"""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

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



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				return [
				    int(1_0_0_0 * (box[0] / width)       ),
				    int(1_0_0_0 * (box[1] / height)       ),
				    int(1_0_0_0 * (box[2] / width)       ),
				    int(1_0_0_0 * (box[3] / height)       ),
				]



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							to_pil_image(lowerCamelCase       )
				UpperCAmelCase__      ,	UpperCAmelCase__             =							pil_image.size
				UpperCAmelCase__             =							pytesseract.image_to_data(lowerCamelCase					,			lang=lowerCamelCase					,			output_type='dict'					,			config=lowerCamelCase       )
				UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							data['text'], data['left'], data['top'], data['width'], data['height']

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

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

				# finally, normalize the bounding boxes
				UpperCAmelCase__             =							[]
				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 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = ["pixel_values"]







					def __init__(				self   :	int    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BILINEAR    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	float = 1 / 255    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Union[float, Iterable[float]] = None    ,lowerCamelCase__   :	Union[float, Iterable[float]] = None    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[str] = None    ,lowerCamelCase__   :	Optional[str] = ""    ,**lowerCamelCase__   :	Any    ,):
									super().__init__(**lowerCamelCase__			)
									UpperCAmelCase__             =							size if size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__			)

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







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







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Union[int, float]    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	Dict    ,):
									return rescale(lowerCamelCase__    ,scale=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Union[float, Iterable[float]]    ,lowerCamelCase__   :	Union[float, Iterable[float]]    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	Optional[int]    ,):
									return normalize(lowerCamelCase__    ,mean=lowerCamelCase__    ,std=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	ImageInput    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	Union[str, Any]=None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	float = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Union[float, Iterable[float]] = None    ,lowerCamelCase__   :	Union[float, Iterable[float]] = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Optional[str] = None    ,lowerCamelCase__   :	Optional[str] = None    ,lowerCamelCase__   :	Optional[Union[str, TensorType]] = None    ,lowerCamelCase__   :	ChannelDimension = ChannelDimension.FIRST    ,**lowerCamelCase__   :	str    ,):
									UpperCAmelCase__             =							do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__             =							size if size is not None else self.size
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__			)
									UpperCAmelCase__             =							resample if resample is not None else self.resample
									UpperCAmelCase__             =							do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__             =							rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__             =							do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__             =							image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__             =							image_std if image_std is not None else self.image_std
									UpperCAmelCase__             =							apply_ocr if apply_ocr is not None else self.apply_ocr
									UpperCAmelCase__             =							ocr_lang if ocr_lang is not None else self.ocr_lang
									UpperCAmelCase__             =							tesseract_config if tesseract_config is not None else self.tesseract_config

									UpperCAmelCase__             =							make_list_of_images(lowerCamelCase__			)

									if not valid_images(lowerCamelCase__			):
													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.
									UpperCAmelCase__             =							[to_numpy_array(lowerCamelCase__			) for image in images]

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

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

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

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

									UpperCAmelCase__             =							[to_channel_dimension_format(lowerCamelCase__    ,lowerCamelCase__			) for image in images]

									UpperCAmelCase__             =							BatchFeature(data={'pixel_values': images}    ,tensor_type=lowerCamelCase__			)

									if apply_ocr:
													UpperCAmelCase__             =							words_batch
													UpperCAmelCase__             =							boxes_batch
									return data









'''simple docstring'''

import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
							from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
							from tensorflow.keras.optimizers import Adam






class 						a				(   tf.keras.optimizers.schedules.LearningRateSchedule     ):


				def __init__(		self    :					Optional[Any]       ,				lowercase_    :					float       ,				lowercase_    :					Callable       ,				lowercase_    :					int       ,				lowercase_    :					float = 1.0       ,				lowercase_    :					str = None       ,				):
									super().__init__()
									snake_case_									=				initial_learning_rate
									snake_case_									=				warmup_steps
									snake_case_									=				power
									snake_case_									=				decay_schedule_fn
									snake_case_									=				name


