


"""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

lowercase_			     =  logging.get_logger(__name__)

def        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     Union[str, Any]			) ->      Any:
		return [
		    int(1000 * (box[0] / width)			),
		    int(1000 * (box[1] / height)			),
		    int(1000 * (box[2] / width)			),
		    int(1000 * (box[3] / height)			),
		]

def        lowercase							(		lowerCAmelCase__ :     np.ndarray    ,					lowerCAmelCase__ :     Optional[str]    ,					lowerCAmelCase__ :     Optional[str]			) ->      str:
		__a           =			to_pil_image(lowerCAmelCase__			)
		__a      ,     __a           =			pil_image.size
		__a           =			pytesseract.image_to_data(lowerCAmelCase__    ,					lang=lowerCAmelCase__    ,					output_type='''dict'''    ,					config=lowerCAmelCase__			)
		__a      ,     __a      ,     __a      ,     __a      ,     __a           =			data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

		# finally, normalize the bounding boxes
		__a           =			[]
		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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				['pixel_values']

		def __init__(  self							, _a = True							, _a = None							, _a = PILImageResampling.BILINEAR							, _a = True							, _a = 1 / 255							, _a = True							, _a = None							, _a = None							, _a = True							, _a = None							, _a = ""							, **_a							, ):
				super().__init__(**_a      )
				__a           =			size if size is not None else {'''height''': 224, '''width''': 224}
				__a           =			get_size_dict(_a      )

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

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = PILImageResampling.BILINEAR							, _a = None							, **_a							, ):
				__a           =			get_size_dict(_a      )
				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           =			(size['''height'''], size['''width'''])
				return resize(_a							, size=_a							, resample=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = None							, **_a							, ):
				return rescale(_a							, scale=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a = None							, **_a							, ):
				return normalize(_a							, mean=_a							, std=_a							, data_format=_a							, **_a      )





		def 			__UpperCAmelCase       (  self							, _a							, _a = None							, _a = None							, _a=None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = ChannelDimension.FIRST							, **_a							, ):
				__a           =			do_resize if do_resize is not None else self.do_resize
				__a           =			size if size is not None else self.size
				__a           =			get_size_dict(_a      )
				__a           =			resample if resample is not None else self.resample
				__a           =			do_rescale if do_rescale is not None else self.do_rescale
				__a           =			rescale_factor if rescale_factor is not None else self.rescale_factor
				__a           =			do_normalize if do_normalize is not None else self.do_normalize
				__a           =			image_mean if image_mean is not None else self.image_mean
				__a           =			image_std if image_std is not None else self.image_std
				__a           =			apply_ocr if apply_ocr is not None else self.apply_ocr
				__a           =			ocr_lang if ocr_lang is not None else self.ocr_lang
				__a           =			tesseract_config if tesseract_config is not None else self.tesseract_config

				__a           =			make_list_of_images(_a      )

				if not valid_images(_a      ):
						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           =			[to_numpy_array(_a      ) for image in images]

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

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

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

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

				__a           =			[to_channel_dimension_format(_a							, _a      ) for image in images]

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

				if apply_ocr:
						__a           =			words_batch
						__a           =			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() and is_transformers_version(""">=""", """4.25.0""")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel






