'''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

lowerCAmelCase_ : List[Any]						  =      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						List[str]				)	->							List[str]:
      '''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 (	__lowerCamelCase			:						np.ndarray ,		__lowerCamelCase			:						Optional[str] ,		__lowerCamelCase			:						Optional[str]				)	->							Optional[Any]:
      '''simple docstring'''


      lowercase_     =      to_pil_image(__lowerCamelCase				)
      lowercase_ ,							lowercase_     =      pil_image.size
      lowercase_     =      pytesseract.image_to_data(__lowerCamelCase ,		lang=__lowerCamelCase ,		output_type="dict" ,		config=__lowerCamelCase				)
      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(__lowerCamelCase				) if not word.strip()]
      lowercase_     =      [word for idx, word in enumerate(__lowerCamelCase				) if idx not in irrelevant_indices]
      lowercase_     =      [coord for idx, coord in enumerate(__lowerCamelCase				) if idx not in irrelevant_indices]
      lowercase_     =      [coord for idx, coord in enumerate(__lowerCamelCase				) if idx not in irrelevant_indices]
      lowercase_     =      [coord for idx, coord in enumerate(__lowerCamelCase				) if idx not in irrelevant_indices]
      lowercase_     =      [coord for idx, coord in enumerate(__lowerCamelCase				) if idx not in irrelevant_indices]

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

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

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

      return words, normalized_boxes






class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Dict[str, int] = None   ,		__lowerCAmelCase				:	PILImageResampling = PILImageResampling.BILINEAR   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	float = 1 / 255   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Union[float, Iterable[float]] = None   ,		__lowerCAmelCase				:	Union[float, Iterable[float]] = None   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Optional[str] = None   ,		__lowerCAmelCase				:	Optional[str] = ""   ,		**__lowerCAmelCase				:	str   ,		)      ->					None:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      size if size is not None else {"height": 224, "width": 224}
         lowercase_     =      get_size_dict(__lowerCAmelCase)

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


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Dict[str, int]   ,		__lowerCAmelCase				:	PILImageResampling = PILImageResampling.BILINEAR   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Any   ,		)      ->					np.ndarray:
         lowercase_     =      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()}')
         lowercase_     =      (size["height"], size["width"])
         return resize(__lowerCAmelCase   ,		size=__lowerCAmelCase   ,		resample=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Union[int, float]   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					np.ndarray:
         return rescale(__lowerCAmelCase   ,		scale=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Union[float, Iterable[float]]   ,		__lowerCAmelCase				:	Union[float, Iterable[float]]   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Any   ,		)      ->					np.ndarray:
         return normalize(__lowerCAmelCase   ,		mean=__lowerCAmelCase   ,		std=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	ImageInput   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	Dict[str, int] = None   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	float = None   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	Union[float, Iterable[float]] = None   ,		__lowerCAmelCase				:	Union[float, Iterable[float]] = None   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	Optional[str] = None   ,		__lowerCAmelCase				:	Optional[str] = None   ,		__lowerCAmelCase				:	Optional[Union[str, TensorType]] = None   ,		__lowerCAmelCase				:	ChannelDimension = ChannelDimension.FIRST   ,		**__lowerCAmelCase				:	Any   ,		)      ->					PIL.Image.Image:
         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(__lowerCAmelCase)
         lowercase_     =      resample if resample is not None else self.resample
         lowercase_     =      do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     =      rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase_     =      do_normalize if do_normalize is not None else self.do_normalize
         lowercase_     =      image_mean if image_mean is not None else self.image_mean
         lowercase_     =      image_std if image_std is not None else self.image_std
         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(__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.
         lowercase_     =      [to_numpy_array(__lowerCAmelCase) for image in images]

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

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

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

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

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

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

         if apply_ocr:
               lowercase_     =      words_batch
               lowercase_     =      boxes_batch
         return data







'''simple docstring'''

from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	str   ,		__lowerCAmelCase				:	Callable   ,		__lowerCAmelCase				:	Optional[Features] = None   ,		__lowerCAmelCase				:	str = None   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[dict] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					List[Any]:
         super().__init__(
             features=__lowerCAmelCase   ,		cache_dir=__lowerCAmelCase   ,		keep_in_memory=__lowerCAmelCase   ,		streaming=__lowerCAmelCase   ,		num_proc=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)
         lowercase_     =      Generator(
             cache_dir=__lowerCAmelCase   ,		features=__lowerCAmelCase   ,		generator=__lowerCAmelCase   ,		gen_kwargs=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					List[str]:
         # Build iterable dataset
         if self.streaming:
               lowercase_     =      self.builder.as_streaming_dataset(split="train")
         # Build regular (map-style) dataset
         else:
               lowercase_     =      None
               lowercase_     =      None
               lowercase_     =      None
               lowercase_     =      None

               self.builder.download_and_prepare(
                   download_config=__lowerCAmelCase   ,		download_mode=__lowerCAmelCase   ,		verification_mode=__lowerCAmelCase   ,		base_path=__lowerCAmelCase   ,		num_proc=self.num_proc   ,		)
               lowercase_     =      self.builder.as_dataset(
                   split="train"   ,		verification_mode=__lowerCAmelCase   ,		in_memory=self.keep_in_memory)
         return dataset






