
"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin





@flax.struct.dataclass
class 			snake_case  :
							# setable values
							UpperCAmelCase__      =   None
							UpperCAmelCase__      =   None
							UpperCAmelCase__      =   None  # sigma(t_i)

							@classmethod
							def _lowercase       (cls     ):

									"""simple docstring"""







									return cls()







@dataclass
class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   42
							UpperCAmelCase__      =   42
							UpperCAmelCase__      =   42







class 			snake_case  (				__lowercase							,      __lowercase ):

							@property
							def _lowercase       (self     ):

									"""simple docstring"""







									return True

							@register_to_config
							def __init__(self							,							SCREAMING_SNAKE_CASE_ = 0.02							,							SCREAMING_SNAKE_CASE_ = 1_00							,							SCREAMING_SNAKE_CASE_ = 1.0_07							,							SCREAMING_SNAKE_CASE_ = 80							,							SCREAMING_SNAKE_CASE_ = 0.05							,							SCREAMING_SNAKE_CASE_ = 50							,							):

									"""simple docstring"""







									pass

							def _lowercase       (self     ):

									"""simple docstring"""







									return KarrasVeSchedulerState.create()

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = ()     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			jnp.arange(0							,							SCREAMING_SNAKE_CASE_     )[::-1].copy()
									SCREAMING_SNAKE_CASE_    			=			[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in timesteps
									]

									return state.replace(
									    num_inference_steps=SCREAMING_SNAKE_CASE_							,							schedule=jnp.array(SCREAMING_SNAKE_CASE_							,							dtype=jnp.floataa     )							,							timesteps=SCREAMING_SNAKE_CASE_							,							)

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									if self.config.s_min <= sigma <= self.config.s_max:
											SCREAMING_SNAKE_CASE_    			=			min(self.config.s_churn / state.num_inference_steps							,							2**0.5 - 1     )
									else:
											SCREAMING_SNAKE_CASE_    			=			0

									# sample eps ~ N(0, S_noise^2 * I)
									SCREAMING_SNAKE_CASE_    			=			random.split(SCREAMING_SNAKE_CASE_							,							num=1     )
									SCREAMING_SNAKE_CASE_    			=			self.config.s_noise * random.normal(key=SCREAMING_SNAKE_CASE_							,							shape=sample.shape     )
									SCREAMING_SNAKE_CASE_    			=			sigma + gamma * sigma
									SCREAMING_SNAKE_CASE_    			=			sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = True							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			sample_hat + sigma_hat * model_output
									SCREAMING_SNAKE_CASE_    			=			(sample_hat - pred_original_sample) / sigma_hat
									SCREAMING_SNAKE_CASE_    			=			sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
											return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE_							,							derivative=SCREAMING_SNAKE_CASE_							,							state=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = True							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			sample_prev + sigma_prev * model_output
									SCREAMING_SNAKE_CASE_    			=			(sample_prev - pred_original_sample) / sigma_prev
									SCREAMING_SNAKE_CASE_    			=			sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
											return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE_							,							derivative=SCREAMING_SNAKE_CASE_							,							state=SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									raise NotImplementedError()

"""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__				=					logging.get_logger(__name__)



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		return [
		    int(1_000 * (box[0] / width)     ),
		    int(1_000 * (box[1] / height)     ),
		    int(1_000 * (box[2] / width)     ),
		    int(1_000 * (box[3] / height)     ),
		]



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

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

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

		# finally, normalize the bounding boxes
		SCREAMING_SNAKE_CASE_    			=			[]
		for box in actual_boxes:
				normalized_boxes.append(normalize_box(__a,				__a,				__a     )     )

		assert len(__a     ) == len(__a     ), "Not as many words as there are bounding boxes"

		return words, normalized_boxes





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   ['''pixel_values''']

							def __init__(self							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = 1 / 2_55							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = ""							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else {'''height''': 2_24, '''width''': 2_24}
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )

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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_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()}'     )
									SCREAMING_SNAKE_CASE_    			=			(size['''height'''], size['''width'''])
									return resize(SCREAMING_SNAKE_CASE_							,							size=SCREAMING_SNAKE_CASE_							,							resample=SCREAMING_SNAKE_CASE_							,							data_format=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									return rescale(SCREAMING_SNAKE_CASE_							,							scale=SCREAMING_SNAKE_CASE_							,							data_format=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									return normalize(SCREAMING_SNAKE_CASE_							,							mean=SCREAMING_SNAKE_CASE_							,							std=SCREAMING_SNAKE_CASE_							,							data_format=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else self.size
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_    			=			do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_    			=			rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_    			=			do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_    			=			image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_    			=			image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_    			=			apply_ocr if apply_ocr is not None else self.apply_ocr
									SCREAMING_SNAKE_CASE_    			=			ocr_lang if ocr_lang is not None else self.ocr_lang
									SCREAMING_SNAKE_CASE_    			=			tesseract_config if tesseract_config is not None else self.tesseract_config

									SCREAMING_SNAKE_CASE_    			=			make_list_of_images(SCREAMING_SNAKE_CASE_     )

									if not valid_images(SCREAMING_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.
									SCREAMING_SNAKE_CASE_    			=			[to_numpy_array(SCREAMING_SNAKE_CASE_     ) for image in images]

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

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

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

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

									SCREAMING_SNAKE_CASE_    			=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ) for image in images]

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

									if apply_ocr:
											SCREAMING_SNAKE_CASE_    			=			words_batch
											SCREAMING_SNAKE_CASE_    			=			boxes_batch
									return data