


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

lowerCamelCase											=       logging.get_logger(__name__)




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      List[Any]		,			SCREAMING_SNAKE_CASE__					:      Dict     ):
       '''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 	a_      (    SCREAMING_SNAKE_CASE__					:      np.ndarray		,			SCREAMING_SNAKE_CASE__					:      Optional[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[str]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		=to_pil_image(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase	,     _lowerCamelCase       :		int		=pil_image.size
       _lowerCamelCase       :		Optional[int]		=pytesseract.image_to_data(SCREAMING_SNAKE_CASE__		,			lang=SCREAMING_SNAKE_CASE__		,			output_type='dict'		,			config=SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		List[str]		=data['text'], data['left'], data['top'], data['width'], data['height']

       # filter empty words and corresponding coordinates
       _lowerCamelCase       :		List[str]		=[idx for idx, word in enumerate(SCREAMING_SNAKE_CASE__     ) if not word.strip()]
       _lowerCamelCase       :		Dict		=[word for idx, word in enumerate(SCREAMING_SNAKE_CASE__     ) if idx not in irrelevant_indices]
       _lowerCamelCase       :		List[Any]		=[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__     ) if idx not in irrelevant_indices]
       _lowerCamelCase       :		List[str]		=[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__     ) if idx not in irrelevant_indices]
       _lowerCamelCase       :		Tuple		=[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__     ) if idx not in irrelevant_indices]
       _lowerCamelCase       :		Optional[Any]		=[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__     ) if idx not in irrelevant_indices]

       # turn coordinates into (left, top, left+width, top+height) format
       _lowerCamelCase       :		str		=[]
       for x, y, w, h in zip(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     ):
              _lowerCamelCase       :		Union[str, Any]		=[x, y, x + w, y + h]
              actual_boxes.append(SCREAMING_SNAKE_CASE__     )

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

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

       return words, normalized_boxes




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Dict      =['pixel_values']






      def __init__(					self						:     str			,   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_						:     List[str]			,   )					->						None:
             """simple docstring"""




             super().__init__(**lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=size if size is not None else {'height': 224, 'width': 224}
             _lowerCamelCase       :		List[str]		=get_size_dict(lowercase_ )

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






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     np.ndarray			,   lowercase_						:     Dict[str, int]			,   lowercase_						:     PILImageResampling = PILImageResampling.BILINEAR			,   lowercase_						:     Optional[Union[str, ChannelDimension]] = None			,   **lowercase_						:     Tuple			,   )					->						np.ndarray:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=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()}''' )
             _lowerCamelCase       :		Optional[Any]		=(size['height'], size['width'])
             return resize(lowercase_			,   size=lowercase_			,   resample=lowercase_			,   data_format=lowercase_			,   **lowercase_ )






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     np.ndarray			,   lowercase_						:     Union[int, float]			,   lowercase_						:     Optional[Union[str, ChannelDimension]] = None			,   **lowercase_						:     List[str]			,   )					->						np.ndarray:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     np.ndarray			,   lowercase_						:     Union[float, Iterable[float]]			,   lowercase_						:     Union[float, Iterable[float]]			,   lowercase_						:     Optional[Union[str, ChannelDimension]] = None			,   **lowercase_						:     int			,   )					->						np.ndarray:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     ImageInput			,   lowercase_						:     bool = None			,   lowercase_						:     Dict[str, int] = None			,   lowercase_						:     Optional[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_						:     List[str]			,   )					->						PIL.Image.Image:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=do_resize if do_resize is not None else self.do_resize
             _lowerCamelCase       :		str		=size if size is not None else self.size
             _lowerCamelCase       :		Optional[int]		=get_size_dict(lowercase_ )
             _lowerCamelCase       :		Tuple		=resample if resample is not None else self.resample
             _lowerCamelCase       :		Optional[Any]		=do_rescale if do_rescale is not None else self.do_rescale
             _lowerCamelCase       :		Optional[Any]		=rescale_factor if rescale_factor is not None else self.rescale_factor
             _lowerCamelCase       :		Any		=do_normalize if do_normalize is not None else self.do_normalize
             _lowerCamelCase       :		Union[str, Any]		=image_mean if image_mean is not None else self.image_mean
             _lowerCamelCase       :		int		=image_std if image_std is not None else self.image_std
             _lowerCamelCase       :		Union[str, Any]		=apply_ocr if apply_ocr is not None else self.apply_ocr
             _lowerCamelCase       :		int		=ocr_lang if ocr_lang is not None else self.ocr_lang
             _lowerCamelCase       :		str		=tesseract_config if tesseract_config is not None else self.tesseract_config

             _lowerCamelCase       :		Any		=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.
             _lowerCamelCase       :		List[str]		=[to_numpy_array(lowercase_ ) for image in images]

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

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

             if do_rescale:
                    _lowerCamelCase       :		Optional[Any]		=[self.rescale(image=lowercase_			,   scale=lowercase_ ) for image in images]

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

             _lowerCamelCase       :		Optional[int]		=[to_channel_dimension_format(lowercase_			,   lowercase_ ) for image in images]

             _lowerCamelCase       :		Tuple		=BatchFeature(data={'pixel_values': images}			,   tensor_type=lowercase_ )

             if apply_ocr:
                    _lowerCamelCase       :		List[str]		=words_batch
                    _lowerCamelCase       :		Tuple		=boxes_batch
             return data






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
 from .camera import create_pan_cameras
 from .pipeline_shap_e import ShapEPipeline
 from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
 from .renderer import (
     BoundingBoxVolume,
     ImportanceRaySampler,
     MLPNeRFModelOutput,
     MLPNeRSTFModel,
     ShapEParamsProjModel,
     ShapERenderer,
     StratifiedRaySampler,
     VoidNeRFModel,
 )


