





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

_a     :    Dict         =						logging.get_logger(__name__)
def 							a_	(    __magic_name__		,     __magic_name__		,     __magic_name__   )				->			List[str]:




				"""simple docstring"""






				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 							a_	(    __magic_name__		,     __magic_name__		,     __magic_name__   )				->			Union[str, Any]:




				"""simple docstring"""






				snake_case   :					List[str]    						=				to_pil_image(__A   )
				snake_case						,						snake_case   :					str    						=				pil_image.size
				snake_case   :					Tuple    						=				pytesseract.image_to_data(__A		,     lang=__A		,     output_type='''dict'''		,     config=__A   )
				snake_case						,						snake_case						,						snake_case						,						snake_case						,						snake_case   :					Tuple    						=				data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

				# turn coordinates into (left, top, left+width, top+height) format
				snake_case   :					Union[str, Any]    						=				[]
				for x, y, w, h in zip(__A		,     __A		,     __A		,     __A   ):
								snake_case   :					Union[str, Any]    						=				[x, y, x + w, y + h]
								actual_boxes.append(__A   )

				# finally, normalize the bounding boxes
				snake_case   :					Union[str, Any]    						=				[]
				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   a_ (			snake_case__      ):
			A__      :			int       	=  ['pixel_values']

			def __init__(   self   :  List[Any]     , UpperCAmelCase__   :  Tuple = True     , UpperCAmelCase__   :  Union[str, Any] = None     , UpperCAmelCase__   :  Optional[int] = PILImageResampling.BILINEAR     , UpperCAmelCase__   :  Any = True     , UpperCAmelCase__   :  List[str] = 1 / 255     , UpperCAmelCase__   :  Optional[Any] = True     , UpperCAmelCase__   :  Any = None     , UpperCAmelCase__   :  Any = None     , UpperCAmelCase__   :  str = True     , UpperCAmelCase__   :  int = None     , UpperCAmelCase__   :  Dict = ""     , **UpperCAmelCase__   :  List[Any]     , ):






							"""simple docstring"""
							super().__init__(**_UpperCamelCase						)
							snake_case   :					Optional[Any]    						=				size if size is not None else {'''height''': 224, '''width''': 224}
							snake_case   :					Tuple    						=				get_size_dict(_UpperCamelCase						)

							snake_case   :					Union[str, Any]    						=				do_resize
							snake_case   :					Any    						=				size
							snake_case   :					Dict    						=				resample
							snake_case   :					List[str]    						=				do_rescale
							snake_case   :					int    						=				rescale_value
							snake_case   :					Union[str, Any]    						=				do_normalize
							snake_case   :					Tuple    						=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							snake_case   :					Tuple    						=				image_std if image_std is not None else IMAGENET_STANDARD_STD
							snake_case   :					Optional[Any]    						=				apply_ocr
							snake_case   :					str    						=				ocr_lang
							snake_case   :					Union[str, Any]    						=				tesseract_config

			def      lowerCAmelCase(   self   :  Tuple     , UpperCAmelCase__   :  str     , UpperCAmelCase__   :  List[Any]     , UpperCAmelCase__   :  Union[str, Any] = PILImageResampling.BILINEAR     , UpperCAmelCase__   :  Dict = None     , **UpperCAmelCase__   :  int     , ):






							"""simple docstring"""
							snake_case   :					int    						=				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()}"						)
							snake_case   :					str    						=				(size['''height'''], size['''width'''])
							return resize(_UpperCamelCase     , size=_UpperCamelCase     , resample=_UpperCamelCase     , data_format=_UpperCamelCase     , **_UpperCamelCase						)

			def      lowerCAmelCase(   self   :  List[str]     , UpperCAmelCase__   :  str     , UpperCAmelCase__   :  Dict     , UpperCAmelCase__   :  Optional[Any] = None     , **UpperCAmelCase__   :  Optional[int]     , ):






							"""simple docstring"""
							return rescale(_UpperCamelCase     , scale=_UpperCamelCase     , data_format=_UpperCamelCase     , **_UpperCamelCase						)

			def      lowerCAmelCase(   self   :  Union[str, Any]     , UpperCAmelCase__   :  Tuple     , UpperCAmelCase__   :  Optional[int]     , UpperCAmelCase__   :  Union[str, Any]     , UpperCAmelCase__   :  List[Any] = None     , **UpperCAmelCase__   :  List[str]     , ):






