



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

SCREAMING_SNAKE_CASE_:str					=						logging.get_logger(__name__)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    return [
        int(1000 * (box[0] / width)		),
        int(1000 * (box[1] / height)		),
        int(1000 * (box[2] / width)		),
        int(1000 * (box[3] / height)		),
    ]




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  List[Any]:



    """simple docstring"""






    A			:	Tuple										=       to_pil_image(_lowerCAmelCase		)
    A			:	Tuple										=       pil_image.size
    A			:	List[Any]										=       pytesseract.image_to_data(_lowerCAmelCase				,					lang=_lowerCAmelCase				,					output_type="""dict"""				,					config=_lowerCAmelCase		)
    A			:	Any										=       data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

    # filter empty words and corresponding coordinates
    A			:	List[Any]										=       [idx for idx, word in enumerate(_lowerCAmelCase		) if not word.strip()]
    A			:	List[Any]										=       [word for idx, word in enumerate(_lowerCAmelCase		) if idx not in irrelevant_indices]
    A			:	Optional[Any]										=       [coord for idx, coord in enumerate(_lowerCAmelCase		) if idx not in irrelevant_indices]
    A			:	int										=       [coord for idx, coord in enumerate(_lowerCAmelCase		) if idx not in irrelevant_indices]
    A			:	Any										=       [coord for idx, coord in enumerate(_lowerCAmelCase		) if idx not in irrelevant_indices]
    A			:	Optional[int]										=       [coord for idx, coord in enumerate(_lowerCAmelCase		) if idx not in irrelevant_indices]

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

    # finally, normalize the bounding boxes
    A			:	Tuple										=       []
    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 SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):
    __lowerCamelCase      :			Union[str, Any] =				["pixel_values"]






    def __init__(	self,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = PILImageResampling.BILINEAR,		lowerCamelCase__ = True,		lowerCamelCase__ = 1 / 255,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = "",		**lowerCamelCase__,		):
        super().__init__(**lowerCamelCase__		)
        A			:	List[str]										=       size if size is not None else {"""height""": 224, """width""": 224}
        A			:	List[str]										=       get_size_dict(lowerCamelCase__		)

        A			:	Optional[int]										=       do_resize
        A			:	int										=       size
        A			:	Union[str, Any]										=       resample
        A			:	Any										=       do_rescale
        A			:	int										=       rescale_value
        A			:	str										=       do_normalize
        A			:	List[str]										=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        A			:	Tuple										=       image_std if image_std is not None else IMAGENET_STANDARD_STD
        A			:	Optional[int]										=       apply_ocr
        A			:	Any										=       ocr_lang
        A			:	Any										=       tesseract_config






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = PILImageResampling.BILINEAR,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A			:	str										=       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()}'''		)
        A			:	Optional[Any]										=       (size["""height"""], size["""width"""])
        return resize(lowerCamelCase__,		size=lowerCamelCase__,		resample=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        return rescale(lowerCamelCase__,		scale=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        return normalize(lowerCamelCase__,		mean=lowerCamelCase__,		std=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__=None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = ChannelDimension.FIRST,		**lowerCamelCase__,		):
        A			:	Optional[int]										=       do_resize if do_resize is not None else self.do_resize
        A			:	str										=       size if size is not None else self.size
        A			:	Any										=       get_size_dict(lowerCamelCase__		)
        A			:	Tuple										=       resample if resample is not None else self.resample
        A			:	Any										=       do_rescale if do_rescale is not None else self.do_rescale
        A			:	Dict										=       rescale_factor if rescale_factor is not None else self.rescale_factor
        A			:	Dict										=       do_normalize if do_normalize is not None else self.do_normalize
        A			:	Optional[Any]										=       image_mean if image_mean is not None else self.image_mean
        A			:	Optional[int]										=       image_std if image_std is not None else self.image_std
        A			:	Dict										=       apply_ocr if apply_ocr is not None else self.apply_ocr
        A			:	Union[str, Any]										=       ocr_lang if ocr_lang is not None else self.ocr_lang
        A			:	Union[str, Any]										=       tesseract_config if tesseract_config is not None else self.tesseract_config

        A			:	str										=       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."""		)

        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.
        A			:	Optional[Any]										=       [to_numpy_array(lowerCamelCase__		) for image in images]

        # Tesseract OCR to get words + normalized bounding boxes
        if apply_ocr:
            requires_backends(self,		"""pytesseract"""		)
            A			:	Optional[Any]										=       []
            A			:	Optional[int]										=       []
            for image in images:
                A			:	Tuple										=       apply_tesseract(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
                words_batch.append(lowerCamelCase__		)
                boxes_batch.append(lowerCamelCase__		)

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

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

        if do_normalize:
            A			:	Union[str, Any]										=       [self.normalize(image=lowerCamelCase__,		mean=lowerCamelCase__,		std=lowerCamelCase__		) for image in images]

        A			:	List[str]										=       [to_channel_dimension_format(lowerCamelCase__,		lowerCamelCase__		) for image in images]

        A			:	Union[str, Any]										=       BatchFeature(data={"""pixel_values""": images},		tensor_type=lowerCamelCase__		)

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_:Any					=						{
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:List[Any]					=						["""MobileNetV2FeatureExtractor"""]
     SCREAMING_SNAKE_CASE_:Tuple					=						["""MobileNetV2ImageProcessor"""]


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any					=						[
         """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileNetV2ForImageClassification""",
         """MobileNetV2ForSemanticSegmentation""",
         """MobileNetV2Model""",
         """MobileNetV2PreTrainedModel""",
         """load_tf_weights_in_mobilenet_v2""",
     ]


if TYPE_CHECKING:
     from .configuration_mobilenet_va import (
         MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileNetVaConfig,
         MobileNetVaOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
          from .image_processing_mobilenet_va import MobileNetVaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilenet_va import (
              MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileNetVaForImageClassification,
              MobileNetVaForSemanticSegmentation,
              MobileNetVaModel,
              MobileNetVaPreTrainedModel,
              load_tf_weights_in_mobilenet_va,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:Optional[Any]					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


