





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__			:       int       ,    UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Tuple						)			-> Optional[int]:



     '''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 						lowerCamelCase__     (     UpperCamelCase__			:       np.ndarray       ,    UpperCamelCase__			:       Optional[str]       ,    UpperCamelCase__			:       Optional[str] = None						)			-> int:



     '''simple docstring'''




     _snake_case									=				tesseract_config if tesseract_config is not None else ''

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

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

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

     # finally, normalize the bounding boxes
     _snake_case									=				[]
     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 			UpperCamelCase_		(  _lowerCamelCase					):
     lowerCAmelCase_							=	['''pixel_values''']




     def __init__(		self				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ""				,						**lowerCAmelCase_				,						)		->							None:
          super().__init__(**lowerCAmelCase_  )
          _snake_case									=				size if size is not None else {'height': 224, 'width': 224}
          _snake_case									=				get_size_dict(lowerCAmelCase_  )

          _snake_case									=				do_resize
          _snake_case									=				size
          _snake_case									=				resample
          _snake_case									=				apply_ocr
          _snake_case									=				ocr_lang
          _snake_case									=				tesseract_config




     def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							np.ndarray:
          _snake_case									=				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()}'''  )
          _snake_case									=				(size['height'], size['width'])
          return resize(lowerCAmelCase_				,						size=lowerCAmelCase_				,						resample=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )






     def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ChannelDimension.FIRST				,						**lowerCAmelCase_				,						)		->							PIL.Image.Image:
          _snake_case									=				do_resize if do_resize is not None else self.do_resize
          _snake_case									=				size if size is not None else self.size
          _snake_case									=				get_size_dict(lowerCAmelCase_  )
          _snake_case									=				resample if resample is not None else self.resample
          _snake_case									=				apply_ocr if apply_ocr is not None else self.apply_ocr
          _snake_case									=				ocr_lang if ocr_lang is not None else self.ocr_lang
          _snake_case									=				tesseract_config if tesseract_config is not None else self.tesseract_config

          _snake_case									=				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.
          _snake_case									=				[to_numpy_array(lowerCAmelCase_  ) for image in images]

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

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

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

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

          if apply_ocr:
               _snake_case									=				words_batch
               _snake_case									=				boxes_batch
          return data










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      							=     logging.get_logger(__name__)

UpperCAmelCase_      							=     {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	'''vit_msn'''




					def __init__(		self				,						lowerCAmelCase_=768				,						lowerCAmelCase_=12				,						lowerCAmelCase_=12				,						lowerCAmelCase_=3072				,						lowerCAmelCase_="gelu"				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=1E-06				,						lowerCAmelCase_=224				,						lowerCAmelCase_=16				,						lowerCAmelCase_=3				,						lowerCAmelCase_=True				,						**lowerCAmelCase_				,						)		->							str:
										super().__init__(**lowerCAmelCase_  )

										_snake_case									=				hidden_size
										_snake_case									=				num_hidden_layers
										_snake_case									=				num_attention_heads
										_snake_case									=				intermediate_size
										_snake_case									=				hidden_act
										_snake_case									=				hidden_dropout_prob
										_snake_case									=				attention_probs_dropout_prob
										_snake_case									=				initializer_range
										_snake_case									=				layer_norm_eps
										_snake_case									=				image_size
										_snake_case									=				patch_size
										_snake_case									=				num_channels
										_snake_case									=				qkv_bias



