper(*_SCREAMING_SNAKE_CASE   ) -> U:
                    if func not in cls.decorator_function_to_instance_map:
                         _snake_case							=				LRUCache(_SCREAMING_SNAKE_CASE   )

                    _snake_case							=				cls.decorator_function_to_instance_map[func].get(args[0]   )
                    if result is None:
                         _snake_case							=				func(*_SCREAMING_SNAKE_CASE   )
                         cls.decorator_function_to_instance_map[func].put(args[0] ,_SCREAMING_SNAKE_CASE   )
                    return result

               def cache_info() -> LRUCache[T, U]:
                    return cls.decorator_function_to_instance_map[func]

               setattr(_SCREAMING_SNAKE_CASE ,"cache_info" ,_SCREAMING_SNAKE_CASE   )  # noqa: B010

               return cache_decorator_wrapper

          return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()