							"""simple docstring"""
							return normalize(_UpperCamelCase     , mean=_UpperCamelCase     , std=_UpperCamelCase     , data_format=_UpperCamelCase     , **_UpperCamelCase						)



			def      lowerCAmelCase(   self   :  Tuple     , UpperCAmelCase__   :  Any     , UpperCAmelCase__   :  Optional[Any] = None     , UpperCAmelCase__   :  str = None     , UpperCAmelCase__   :  List[Any]=None     , UpperCAmelCase__   :  List[Any] = None     , UpperCAmelCase__   :  List[Any] = None     , UpperCAmelCase__   :  Optional[int] = None     , UpperCAmelCase__   :  Optional[int] = None     , UpperCAmelCase__   :  Union[str, Any] = None     , UpperCAmelCase__   :  str = None     , UpperCAmelCase__   :  int = None     , UpperCAmelCase__   :  Tuple = None     , UpperCAmelCase__   :  Union[str, Any] = None     , UpperCAmelCase__   :  Union[str, Any] = ChannelDimension.FIRST     , **UpperCAmelCase__   :  List[str]     , ):






							"""simple docstring"""
							snake_case   :					Any    						=				do_resize if do_resize is not None else self.do_resize
							snake_case   :					Any    						=				size if size is not None else self.size
							snake_case   :					Union[str, Any]    						=				get_size_dict(_UpperCamelCase						)
							snake_case   :					List[Any]    						=				resample if resample is not None else self.resample
							snake_case   :					Dict    						=				do_rescale if do_rescale is not None else self.do_rescale
							snake_case   :					List[Any]    						=				rescale_factor if rescale_factor is not None else self.rescale_factor
							snake_case   :					int    						=				do_normalize if do_normalize is not None else self.do_normalize
							snake_case   :					List[str]    						=				image_mean if image_mean is not None else self.image_mean
							snake_case   :					Optional[int]    						=				image_std if image_std is not None else self.image_std
							snake_case   :					List[Any]    						=				apply_ocr if apply_ocr is not None else self.apply_ocr
							snake_case   :					Optional[Any]    						=				ocr_lang if ocr_lang is not None else self.ocr_lang
							snake_case   :					Union[str, Any]    						=				tesseract_config if tesseract_config is not None else self.tesseract_config

							snake_case   :					Tuple    						=				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.'''						)

							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.
							snake_case   :					int    						=				[to_numpy_array(_UpperCamelCase						) for image in images]

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

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

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

							if do_normalize:
											snake_case   :					Union[str, Any]    						=				[self.normalize(image=_UpperCamelCase     , mean=_UpperCamelCase     , std=_UpperCamelCase						) for image in images]

							snake_case   :					List[str]    						=				[to_channel_dimension_format(_UpperCamelCase     , _UpperCamelCase						) for image in images]

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

							if apply_ocr:
											snake_case   :					str    						=				words_batch
											snake_case   :					Union[str, Any]    						=				boxes_batch
							return data



import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCamelCase__		 =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')







def 						lowerCamelCase__      ( __A    :Any	,__A    :tuple	,__A    :Path	,__A    :int	,__A    :Union[str, Any]	,__A    :Optional[Any]	,__A    :Optional[Any]	,__A    :List[Any]=False	,):


       """simple docstring"""




       output_path.parent.mkdir(parents=__A	,exist_ok=__A		)
       # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
       # so we check the torch version for backwards compatibility
       if is_torch_less_than_1_11:
              export(
                  __A	,__A	,f=output_path.as_posix()	,input_names=__A	,output_names=__A	,dynamic_axes=__A	,do_constant_folding=__A	,use_external_data_format=__A	,enable_onnx_checker=__A	,opset_version=__A	,)
       else:
              export(
                  __A	,__A	,f=output_path.as_posix()	,input_names=__A	,output_names=__A	,dynamic_axes=__A	,do_constant_folding=__A	,opset_version=__A	,)







@torch.no_grad()
def 						lowerCamelCase__      ( __A    :str	,__A    :str	,__A    :int	,__A    :bool = False		):


       """simple docstring"""




       __snake_case       	=							torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              __snake_case       	=							"""cuda"""
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
       else:
              __snake_case       	=							"""cpu"""
       __snake_case       	=							Path(__A		)

       # VAE DECODER
       __snake_case       	=							AutoencoderKL.from_pretrained(model_path + """/vae"""		)
       __snake_case       	=							vae_decoder.config.latent_channels
       # forward only through the decoder part
       __snake_case       	=							vae_decoder.decode
       onnx_export(
           __A	,model_args=(
               torch.randn(1	,__A	,2_5	,2_5		).to(device=__A	,dtype=__A		),
               False,
           )	,output_path=output_path / """vae_decoder""" / """model.onnx"""	,ordered_input_names=["""latent_sample""", """return_dict"""]	,output_names=["""sample"""]	,dynamic_axes={
               """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
           }	,opset=__A	,)
       del vae_decoder


if __name__ == "__main__":
    UpperCamelCase__		 =     argparse.ArgumentParser()

    parser.add_argument(
        '''--model_path''',
        type=str,
        required=True,
        help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
    )

    parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
    parser.add_argument(
        '''--opset''',
        default=14,
        type=int,
        help='''The version of the ONNX operator set to use.''',
    )
    parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

    UpperCamelCase__		 =     parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print('''SD: Done: ONNX''')