				def __call__(		self    :					Tuple       ,				lowercase_    :					str					):
									with tf.name_scope(self.name or '''WarmUp'''					) as name:
														# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
														# learning rate will be `global_step/num_warmup_steps * init_lr`.
														snake_case_									=				tf.cast(lowercase_       ,				tf.floataa					)
														snake_case_									=				tf.cast(self.warmup_steps       ,				tf.floataa					)
														snake_case_									=				global_step_float / warmup_steps_float
														snake_case_									=				self.initial_learning_rate * tf.math.pow(lowercase_       ,				self.power					)
														return tf.cond(
														    global_step_float < warmup_steps_float       ,				lambda: warmup_learning_rate       ,				lambda: self.decay_schedule_fn(step - self.warmup_steps					)       ,				name=lowercase_       ,				)


				def   A_ (		self    :					Any					):
									return {
									    "initial_learning_rate": self.initial_learning_rate,
									    "decay_schedule_fn": self.decay_schedule_fn,
									    "warmup_steps": self.warmup_steps,
									    "power": self.power,
									    "name": self.name,
									}







def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = 0.0,      __UpperCAmelCase = 0.9,      __UpperCAmelCase = 0.9_9_9,      __UpperCAmelCase = 1e-8,      __UpperCAmelCase = None,      __UpperCAmelCase = None,      __UpperCAmelCase = 0.0,      __UpperCAmelCase = 1.0,      __UpperCAmelCase = None,      )		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tf.keras.optimizers.schedules.PolynomialDecay(
					    initial_learning_rate=__UpperCAmelCase,      decay_steps=num_train_steps - num_warmup_steps,      end_learning_rate=init_lr * min_lr_ratio,      power=__UpperCAmelCase,      )
					if num_warmup_steps:
										snake_case_									=				WarmUp(
										    initial_learning_rate=__UpperCAmelCase,      decay_schedule_fn=__UpperCAmelCase,      warmup_steps=__UpperCAmelCase,      )
					if weight_decay_rate > 0.0:
										snake_case_									=				AdamWeightDecay(
										    learning_rate=__UpperCAmelCase,      weight_decay_rate=__UpperCAmelCase,      beta_a=__UpperCAmelCase,      beta_a=__UpperCAmelCase,      epsilon=__UpperCAmelCase,      clipnorm=__UpperCAmelCase,      global_clipnorm=__UpperCAmelCase,      exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''],      include_in_weight_decay=__UpperCAmelCase,      )
					else:
										snake_case_									=				tf.keras.optimizers.Adam(
										    learning_rate=__UpperCAmelCase,      beta_a=__UpperCAmelCase,      beta_a=__UpperCAmelCase,      epsilon=__UpperCAmelCase,      clipnorm=__UpperCAmelCase,      global_clipnorm=__UpperCAmelCase,      )
					# We return the optimizer and the LR scheduler in order to better track the
					# evolution of the LR independently of the optimizer.
					return optimizer, lr_schedule






class 						a				(   _lowerCamelCase     ):


				def __init__(		self    :					Dict       ,				lowercase_    :					Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001       ,				lowercase_    :					float = 0.9       ,				lowercase_    :					float = 0.999       ,				lowercase_    :					float = 1e-7       ,				lowercase_    :					bool = False       ,				lowercase_    :					float = 0.0       ,				lowercase_    :					Optional[List[str]] = None       ,				lowercase_    :					Optional[List[str]] = None       ,				lowercase_    :					str = "AdamWeightDecay"       ,				**lowercase_    :					Optional[int]       ,				):
									super().__init__(lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					)
									snake_case_									=				weight_decay_rate
									snake_case_									=				include_in_weight_decay
									snake_case_									=				exclude_from_weight_decay


				@classmethod
				def   A_ (		cls    :					Dict       ,				lowercase_    :					Union[str, Any]					):
									snake_case_									=				{'''WarmUp''': WarmUp}
									return super(lowercase_       ,				cls					).from_config(lowercase_       ,				custom_objects=lowercase_					)


				def   A_ (		self    :					str       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]       ,				lowercase_    :					Optional[int]					):
									super(lowercase_       ,				self					)._prepare_local(lowercase_       ,				lowercase_       ,				lowercase_					)
									snake_case_									=				tf.constant(
									    self.weight_decay_rate       ,				name='''adam_weight_decay_rate'''					)


				def   A_ (		self    :					str       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Dict       ,				lowercase_    :					Any					):
									snake_case_									=				self._do_use_weight_decay(var.name					)
									if do_decay:
														return var.assign_sub(
														    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']       ,				use_locking=self._use_locking       ,				)
									return tf.no_op()


				def   A_ (		self    :					Union[str, Any]       ,				lowercase_    :					int       ,				lowercase_    :					str=None       ,				**lowercase_    :					List[str]					):
									snake_case_			,snake_case_									=				list(zip(*lowercase_					)					)
									return super(lowercase_       ,				self					).apply_gradients(zip(lowercase_       ,				lowercase_					)       ,				name=lowercase_       ,				**lowercase_					)


