


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

__snake_case									=		logging.get_logger(__name__)






def 						_A						(						_lowercase ,   _lowercase ,   _lowercase      ) ->      Union[str, Any]:


     """simple docstring"""


     return [
         int(10_00 * (box[0] / width)      ),
         int(10_00 * (box[1] / height)      ),
         int(10_00 * (box[2] / width)      ),
         int(10_00 * (box[3] / height)      ),
     ]






def 						_A						(						_lowercase ,   _lowercase ,   _lowercase = None      ) ->      Any:


     """simple docstring"""


     __UpperCamelCase      =       tesseract_config if tesseract_config is not None else ''

     # apply OCR
     __UpperCamelCase      =       to_pil_image(__A      )
     __UpperCamelCase,  __UpperCamelCase      =       pil_image.size
     __UpperCamelCase      =       pytesseract.image_to_data(__A ,   lang=__A ,   output_type='dict' ,   config=__A      )
     __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(__A      ) if not word.strip()]
     __UpperCamelCase      =       [word for idx, word in enumerate(__A      ) if idx not in irrelevant_indices]
     __UpperCamelCase      =       [coord for idx, coord in enumerate(__A      ) if idx not in irrelevant_indices]
     __UpperCamelCase      =       [coord for idx, coord in enumerate(__A      ) if idx not in irrelevant_indices]
     __UpperCamelCase      =       [coord for idx, coord in enumerate(__A      ) if idx not in irrelevant_indices]
     __UpperCamelCase      =       [coord for idx, coord in enumerate(__A      ) if idx not in irrelevant_indices]

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

     # finally, normalize the bounding boxes
     __UpperCamelCase      =       []
     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      __lowerCamelCase       (_lowerCamelCase		):
       _lowercase											=			["""pixel_values"""]







       def __init__(       self:      List[str],A_:      Any = True,A_:      List[str] = None,A_:      Tuple = PILImageResampling.BILINEAR,A_:      Dict = True,A_:      List[str] = None,A_:      int = "",**A_:      Tuple,):






            '''simple docstring'''




            super().__init__(**lowerCAmelCase_ )
            __UpperCamelCase      =       size if size is not None else {'height': 224, 'width': 224}
            __UpperCamelCase      =       get_size_dict(lowerCAmelCase_ )

            __UpperCamelCase      =       do_resize
            __UpperCamelCase      =       size
            __UpperCamelCase      =       resample
            __UpperCamelCase      =       apply_ocr
            __UpperCamelCase      =       ocr_lang
            __UpperCamelCase      =       tesseract_config







       def 						snake_case_     (       self:      Tuple,A_:      Optional[Any],A_:      Any,A_:      Dict = PILImageResampling.BILINEAR,A_:      Tuple = None,**A_:      str,):






            '''simple docstring'''




            __UpperCamelCase      =       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()}''' )
            __UpperCamelCase      =       (size['height'], size['width'])
            return resize(lowerCAmelCase_,size=lowerCAmelCase_,resample=lowerCAmelCase_,data_format=lowerCAmelCase_,**lowerCAmelCase_ )







       def 						snake_case_     (       self:      Tuple,A_:      Tuple,A_:      str = None,A_:      str = None,A_:      Optional[Any] = None,A_:      Dict = None,A_:      Union[str, Any] = None,A_:      Optional[Any] = None,A_:      Tuple = None,A_:      Union[str, Any] = ChannelDimension.FIRST,**A_:      List[Any],):






            '''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(lowerCAmelCase_ )
            __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(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.' )

            # All transformations expect numpy arrays.
            __UpperCamelCase      =       [to_numpy_array(lowerCAmelCase_ ) for image in images]

            if apply_ocr:
                 requires_backends(self,'pytesseract' )
                 __UpperCamelCase      =       []
                 __UpperCamelCase      =       []
                 for image in images:
                      __UpperCamelCase,  __UpperCamelCase      =       apply_tesseract(lowerCAmelCase_,lowerCAmelCase_,lowerCAmelCase_ )
                      words_batch.append(lowerCAmelCase_ )
                      boxes_batch.append(lowerCAmelCase_ )

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

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

            __UpperCamelCase      =       BatchFeature(data={'pixel_values': images},tensor_type=lowerCAmelCase_ )

            if apply_ocr:
                 __UpperCamelCase      =       words_batch
                 __UpperCamelCase      =       boxes_batch
            return data




'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

