






'''simple docstring'''

import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     import PIL.Image

     from .features import FeatureType


SCREAMING_SNAKE_CASE__     =       None
SCREAMING_SNAKE_CASE__     =       '<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
SCREAMING_SNAKE_CASE__     =       [
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]






@dataclass
class     a_						:
  lowercase							=				True
  lowercase							=				None
  # Automatically constructed
  lowercase							=				"""PIL.Image.Image"""
  lowercase							=				pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}    )
  lowercase							=				field(default="""Image"""     ,	init=lowerCamelCase     ,	repr=lowerCamelCase    )

  def __call__(				self				)  ->				Dict:



        """simple docstring"""






        return self.pa_type

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				dict:



        """simple docstring"""






        if config.PIL_AVAILABLE:
              import PIL.Image
        else:
              raise ImportError("""To support encoding images, please install 'Pillow'."""				)

        if isinstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = np.array(_SCREAMING_SNAKE_CASE				)

        if isinstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              return {"path": value, "bytes": None}
        elif isinstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              return {"path": None, "bytes": value}
        elif isinstance(_SCREAMING_SNAKE_CASE      ,		np.ndarray				):
              # convert the image array to PNG/TIFF bytes
              return encode_np_array(_SCREAMING_SNAKE_CASE				)
        elif isinstance(_SCREAMING_SNAKE_CASE      ,		PIL.Image.Image				):
              # convert the PIL image to bytes (default format is PNG/TIFF)
              return encode_pil_image(_SCREAMING_SNAKE_CASE				)
        elif value.get("""path"""				) is not None and os.path.isfile(value["""path"""]				):
              # we set "bytes": None to not duplicate the data if they're already available locally
              return {"bytes": None, "path": value.get("""path"""				)}
        elif value.get("""bytes"""				) is not None or value.get("""path"""				) is not None:
              # store the image bytes, and path is used to infer the image format using the file extension
              return {"bytes": value.get("""bytes"""				), "path": value.get("""path"""				)}
        else:
              raise ValueError(
                  F"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None				)  ->				"PIL.Image.Image":



        """simple docstring"""






        if not self.decode:
              raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead."""				)

        if config.PIL_AVAILABLE:
              import PIL.Image
        else:
              raise ImportError("""To support decoding images, please install 'Pillow'."""				)

        if token_per_repo_id is None:
              UpperCamelCase      = {}

        UpperCamelCase  ,UpperCamelCase      = value["""path"""], value["""bytes"""]
        if bytes_ is None:
              if path is None:
                    raise ValueError(F"An image should have one of 'path' or 'bytes' but both are None in {value}."				)
              else:
                    if is_local_path(_SCREAMING_SNAKE_CASE				):
                          UpperCamelCase      = PIL.Image.open(_SCREAMING_SNAKE_CASE				)
                    else:
                          UpperCamelCase      = path.split("""::"""				)[-1]
                          try:
                                UpperCamelCase      = string_to_dict(_SCREAMING_SNAKE_CASE      ,		config.HUB_DATASETS_URL				)["""repo_id"""]
                                UpperCamelCase      = token_per_repo_id.get(_SCREAMING_SNAKE_CASE				)
                          except ValueError:
                                UpperCamelCase      = None
                          with xopen(_SCREAMING_SNAKE_CASE      ,		"""rb"""      ,		use_auth_token=_SCREAMING_SNAKE_CASE				) as f:
                                UpperCamelCase      = BytesIO(f.read()				)
                          UpperCamelCase      = PIL.Image.open(bytes_				)
        else:
              UpperCamelCase      = PIL.Image.open(BytesIO(bytes_				)				)
        image.load()  # to avoid "Too many open files" errors
        return image

  def   A__						(				self				)  ->				Union["FeatureType", Dict[str, "FeatureType"]]:



        """simple docstring"""






        from .features import Value

        return (
            self
            if self.decode
            else {
                "bytes": Value("""binary"""				),
                "path": Value("""string"""				),
            }
        )

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				pa.StructArray:



        """simple docstring"""






        if pa.types.is_string(storage.type				):
              UpperCamelCase      = pa.array([None] * len(_SCREAMING_SNAKE_CASE				)      ,		type=pa.binary()				)
              UpperCamelCase      = pa.StructArray.from_arrays([bytes_array, storage]      ,		["""bytes""", """path"""]      ,		mask=storage.is_null()				)
        elif pa.types.is_binary(storage.type				):
              UpperCamelCase      = pa.array([None] * len(_SCREAMING_SNAKE_CASE				)      ,		type=pa.string()				)
              UpperCamelCase      = pa.StructArray.from_arrays([storage, path_array]      ,		["""bytes""", """path"""]      ,		mask=storage.is_null()				)
        elif pa.types.is_struct(storage.type				):
              if storage.type.get_field_index("""bytes"""				) >= 0:
                    UpperCamelCase      = storage.field("""bytes"""				)
              else:
                    UpperCamelCase      = pa.array([None] * len(_SCREAMING_SNAKE_CASE				)      ,		type=pa.binary()				)
              if storage.type.get_field_index("""path"""				) >= 0:
                    UpperCamelCase      = storage.field("""path"""				)
              else:
                    UpperCamelCase      = pa.array([None] * len(_SCREAMING_SNAKE_CASE				)      ,		type=pa.string()				)
              UpperCamelCase      = pa.StructArray.from_arrays([bytes_array, path_array]      ,		["""bytes""", """path"""]      ,		mask=storage.is_null()				)
        elif pa.types.is_list(storage.type				):
              UpperCamelCase      = pa.array(
                  [encode_np_array(np.array(_SCREAMING_SNAKE_CASE				)				)["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]      ,		type=pa.binary()      ,		)
              UpperCamelCase      = pa.array([None] * len(_SCREAMING_SNAKE_CASE				)      ,		type=pa.string()				)
              UpperCamelCase      = pa.StructArray.from_arrays(
                  [bytes_array, path_array]      ,		["""bytes""", """path"""]      ,		mask=bytes_array.is_null()				)
        return array_cast(_SCREAMING_SNAKE_CASE      ,		self.pa_type				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				pa.StructArray:



        """simple docstring"""






        @no_op_if_value_is_null
        def path_to_bytes(_SCREAMING_SNAKE_CASE				):
              with xopen(_SCREAMING_SNAKE_CASE      ,		"""rb"""				) as f:
                    UpperCamelCase      = f.read()
              return bytes_

        UpperCamelCase      = pa.array(
            [
                (path_to_bytes(x["""path"""]				) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
                for x in storage.to_pylist()
            ]      ,		type=pa.binary()      ,		)
        UpperCamelCase      = pa.array(
            [os.path.basename(_SCREAMING_SNAKE_CASE				) if path is not None else None for path in storage.field("""path"""				).to_pylist()]      ,		type=pa.string()      ,		)
        UpperCamelCase      = pa.StructArray.from_arrays([bytes_array, path_array]      ,		["""bytes""", """path"""]      ,		mask=bytes_array.is_null()				)
        return array_cast(_SCREAMING_SNAKE_CASE      ,		self.pa_type				)







def    lowercase__			(     )->			List[str]:
      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""   )

      global _IMAGE_COMPRESSION_FORMATS
      if _IMAGE_COMPRESSION_FORMATS is None:
            PIL.Image.init()
            UpperCamelCase      = list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
      return _IMAGE_COMPRESSION_FORMATS







def    lowercase__			(     __UpperCamelCase   )->			bytes:
      UpperCamelCase      = BytesIO()
      if image.format in list_image_compression_formats():
            UpperCamelCase      = image.format
      else:
            UpperCamelCase      = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
      image.save(__UpperCamelCase		,						format=__UpperCamelCase   )
      return buffer.getvalue()







def    lowercase__			(     __UpperCamelCase   )->			dict:
      if hasattr(__UpperCamelCase		,						"""filename"""   ) and image.filename != "":
            return {"path": image.filename, "bytes": None}
      else:
            return {"path": None, "bytes": image_to_bytes(__UpperCamelCase   )}







def    lowercase__			(     __UpperCamelCase   )->			dict:
      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""   )