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					str       ,				lowercase_    :					str       ,				lowercase_    :					Any					):
									if apply_state is None:
														return self._decayed_lr_t[var_dtype], {}

									snake_case_									=				apply_state or {}
									snake_case_									=				apply_state.get((var_device, var_dtype)					)
									if coefficients is None:
														snake_case_									=				self._fallback_apply_state(lowercase_       ,				lowercase_					)
														snake_case_									=				coefficients

									return coefficients["lr_t"], {"apply_state": apply_state}


				def   A_ (		self    :					Union[str, Any]       ,				lowercase_    :					int       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Optional[int]=None					):
									snake_case_			,snake_case_									=				self._get_lr(var.device       ,				var.dtype.base_dtype       ,				lowercase_					)
									snake_case_									=				self._decay_weights_op(lowercase_       ,				lowercase_       ,				lowercase_					)
									with tf.control_dependencies([decay]					):
														return super(lowercase_       ,				self					)._resource_apply_dense(lowercase_       ,				lowercase_       ,				**lowercase_					)


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					int       ,				lowercase_    :					str       ,				lowercase_    :					List[Any]=None					):
									snake_case_			,snake_case_									=				self._get_lr(var.device       ,				var.dtype.base_dtype       ,				lowercase_					)
									snake_case_									=				self._decay_weights_op(lowercase_       ,				lowercase_       ,				lowercase_					)
									with tf.control_dependencies([decay]					):
														return super(lowercase_       ,				self					)._resource_apply_sparse(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					)


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				super().get_config()
									config.update({'''weight_decay_rate''': self.weight_decay_rate}					)
									return config


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					int					):
									if self.weight_decay_rate == 0:
														return False

									if self._include_in_weight_decay:
														for r in self._include_in_weight_decay:
																			if re.search(lowercase_       ,				lowercase_					) is not None:
																								return True

									if self._exclude_from_weight_decay:
														for r in self._exclude_from_weight_decay:
																			if re.search(lowercase_       ,				lowercase_					) is not None:
																								return False
									return True







class 						a				(   _lowerCamelCase     ):


				def __init__(		self    :					List[Any]					):
									snake_case_									=				[]
									snake_case_									=				None


				@property
				def   A_ (		self    :					Union[str, Any]					):
									if self._accum_steps is None:
														snake_case_									=				tf.Variable(
														    tf.constant(0       ,				dtype=tf.intaa					)       ,				trainable=lowercase_       ,				synchronization=tf.VariableSynchronization.ON_READ       ,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,				)

									return self._accum_steps.value()


				@property
				def   A_ (		self    :					Dict					):
									if not self._gradients:
														raise ValueError('''The accumulator should be called first to initialize the gradients'''					)
									return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]


				def __call__(		self    :					Any       ,				lowercase_    :					int					):
									if not self._gradients:
														snake_case_									=				self.step  # Create the step variable.
														self._gradients.extend(
														    [
														        tf.Variable(
														            tf.zeros_like(lowercase_					)       ,				trainable=lowercase_       ,				synchronization=tf.VariableSynchronization.ON_READ       ,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,				)
														        if gradient is not None
														        else gradient
														        for gradient in gradients
														    ]					)
									if len(lowercase_					) != len(self._gradients					):
														raise ValueError(F"Expected {len(self._gradients					)} gradients, but got {len(lowercase_					)}"					)

									for accum_gradient, gradient in zip(self._gradients       ,				lowercase_					):
														if accum_gradient is not None and gradient is not None:
																			accum_gradient.assign_add(lowercase_					)

									self._accum_steps.assign_add(1					)


				def   A_ (		self    :					Optional[int]					):
									if not self._gradients:
														return
									self._accum_steps.assign(0					)
									for gradient in self._gradients:
														if gradient is not None:
																			gradient.assign(tf.zeros_like(lowercase_					)					)

