



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

SCREAMING_SNAKE_CASE_:str             =						logging.get_logger(__name__)




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Any:



    """simple docstring"""






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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = None		)       ->  int:



    """simple docstring"""






    A :       str          =   tesseract_config if tesseract_config is not None else """"""

    # apply OCR
    A :       List[str]          =   to_pil_image(_lowerCAmelCase		)
    A     ,							A :       Optional[int]          =   pil_image.size
    A :       str          =   pytesseract.image_to_data(_lowerCAmelCase      ,     lang=_lowerCAmelCase      ,     output_type="""dict"""      ,     config=_lowerCAmelCase		)
    A     ,							A     ,							A     ,							A     ,							A :       Dict          =   data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

    # filter empty words and corresponding coordinates
    A :       List[str]          =   [idx for idx, word in enumerate(_lowerCAmelCase		) if not word.strip()]
    A :       Union[str, Any]          =   [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 :       Optional[int]          =   [coord for idx, coord in enumerate(_lowerCAmelCase		) if idx not in irrelevant_indices]
    A :       Optional[Any]          =   [coord for idx, coord in enumerate(_lowerCAmelCase		) if idx not in irrelevant_indices]
    A :       str          =   [coord for idx, coord in enumerate(_lowerCAmelCase		) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    A :       List[Any]          =   []
    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 :       Union[str, Any]          =   []
    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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      ["pixel_values"]






    def __init__(       self,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = PILImageResampling.BILINEAR,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = "",		**lowerCamelCase__,		):
        super().__init__(**lowerCamelCase__		)
        A :       Dict          =   size if size is not None else {"""height""": 224, """width""": 224}
        A :       List[Any]          =   get_size_dict(lowerCamelCase__		)

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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = PILImageResampling.BILINEAR,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Optional[Any]          =   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()}'''		)
        A :       Tuple          =   (size["""height"""], size["""width"""])
        return resize(lowerCamelCase__,		size=lowerCamelCase__,		resample=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = ChannelDimension.FIRST,		**lowerCamelCase__,		):
        A :       Any          =   do_resize if do_resize is not None else self.do_resize
        A :       List[Any]          =   size if size is not None else self.size
        A :       List[Any]          =   get_size_dict(lowerCamelCase__		)
        A :       Dict          =   resample if resample is not None else self.resample
        A :       Dict          =   apply_ocr if apply_ocr is not None else self.apply_ocr
        A :       Dict          =   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 :       List[str]          =   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."""		)

        # All transformations expect numpy arrays.
        A :       Dict          =   [to_numpy_array(lowerCamelCase__		) for image in images]

        if apply_ocr:
            requires_backends(self,		"""pytesseract"""		)
            A :       int          =   []
            A :       Dict          =   []
            for image in images:
                A     ,							A :       Union[str, Any]          =   apply_tesseract(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
                words_batch.append(lowerCamelCase__		)
                boxes_batch.append(lowerCamelCase__		)

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

        # flip color channels from RGB to BGR (as Detectron2 requires this)
        A :       Optional[Any]          =   [flip_channel_order(lowerCamelCase__		) for image in images]
        A :       Optional[int]          =   [to_channel_dimension_format(lowerCamelCase__,		lowerCamelCase__		) for image in images]

        A :       Any          =   BatchFeature(data={"""pixel_values""": images},		tensor_type=lowerCamelCase__		)

        if apply_ocr:
            A :       List[Any]          =   words_batch
            A :       int          =   boxes_batch
        return data







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 SCREAMING_SNAKE_CASE__     (		tf.keras.optimizers.schedules.LearningRateSchedule    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = 1.0,		lowerCamelCase__ = None,		):
        super().__init__()
        A :       Union[str, Any]          =   initial_learning_rate
        A :       List[Any]          =   warmup_steps
        A :       int          =   power
        A :       Optional[int]          =   decay_schedule_fn
        A :       int          =   name






    def __call__(       self,		lowerCamelCase__		):
        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`.
            A :       str          =   tf.cast(lowerCamelCase__,		tf.floataa		)
            A :       List[Any]          =   tf.cast(self.warmup_steps,		tf.floataa		)
            A :       Dict          =   global_step_float / warmup_steps_float
            A :       Union[str, Any]          =   self.initial_learning_rate * tf.math.pow(lowerCamelCase__,		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=lowerCamelCase__,		)






    def 			_lowerCAmelCase					(       self		):
        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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 0.9      ,     _lowerCAmelCase = 0.999      ,     _lowerCAmelCase = 1e-8      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 1.0      ,     _lowerCAmelCase = None      ,     )       ->  Union[str, Any]:



