



'''simple docstring'''

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[Any]:
		return [
		    int(1000 * (box[0] / width)  ),
		    int(1000 * (box[1] / height)  ),
		    int(1000 * (box[2] / width)  ),
		    int(1000 * (box[3] / height)  ),
		]



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = None  )      ->				Union[str, Any]:
		_a					:				Any				=							tesseract_config if tesseract_config is not None else ''

		# apply OCR
		_a					:				List[str]				=							to_pil_image(lowerCAmelCase_  )
		_a					:				List[Any]				=							pil_image.size
		_a					:				Any				=							pytesseract.image_to_data(lowerCAmelCase_						,      lang=lowerCAmelCase_						,      output_type='dict'						,      config=lowerCAmelCase_  )
		_a					:				str				=							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					:				Tuple				=							[word for idx, word in enumerate(lowerCAmelCase_  ) if idx not in irrelevant_indices]
		_a					:				List[str]				=							[coord for idx, coord in enumerate(lowerCAmelCase_  ) if idx not in irrelevant_indices]
		_a					:				Tuple				=							[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					:				Tuple				=							[coord for idx, coord in enumerate(lowerCAmelCase_  ) if idx not in irrelevant_indices]

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

		# finally, normalize the bounding boxes
		_a					:				int				=							[]
		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      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   List[Any]           = ['pixel_values']







							def __init__(							self :	List[str]     ,_UpperCAmelCase :	bool = True     ,_UpperCAmelCase :	Dict[str, int] = None     ,_UpperCAmelCase :	PILImageResampling = PILImageResampling.BILINEAR     ,_UpperCAmelCase :	bool = True     ,_UpperCAmelCase :	Optional[str] = None     ,_UpperCAmelCase :	Optional[str] = ""     ,**_UpperCAmelCase :	Dict     ,):
									super().__init__(**_UpperCAmelCase		)
									_a					:				Dict				=							size if size is not None else {'height': 224, 'width': 224}
									_a					:				Union[str, Any]				=							get_size_dict(_UpperCAmelCase		)

									_a					:				List[Any]				=							do_resize
									_a					:				Dict				=							size
									_a					:				str				=							resample
									_a					:				Dict				=							apply_ocr
									_a					:				Tuple				=							ocr_lang
									_a					:				Optional[int]				=							tesseract_config







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	np.ndarray     ,_UpperCAmelCase :	Dict[str, int]     ,_UpperCAmelCase :	PILImageResampling = PILImageResampling.BILINEAR     ,_UpperCAmelCase :	Optional[Union[str, ChannelDimension]] = None     ,**_UpperCAmelCase :	List[str]     ,):
									_a					:				Optional[Any]				=							get_size_dict(_UpperCAmelCase		)
									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					:				List[Any]				=							(size['height'], size['width'])
									return resize(_UpperCAmelCase     ,size=_UpperCAmelCase     ,resample=_UpperCAmelCase     ,data_format=_UpperCAmelCase     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	ImageInput     ,_UpperCAmelCase :	bool = None     ,_UpperCAmelCase :	Dict[str, int] = None     ,_UpperCAmelCase :	PILImageResampling = None     ,_UpperCAmelCase :	bool = None     ,_UpperCAmelCase :	Optional[str] = None     ,_UpperCAmelCase :	Optional[str] = None     ,_UpperCAmelCase :	Optional[Union[str, TensorType]] = None     ,_UpperCAmelCase :	ChannelDimension = ChannelDimension.FIRST     ,**_UpperCAmelCase :	Dict     ,):
									_a					:				Any				=							do_resize if do_resize is not None else self.do_resize
									_a					:				Optional[Any]				=							size if size is not None else self.size
									_a					:				List[Any]				=							get_size_dict(_UpperCAmelCase		)
									_a					:				Optional[Any]				=							resample if resample is not None else self.resample
									_a					:				Optional[Any]				=							apply_ocr if apply_ocr is not None else self.apply_ocr
									_a					:				List[Any]				=							ocr_lang if ocr_lang is not None else self.ocr_lang
									_a					:				List[str]				=							tesseract_config if tesseract_config is not None else self.tesseract_config

									_a					:				str				=							make_list_of_images(_UpperCAmelCase		)

									if not valid_images(_UpperCAmelCase		):
											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.'		)

									# All transformations expect numpy arrays.
									_a					:				Any				=							[to_numpy_array(_UpperCAmelCase		) for image in images]

									if apply_ocr:
											requires_backends(self     ,'pytesseract'		)
											_a					:				Tuple				=							[]
											_a					:				str				=							[]
											for image in images:
													_a					:				Dict				=							apply_tesseract(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)
													words_batch.append(_UpperCAmelCase		)
													boxes_batch.append(_UpperCAmelCase		)

