from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset






class 		a__						(					A__					):

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return [
							    {"col_1": 3, "col_2": "a"},
							    {"col_1": 2, "col_2": "b"},
							    {"col_1": 1, "col_2": "c"},
							    {"col_1": 0, "col_2": "d"},
							]

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
							return Dataset.from_dict(_A    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._create_example_records()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    Dataset.from_list(_A    )
							self.assertListEqual(dset.column_names,["col_1", "col_2"]    )
							for i, r in enumerate(_A    ):
									self.assertDictEqual(_A,example_records[i]    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._create_example_records()
							SCREAMING_SNAKE_CASE_  :		Any				      =    Dataset.from_list(_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}    )
							self.assertEqual(dset.info,dset_from_dict.info    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):  # checks what happens with missing columns







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    [{"col_1": 1}, {"col_2": "x"}]
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    Dataset.from_list(_A    )
							self.assertDictEqual(dset[0],{"col_1": 1}    )
							self.assertDictEqual(dset[1],{"col_1": None}    )  # NB: first record is used for columns

					def 				__UpperCamelCase   (    self      :       Any    ):  # checks if the type can be inferred from the second record







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    [{"col_1": []}, {"col_1": [1, 2]}]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    Dataset.from_list(_A    )
							self.assertEqual(dset.info.features["col_1"],Sequence(Value("int64"    )    )    )






					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    Dataset.from_list([]    )
							self.assertEqual(len(_A    ),0    )
							self.assertListEqual(dset.column_names,[]    )


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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    to_pil_image(__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase       =    pil_image.size
	lowercase       =    pytesseract.image_to_data(__SCREAMING_SNAKE_CASE ,							lang=__SCREAMING_SNAKE_CASE ,							output_type='dict' ,							config=__SCREAMING_SNAKE_CASE							)
	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(__SCREAMING_SNAKE_CASE							) if not word.strip()]
	lowercase       =    [word for idx, word in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [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
	lowercase       =    []
	for x, y, w, h in zip(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
		lowercase       =    [x, y, x + w, y + h]
		actual_boxes.append(__SCREAMING_SNAKE_CASE							)

	# finally, normalize the bounding boxes
	lowercase       =    []
	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_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       ["""pixel_values"""]





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

		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 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case       )
		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(snake_case		,    size=snake_case		,    resample=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return rescale(snake_case		,    scale=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return normalize(snake_case		,    mean=snake_case		,    std=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = None		,    snake_case=None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = ChannelDimension.FIRST		,    **snake_case		,    ):
		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(snake_case       )
		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(snake_case       )

		if not valid_images(snake_case       ):
			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(snake_case       ) 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(snake_case		,    snake_case		,    snake_case       )
				words_batch.append(snake_case       )
				boxes_batch.append(snake_case       )

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

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

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

		lowercase       =    [to_channel_dimension_format(snake_case		,    snake_case       ) for image in images]

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

		if apply_ocr:
			lowercase       =    words_batch
			lowercase       =    boxes_batch
		return data