    """simple docstring"""






    A :       Optional[int]          =   tf.keras.optimizers.schedules.PolynomialDecay(
        initial_learning_rate=_lowerCAmelCase      ,     decay_steps=num_train_steps - num_warmup_steps      ,     end_learning_rate=init_lr * min_lr_ratio      ,     power=_lowerCAmelCase      ,     )
    if num_warmup_steps:
        A :       Dict          =   WarmUp(
            initial_learning_rate=_lowerCAmelCase      ,     decay_schedule_fn=_lowerCAmelCase      ,     warmup_steps=_lowerCAmelCase      ,     )
    if weight_decay_rate > 0.0:
        A :       str          =   AdamWeightDecay(
            learning_rate=_lowerCAmelCase      ,     weight_decay_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]      ,     include_in_weight_decay=_lowerCAmelCase      ,     )
    else:
        A :       Optional[int]          =   tf.keras.optimizers.Adam(
            learning_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     )
    # 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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 0.001,		lowerCamelCase__ = 0.9,		lowerCamelCase__ = 0.999,		lowerCamelCase__ = 1e-7,		lowerCamelCase__ = False,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = "AdamWeightDecay",		**lowerCamelCase__,		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)
        A :       int          =   weight_decay_rate
        A :       Any          =   include_in_weight_decay
        A :       Dict          =   exclude_from_weight_decay






    @classmethod
    def 			_lowerCAmelCase					(       cls,		lowerCamelCase__		):
        A :       Tuple          =   {"""WarmUp""": WarmUp}
        return super(lowerCamelCase__,		cls		).from_config(lowerCamelCase__,		custom_objects=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        super(lowerCamelCase__,		self		)._prepare_local(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        A :       List[str]          =   tf.constant(
            self.weight_decay_rate,		name="""adam_weight_decay_rate"""		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   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 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None,		**lowerCamelCase__		):
        A     ,							A :       Dict          =   list(zip(*lowerCamelCase__		)		)
        return super(lowerCamelCase__,		self		).apply_gradients(zip(lowerCamelCase__,		lowerCamelCase__		),		name=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

        A :       Union[str, Any]          =   apply_state or {}
        A :       Optional[int]          =   apply_state.get((var_device, var_dtype)		)
        if coefficients is None:
            A :       Dict          =   self._fallback_apply_state(lowerCamelCase__,		lowerCamelCase__		)
            A :       List[Any]          =   coefficients

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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       str          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Any          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_dense(lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       Tuple          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Optional[Any]          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_sparse(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   super().get_config()
        config.update({"""weight_decay_rate""": self.weight_decay_rate}		)
        return config






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        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(lowerCamelCase__,		lowerCamelCase__		) is not None:
                    return True

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






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self		):
        A :       List[str]          =   []
        A :       List[str]          =   None






    @property
    def 			_lowerCAmelCase					(       self		):
        if self._accum_steps is None:
            A :       str          =   tf.Variable(
                tf.constant(0,		dtype=tf.intaa		),		trainable=lowerCamelCase__,		synchronization=tf.VariableSynchronization.ON_READ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,		)

        return self._accum_steps.value()






    @property
    def 			_lowerCAmelCase					(       self		):
        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,		lowerCamelCase__		):
        if not self._gradients:
            A :       int          =   self.step  # Create the step variable.
            self._gradients.extend(
                [
                    tf.Variable(
                        tf.zeros_like(lowerCamelCase__		),		trainable=lowerCamelCase__,		synchronization=tf.VariableSynchronization.ON_READ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,		)
                    if gradient is not None
                    else gradient
                    for gradient in gradients
                ]		)
        if len(lowerCamelCase__		) != len(self._gradients		):
            raise ValueError(f'''Expected {len(self._gradients		)} gradients, but got {len(lowerCamelCase__		)}'''		)

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

        self._accum_steps.assign_add(1		)






    def 			_lowerCAmelCase					(       self		):
        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(lowerCamelCase__		)		)


