



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

a_    :  Any						  =		logging.get_logger(__name__)

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      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			):


      """simple docstring"""



      lowerCamelCase             =		tesseract_config if tesseract_config is not None else ""

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

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

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

      # finally, normalize the bounding boxes
      lowerCamelCase             =		[]
      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  lowerCamelCase__	(    a__):
     """simple docstring"""



     _A      =      ['pixel_values']







     def __init__(self					,      __a = True					,      __a = None					,      __a = PILImageResampling.BILINEAR					,      __a = True					,      __a = None					,      __a = ""					,      **__a					,      ):






           '''simple docstring'''



           super().__init__(**_snake_case     )
           lowerCamelCase             =		size if size is not None else {"height": 2_24, "width": 2_24}
           lowerCamelCase             =		get_size_dict(_snake_case     )

           lowerCamelCase             =		do_resize
           lowerCamelCase             =		size
           lowerCamelCase             =		resample
           lowerCamelCase             =		apply_ocr
           lowerCamelCase             =		ocr_lang
           lowerCamelCase             =		tesseract_config







     def     _a   (self					,      __a					,      __a					,      __a = PILImageResampling.BILINEAR					,      __a = None					,      **__a					,      ):






           '''simple docstring'''



           lowerCamelCase             =		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()}"""     )
           lowerCamelCase             =		(size["height"], size["width"])
           return resize(_snake_case					,      size=_snake_case					,      resample=_snake_case					,      data_format=_snake_case					,      **_snake_case     )







     def     _a   (self					,      __a					,      __a = None					,      __a = None					,      __a = None					,      __a = None					,      __a = None					,      __a = None					,      __a = None					,      __a = ChannelDimension.FIRST					,      **__a					,      ):






           '''simple docstring'''



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

           lowerCamelCase             =		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."     )

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

           if apply_ocr:
                 requires_backends(self					,      "pytesseract"     )
                 lowerCamelCase             =		[]
                 lowerCamelCase             =		[]
                 for image in images:
                       lowerCamelCase      ,				lowerCamelCase             =		apply_tesseract(_snake_case					,      _snake_case					,      _snake_case     )
                       words_batch.append(_snake_case     )
                       boxes_batch.append(_snake_case     )

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

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

           lowerCamelCase             =		BatchFeature(data={"pixel_values": images}					,      tensor_type=_snake_case     )

           if apply_ocr:
                 lowerCamelCase             =		words_batch
                 lowerCamelCase             =		boxes_batch
           return data






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__UpperCamelCase     :						Tuple						       =  {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class       a							(   a__  ):
 snake_case__             =       '''glpn'''



 def __init__(     self				,       _snake_case=3				,       _snake_case=4				,       _snake_case=[2, 2, 2, 2]				,       _snake_case=[8, 4, 2, 1]				,       _snake_case=[32, 64, 1_60, 2_56]				,       _snake_case=[7, 3, 3, 3]				,       _snake_case=[4, 2, 2, 2]				,       _snake_case=[1, 2, 5, 8]				,       _snake_case=[4, 4, 4, 4]				,       _snake_case="gelu"				,       _snake_case=0.0				,       _snake_case=0.0				,       _snake_case=0.02				,       _snake_case=0.1				,       _snake_case=1E-6				,       _snake_case=64				,       _snake_case=10				,       _snake_case=-1				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(**_snake_case     )

  lowerCAmelCase    				= num_channels
  lowerCAmelCase    				= num_encoder_blocks
  lowerCAmelCase    				= depths
  lowerCAmelCase    				= sr_ratios
  lowerCAmelCase    				= hidden_sizes
  lowerCAmelCase    				= patch_sizes
  lowerCAmelCase    				= strides
  lowerCAmelCase    				= mlp_ratios
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= drop_path_rate
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= decoder_hidden_size
  lowerCAmelCase    				= max_depth
  lowerCAmelCase    				= head_in_index





