

'''simple docstring'''

from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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 					__UpperCamelCase				(						lowercase__    :	Tuple,       lowercase__    :	Tuple,       lowercase__    :	Tuple			):






     '''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 					__UpperCamelCase				(						lowercase__    :	np.ndarray,       lowercase__    :	Optional[str],       lowercase__    :	Optional[str]			):






     '''simple docstring'''





     __lowercase      		=to_pil_image(lowercase__			)
     __lowercase		,  __lowercase      		=pil_image.size
     __lowercase      		=pytesseract.image_to_data(lowercase__,       lang=lowercase__,       output_type='dict',       config=lowercase__			)
     __lowercase		,  __lowercase		,  __lowercase		,  __lowercase		,  __lowercase      		=data['text'], data['left'], data['top'], data['width'], data['height']

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

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

     # finally, normalize the bounding boxes
     __lowercase      		=[]
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(lowercase__,       lowercase__,       lowercase__			)			)

     assert len(lowercase__			) == len(lowercase__			), "Not as many words as there are bounding boxes"

     return words, normalized_boxes







class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			["pixel_values"]







       def __init__(				self      :			Optional[int]    ,   __lowercase      :			bool = True    ,   __lowercase      :			Dict[str, int] = None    ,   __lowercase      :			PILImageResampling = PILImageResampling.BILINEAR    ,   __lowercase      :			bool = True    ,   __lowercase      :			float = 1 / 255    ,   __lowercase      :			bool = True    ,   __lowercase      :			Union[float, Iterable[float]] = None    ,   __lowercase      :			Union[float, Iterable[float]] = None    ,   __lowercase      :			bool = True    ,   __lowercase      :			Optional[str] = None    ,   __lowercase      :			Optional[str] = ""    ,   **__lowercase      :			Any    ,   ):







            """simple docstring"""







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

            __lowercase      		=do_resize
            __lowercase      		=size
            __lowercase      		=resample
            __lowercase      		=do_rescale
            __lowercase      		=rescale_value
            __lowercase      		=do_normalize
            __lowercase      		=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __lowercase      		=image_std if image_std is not None else IMAGENET_STANDARD_STD
            __lowercase      		=apply_ocr
            __lowercase      		=ocr_lang
            __lowercase      		=tesseract_config







       def       snake_case (				self      :			int    ,   __lowercase      :			np.ndarray    ,   __lowercase      :			Dict[str, int]    ,   __lowercase      :			PILImageResampling = PILImageResampling.BILINEAR    ,   __lowercase      :			Optional[Union[str, ChannelDimension]] = None    ,   **__lowercase      :			Dict    ,   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			np.ndarray    ,   __lowercase      :			Union[int, float]    ,   __lowercase      :			Optional[Union[str, ChannelDimension]] = None    ,   **__lowercase      :			str    ,   ):







            """simple docstring"""







            return rescale(__lowercase    ,   scale=__lowercase    ,   data_format=__lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			str    ,   __lowercase      :			np.ndarray    ,   __lowercase      :			Union[float, Iterable[float]]    ,   __lowercase      :			Union[float, Iterable[float]]    ,   __lowercase      :			Optional[Union[str, ChannelDimension]] = None    ,   **__lowercase      :			Union[str, Any]    ,   ):







            """simple docstring"""







            return normalize(__lowercase    ,   mean=__lowercase    ,   std=__lowercase    ,   data_format=__lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			Dict    ,   __lowercase      :			ImageInput    ,   __lowercase      :			bool = None    ,   __lowercase      :			Dict[str, int] = None    ,   __lowercase      :			List[Any]=None    ,   __lowercase      :			bool = None    ,   __lowercase      :			float = None    ,   __lowercase      :			bool = None    ,   __lowercase      :			Union[float, Iterable[float]] = None    ,   __lowercase      :			Union[float, Iterable[float]] = None    ,   __lowercase      :			bool = None    ,   __lowercase      :			Optional[str] = None    ,   __lowercase      :			Optional[str] = None    ,   __lowercase      :			Optional[Union[str, TensorType]] = None    ,   __lowercase      :			ChannelDimension = ChannelDimension.FIRST    ,   **__lowercase      :			int    ,   ):







            """simple docstring"""







            __lowercase      		=do_resize if do_resize is not None else self.do_resize
            __lowercase      		=size if size is not None else self.size
            __lowercase      		=get_size_dict(__lowercase   )
            __lowercase      		=resample if resample is not None else self.resample
            __lowercase      		=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase      		=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase      		=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase      		=image_mean if image_mean is not None else self.image_mean
            __lowercase      		=image_std if image_std is not None else self.image_std
            __lowercase      		=apply_ocr if apply_ocr is not None else self.apply_ocr
            __lowercase      		=ocr_lang if ocr_lang is not None else self.ocr_lang
            __lowercase      		=tesseract_config if tesseract_config is not None else self.tesseract_config

            __lowercase      		=make_list_of_images(__lowercase   )

            if not valid_images(__lowercase   ):
                 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.'   )

            if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

            if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('If do_normalize is True, image_mean and image_std must be specified.'   )

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

            # Tesseract OCR to get words + normalized bounding boxes
            if apply_ocr:
                 requires_backends(self    ,   'pytesseract'   )
                 __lowercase      		=[]
                 __lowercase      		=[]
                 for image in images:
                      __lowercase		,  __lowercase      		=apply_tesseract(__lowercase    ,   __lowercase    ,   __lowercase   )
                      words_batch.append(__lowercase   )
                      boxes_batch.append(__lowercase   )

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

            if do_rescale:
                 __lowercase      		=[self.rescale(image=__lowercase    ,   scale=__lowercase   ) for image in images]

            if do_normalize:
                 __lowercase      		=[self.normalize(image=__lowercase    ,   mean=__lowercase    ,   std=__lowercase   ) for image in images]

            __lowercase      		=[to_channel_dimension_format(__lowercase    ,   __lowercase   ) for image in images]

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

            if apply_ocr:
                 __lowercase      		=words_batch
                 __lowercase      		=boxes_batch
            return data





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase					 =	{
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase					 =	['''OwlViTFeatureExtractor''']
       UpperCAmelCase					 =	['''OwlViTImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase					 =	[
           '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''OwlViTModel''',
           '''OwlViTPreTrainedModel''',
           '''OwlViTTextModel''',
           '''OwlViTVisionModel''',
           '''OwlViTForObjectDetection''',
       ]

if TYPE_CHECKING:
       from .configuration_owlvit import (
           OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           OwlViTConfig,
           OwlViTOnnxConfig,
           OwlViTTextConfig,
           OwlViTVisionConfig,
       )
       from .processing_owlvit import OwlViTProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_owlvit import OwlViTFeatureExtractor
              from .image_processing_owlvit import OwlViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_owlvit import (
                  OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OwlViTForObjectDetection,
                  OwlViTModel,
                  OwlViTPreTrainedModel,
                  OwlViTTextModel,
                  OwlViTVisionModel,
              )

else:
       import sys

       UpperCAmelCase					 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


