

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

A_ :				str						 =    logging.get_logger(__name__)

def 		UpperCamelCase      (lowercase_:				Dict					,   lowercase_:				Any					,   lowercase_:				Optional[Any]      )  ->   Optional[Any]:
	return [
	    int(1000 * (box[0] / width)      ),
	    int(1000 * (box[1] / height)      ),
	    int(1000 * (box[2] / width)      ),
	    int(1000 * (box[3] / height)      ),
	]

def 		UpperCamelCase      (lowercase_:				np.ndarray					,   lowercase_:				Optional[str]					,   lowercase_:				Optional[str]      )  ->   List[Any]:
	A__							:					Tuple       =	to_pil_image(lowercase_      )
	A__     ,     A__							:					int       =	pil_image.size
	A__							:					Dict       =	pytesseract.image_to_data(lowercase_					,   lang=lowercase_					,   output_type="""dict"""					,   config=lowercase_      )
	A__     ,     A__     ,     A__     ,     A__     ,     A__							:					Dict       =	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(lowercase_      ) if not word.strip()]
	A__							:					Union[str, Any]       =	[word for idx, word in enumerate(lowercase_      ) if idx not in irrelevant_indices]
	A__							:					Union[str, Any]       =	[coord for idx, coord in enumerate(lowercase_      ) if idx not in irrelevant_indices]
	A__							:					Dict       =	[coord for idx, coord in enumerate(lowercase_      ) if idx not in irrelevant_indices]
	A__							:					Any       =	[coord for idx, coord in enumerate(lowercase_      ) if idx not in irrelevant_indices]
	A__							:					List[str]       =	[coord for idx, coord in enumerate(lowercase_      ) 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(lowercase_					,   lowercase_					,   lowercase_					,   lowercase_      ):
		A__							:					Optional[Any]       =	[x, y, x + w, y + h]
		actual_boxes.append(lowercase_      )

	# finally, normalize the bounding boxes
	A__							:					Union[str, Any]       =	[]
	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 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[Any]													= ['''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__							:					Union[str, Any]       =	size if size is not None else {"""height""": 224, """width""": 224}
						A__							:					Tuple       =	get_size_dict(A__    )

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






					def 						__A    (				self					, A__					, A__					, A__ = PILImageResampling.BILINEAR					, A__ = None					, **A__					, ):
						A__							:					Dict       =	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__							:					Tuple       =	(size["""height"""], size["""width"""])
						return resize(A__					, size=A__					, resample=A__					, data_format=A__					, **A__    )






					def 						__A    (				self					, A__					, A__					, A__ = None					, **A__					, ):
						return rescale(A__					, scale=A__					, data_format=A__					, **A__    )






					def 						__A    (				self					, A__					, A__					, A__					, A__ = None					, **A__					, ):
						return normalize(A__					, mean=A__					, std=A__					, data_format=A__					, **A__    )






					def 						__A    (				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__							:					Tuple       =	do_resize if do_resize is not None else self.do_resize
						A__							:					Any       =	size if size is not None else self.size
						A__							:					Tuple       =	get_size_dict(A__    )
						A__							:					str       =	resample if resample is not None else self.resample
						A__							:					Union[str, Any]       =	do_rescale if do_rescale is not None else self.do_rescale
						A__							:					Optional[int]       =	rescale_factor if rescale_factor is not None else self.rescale_factor
						A__							:					int       =	do_normalize if do_normalize is not None else self.do_normalize
						A__							:					str       =	image_mean if image_mean is not None else self.image_mean
						A__							:					List[Any]       =	image_std if image_std is not None else self.image_std
						A__							:					Tuple       =	apply_ocr if apply_ocr is not None else self.apply_ocr
						A__							:					List[str]       =	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__							:					Optional[int]       =	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__							:					List[Any]       =	[to_numpy_array(A__    ) for image in images]

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

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

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

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

						A__							:					str       =	[to_channel_dimension_format(A__					, A__    ) for image in images]

						A__							:					Optional[int]       =	BatchFeature(data={"""pixel_values""": images}					, tensor_type=A__    )

						if apply_ocr:
							A__							:					str       =	words_batch
							A__							:					Any       =	boxes_batch
						return data






import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


A_ :				Union[str, Any]						 =    logging.get_logger(__name__)



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def __init__(				self					, *A__					, **A__    ):
						warnings.warn(
						    """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
						    """ Please use PoolFormerImageProcessor instead."""					, A__					, )
						super().__init__(*A__					, **A__    )