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

									# flip color channels from RGB to BGR (as Detectron2 requires this)
									_a					:				Union[str, Any]				=							[flip_channel_order(_UpperCAmelCase		) for image in images]
									_a					:				int				=							[to_channel_dimension_format(_UpperCAmelCase     ,_UpperCAmelCase		) for image in images]

									_a					:				Union[str, Any]				=							BatchFeature(data={'pixel_values': images}     ,tensor_type=_UpperCAmelCase		)

									if apply_ocr:
											_a					:				Any				=							words_batch
											_a					:				Tuple				=							boxes_batch
									return data





'''simple docstring'''

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer

@require_tf
class      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[Any]		):
									self.assertEqual(len(_UpperCAmelCase		)     ,len(_UpperCAmelCase		)		)
									for a, b in zip(_UpperCAmelCase     ,_UpperCAmelCase		):
											self.assertAlmostEqual(_UpperCAmelCase     ,_UpperCAmelCase     ,delta=_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				int				=							GradientAccumulator()
									accumulator([tf.constant([1.0, 2.0]		)]		)
									accumulator([tf.constant([-2.0, 1.0]		)]		)
									accumulator([tf.constant([-1.0, 2.0]		)]		)
									with self.assertRaises(_UpperCAmelCase		):
											accumulator([tf.constant([1.0, 1.0]		), tf.constant([2.0, 2.0]		)]		)
									self.assertEqual(accumulator.step     ,3		)
									self.assertEqual(len(accumulator.gradients		)     ,1		)
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()     ,[-2.0, 5.0]     ,tol=1E-2		)
									accumulator.reset()
									self.assertEqual(accumulator.step     ,0		)
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()     ,[0.0, 0.0]     ,tol=1E-2		)







							def 							__lowercase      (							self :	Any		):
									_a					:				int				=							None
									ops.enable_eager_execution_internal()
									_a					:				Optional[int]				=							tf.config.list_physical_devices('CPU'		)
									if len(_UpperCAmelCase		) == 1:
											tf.config.set_logical_device_configuration(
											    physical_devices[0]     ,[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]		)
									_a					:				Tuple				=							tf.config.list_logical_devices(device_type='CPU'		)
									_a					:				List[str]				=							tf.distribute.MirroredStrategy(devices=devices[:2]		)

									with strategy.scope():
											_a					:				Tuple				=							GradientAccumulator()
											_a					:				List[Any]				=							tf.Variable([4.0, 3.0]		)
											_a	, _a					:				Dict				=							create_optimizer(5E-5     ,10     ,5		)
											_a					:				Tuple				=							tf.Variable([0.0, 0.0]     ,trainable=_UpperCAmelCase		)

									def accumulate_on_replica(_UpperCAmelCase :	str		):
											accumulator([gradient]		)

									def apply_on_replica():
											optimizer.apply_gradients(list(zip(accumulator.gradients     ,[variable]		)		)		)

									@tf.function
									def accumulate(_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[int]		):
											with strategy.scope():
													_a					:				Union[str, Any]				=							strategy.experimental_local_results(_UpperCAmelCase		)
													local_variables[0].assign(_UpperCAmelCase		)
													local_variables[1].assign(_UpperCAmelCase		)
													strategy.run(_UpperCAmelCase     ,args=(gradient_placeholder,)		)

									@tf.function
									def apply_grad():
											with strategy.scope():
													strategy.run(_UpperCAmelCase		)

									def _check_local_values(_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	List[str]		):
											_a					:				List[Any]				=							strategy.experimental_local_results(accumulator._gradients[0]		)
											self.assertListAlmostEqual(values[0].value()     ,_UpperCAmelCase     ,tol=1E-2		)
											self.assertListAlmostEqual(values[1].value()     ,_UpperCAmelCase     ,tol=1E-2		)

									accumulate([1.0, 2.0]     ,[-1.0, 1.0]		)
									accumulate([3.0, -1.0]     ,[-1.0, -1.0]		)
									accumulate([-2.0, 2.0]     ,[3.0, -2.0]		)
									self.assertEqual(accumulator.step     ,3		)
									_check_local_values([2.0, 3.0]     ,[1.0, -2.0]		)
									apply_grad()
									self.assertListAlmostEqual(variable.value()     ,[4.0, 3.0]     ,tol=1E-2		)
									accumulator.reset()
									self.assertEqual(accumulator.step     ,0		)
									_check_local_values([0.0, 0.0]     ,[0.0, 0.0]		)