      UpperCamelCase      = array.dtype
      UpperCamelCase      = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
      UpperCamelCase      = dtype.kind
      UpperCamelCase      = dtype.itemsize

      UpperCamelCase      = None

      # Multi-channel array case (only np.dtype("|u1") is allowed)
      if array.shape[2:]:
            UpperCamelCase      = np.dtype("""|u1"""   )
            if dtype_kind not in ["u", "i"]:
                  raise TypeError(
                      F"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."   )
            if dtype is not dest_dtype:
                  warnings.warn(F"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"   )
    # Exact match
      elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
            UpperCamelCase      = dtype
      else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
            while dtype_itemsize >= 1:
                  UpperCamelCase      = dtype_byteorder + dtype_kind + str(__UpperCamelCase   )
                  UpperCamelCase      = np.dtype(__UpperCamelCase   )
                  if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                        warnings.warn(F"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"   )
                        break
                  else:
                        dtype_itemsize //= 2

      if dest_dtype is None:
            raise TypeError(
                F"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"   )
      UpperCamelCase      = PIL.Image.fromarray(array.astype(__UpperCamelCase   )   )
      return {"path": None, "bytes": image_to_bytes(__UpperCamelCase   )}







def    lowercase__			(     __UpperCamelCase   )->			List[dict]:
      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""   )

      if objs:
            UpperCamelCase  ,UpperCamelCase      = first_non_null_value(__UpperCamelCase   )
            if isinstance(__UpperCamelCase		,						__UpperCamelCase   ):
                  return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
            if isinstance(__UpperCamelCase		,						np.ndarray   ):
                  UpperCamelCase      = no_op_if_value_is_null(__UpperCamelCase   )
                  return [obj_to_image_dict_func(__UpperCamelCase   ) for obj in objs]
            elif isinstance(__UpperCamelCase		,						PIL.Image.Image   ):
                  UpperCamelCase      = no_op_if_value_is_null(__UpperCamelCase   )
                  return [obj_to_image_dict_func(__UpperCamelCase   ) for obj in objs]
            else:
                  return objs
      else:
            return objs








'''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

SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			int:
      return [
          int(1000 * (box[0] / width)   ),
          int(1000 * (box[1] / height)   ),
          int(1000 * (box[2] / width)   ),
          int(1000 * (box[3] / height)   ),
      ]







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = None   )->			Dict:
      UpperCamelCase      = tesseract_config if tesseract_config is not None else """"""

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

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

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

      # finally, normalize the bounding boxes
      UpperCamelCase      = []
      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     a_						(      lowerCamelCase    ):
  lowercase							=				["""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 = None      ,		_SCREAMING_SNAKE_CASE = ""      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				None:



        """simple docstring"""






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

        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = resample
        UpperCamelCase      = apply_ocr
        UpperCamelCase      = ocr_lang
        UpperCamelCase      = tesseract_config

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				np.ndarray:



        """simple docstring"""






        UpperCamelCase      = 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()}"				)
        UpperCamelCase      = (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   A__						(				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 = ChannelDimension.FIRST      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				PIL.Image.Image:



        """simple docstring"""






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

        UpperCamelCase      = 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.
        UpperCamelCase      = [to_numpy_array(_SCREAMING_SNAKE_CASE				) for image in images]

        if apply_ocr:
              requires_backends(self      ,		"""pytesseract"""				)
              UpperCamelCase      = []
              UpperCamelCase      = []
              for image in images:
                    UpperCamelCase  ,UpperCamelCase      = 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:
              UpperCamelCase      = [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)
        UpperCamelCase      = [flip_channel_order(_SCREAMING_SNAKE_CASE				) for image in images]
        UpperCamelCase      = [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				) for image in images]

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

        if apply_ocr:
              UpperCamelCase      = words_batch
              UpperCamelCase      = boxes_batch
        return data
