





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

UpperCAmelCase_     							= logging.get_logger(__name__)




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Any		,   __UpperCAmelCase:			List[str]		,   __UpperCAmelCase:			Any      )      ->	Union[str, Any]:
    return [
        int(1000 * (box[0] / width)      ),
        int(1000 * (box[1] / height)      ),
        int(1000 * (box[2] / width)      ),
        int(1000 * (box[3] / height)      ),
    ]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			np.ndarray		,   __UpperCAmelCase:			Optional[str]		,   __UpperCAmelCase:			Optional[str] = None      )      ->	Optional[Any]:
    UpperCamelCase__							:				Dict          =      tesseract_config if tesseract_config is not None else """"""

    # apply OCR
    UpperCamelCase__							:				Optional[Any]          =      to_pil_image(_lowerCamelCase      )
    UpperCamelCase__							:				str          =      pil_image.size
    UpperCamelCase__							:				Dict          =      pytesseract.image_to_data(_lowerCamelCase		,   lang=_lowerCamelCase		,   output_type='''dict'''		,   config=_lowerCamelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

    # filter empty words and corresponding coordinates
    UpperCamelCase__							:				List[str]          =      [idx for idx, word in enumerate(_lowerCamelCase      ) if not word.strip()]
    UpperCamelCase__							:				List[Any]          =      [word for idx, word in enumerate(_lowerCamelCase      ) if idx not in irrelevant_indices]
    UpperCamelCase__							:				Tuple          =      [coord for idx, coord in enumerate(_lowerCamelCase      ) if idx not in irrelevant_indices]
    UpperCamelCase__							:				Dict          =      [coord for idx, coord in enumerate(_lowerCamelCase      ) if idx not in irrelevant_indices]
    UpperCamelCase__							:				Dict          =      [coord for idx, coord in enumerate(_lowerCamelCase      ) if idx not in irrelevant_indices]
    UpperCamelCase__							:				Optional[Any]          =      [coord for idx, coord in enumerate(_lowerCamelCase      ) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    UpperCamelCase__							:				List[str]          =      []
    for x, y, w, h in zip(_lowerCamelCase		,   _lowerCamelCase		,   _lowerCamelCase		,   _lowerCamelCase      ):
        UpperCamelCase__							:				Optional[Any]          =      [x, y, x + w, y + h]
        actual_boxes.append(_lowerCamelCase      )

    # finally, normalize the bounding boxes
    UpperCamelCase__							:				int          =      []
    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__       (	SCREAMING_SNAKE_CASE_				):

   '''simple docstring'''


   a						:							Optional[int]   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = None,		__magic_name__ = PILImageResampling.BILINEAR,		__magic_name__ = True,		__magic_name__ = None,		__magic_name__ = "",		**__magic_name__,		) ->      Optional[int]:






       """simple docstring"""
       super().__init__(**a__							)
       UpperCamelCase__							:				Tuple          =      size if size is not None else {"""height""": 224, """width""": 224}
       UpperCamelCase__							:				Dict          =      get_size_dict(a__							)

       UpperCamelCase__							:				int          =      do_resize
       UpperCamelCase__							:				Optional[int]          =      size
       UpperCamelCase__							:				Optional[Any]          =      resample
       UpperCamelCase__							:				List[Any]          =      apply_ocr
       UpperCamelCase__							:				Dict          =      ocr_lang
       UpperCamelCase__							:				Tuple          =      tesseract_config




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = PILImageResampling.BILINEAR,		__magic_name__ = None,		**__magic_name__,		) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      get_size_dict(a__							)
       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__							:				Optional[int]          =      (size["""height"""], size["""width"""])
       return resize(a__,		size=a__,		resample=a__,		data_format=a__,		**a__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				str          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				str          =      size if size is not None else self.size
       UpperCamelCase__							:				Optional[Any]          =      get_size_dict(a__							)
       UpperCamelCase__							:				List[Any]          =      resample if resample is not None else self.resample
       UpperCamelCase__							:				Any          =      apply_ocr if apply_ocr is not None else self.apply_ocr
       UpperCamelCase__							:				Dict          =      ocr_lang if ocr_lang is not None else self.ocr_lang
       UpperCamelCase__							:				int          =      tesseract_config if tesseract_config is not None else self.tesseract_config

       UpperCamelCase__							:				str          =      make_list_of_images(a__							)

       if not valid_images(a__							):
           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__							:				Optional[int]          =      [to_numpy_array(a__							) for image in images]

       if apply_ocr:
           requires_backends(self,		'''pytesseract'''							)
           UpperCamelCase__							:				List[str]          =      []
           UpperCamelCase__							:				Optional[Any]          =      []
           for image in images:
               UpperCamelCase__							:				Union[str, Any]          =      apply_tesseract(a__,		a__,		a__							)
               words_batch.append(a__							)
               boxes_batch.append(a__							)

       if do_resize:
           UpperCamelCase__							:				List[Any]          =      [self.resize(image=a__,		size=a__,		resample=a__							) for image in images]

       # flip color channels from RGB to BGR (as Detectron2 requires this)
       UpperCamelCase__							:				Union[str, Any]          =      [flip_channel_order(a__							) for image in images]
       UpperCamelCase__							:				Tuple          =      [to_channel_dimension_format(a__,		a__							) for image in images]

       UpperCamelCase__							:				Any          =      BatchFeature(data={'''pixel_values''': images},		tensor_type=a__							)

       if apply_ocr:
           UpperCamelCase__							:				Optional[Any]          =      words_batch
           UpperCamelCase__							:				Optional[int]          =      boxes_batch
       return data


"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_a      :		Dict           =   datasets.utils.logging.get_logger(__name__)
@dataclass
class 						__A							( datasets.BuilderConfig		):
     _UpperCamelCase							:			int							=		10_000
     _UpperCamelCase							:			Optional[List[str]]							=		None
     _UpperCamelCase							:			Optional[datasets.Features]							=		None







class 						__A							( datasets.ArrowBasedBuilder		):
     _UpperCamelCase							:			List[str]							=		ParquetConfig






     def     __A  (		self  ):
       return datasets.DatasetInfo(features=self.config.features  )






     def     __A  (		self				,     a__  ):
       if not self.config.data_files:
         raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"  )
       _lowerCAmelCase						:       Optional[Any]  			=     dl_manager.download_and_extract(self.config.data_files  )
       if isinstance(a__				,     (str, list, tuple)  ):
         _lowerCAmelCase						:       Any  			=     data_files
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Tuple  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Any  			=     [dl_manager.iter_files(a__  ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,     gen_kwargs={"""files""": files}  )]
       _lowerCAmelCase						:       Optional[Any]  			=     []
       for split_name, files in data_files.items():
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Dict  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Tuple  			=     [dl_manager.iter_files(a__  ) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(a__  ):
             with open(a__				,     """rb"""  ) as f:
               _lowerCAmelCase						:       Optional[Any]  			=     datasets.Features.from_arrow_schema(pq.read_schema(a__  )  )
             break
         splits.append(datasets.SplitGenerator(name=a__				,     gen_kwargs={"""files""": files}  )  )
       return splits






     def     __A  (		self				,     a__  ):
       if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         _lowerCAmelCase						:       Optional[int]  			=     table_cast(a__				,     self.info.features.arrow_schema  )
       return pa_table






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema  ) != sorted(self.config.columns  ):
           raise ValueError(
               F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"  )
       for file_idx, file in enumerate(itertools.chain.from_iterable(a__  )  ):
         with open(a__				,     """rb"""  ) as f:
           _lowerCAmelCase						:       Tuple  			=     pq.ParquetFile(a__  )
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size				,     columns=self.config.columns  )  ):
               _lowerCAmelCase						:       Any  			=     pa.Table.from_batches([record_batch]  )
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield F"{file_idx}_{batch_idx}", self._cast_table(a__  )
           except ValueError as e:
             logger.error(F"Failed to read file '{file}' with error {type(a__  )}: {e}"  )
             raise




