





from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
							import torch

if is_tf_available():
							import tensorflow as tf

class       __A     (						a    ):
				__A      =				["""image_processor"""]
				__A      =				"""SamImageProcessor"""


				def __init__(		self   , UpperCAmelCase_							):
								super().__init__(UpperCAmelCase_							)
								lowerCamelCase	 =self.image_processor
								lowerCamelCase	 =-10
								lowerCamelCase	 =self.image_processor.size["""longest_edge"""]


				def __call__(		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =self.image_processor(
								    UpperCAmelCase_   , return_tensors=UpperCAmelCase_   , **UpperCAmelCase_   , )

								# pop arguments that are not used in the foward but used nevertheless
								lowerCamelCase	 =encoding_image_processor["""original_sizes"""]

								if hasattr(UpperCAmelCase_   , """numpy"""							):  # Checks if Torch or TF tensor
												lowerCamelCase	 =original_sizes.numpy()

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._check_and_preprocess_points(
								    input_points=UpperCAmelCase_   , input_labels=UpperCAmelCase_   , input_boxes=UpperCAmelCase_   , )

								lowerCamelCase	 =self._normalize_and_convert(
								    UpperCAmelCase_   , UpperCAmelCase_   , input_points=UpperCAmelCase_   , input_labels=UpperCAmelCase_   , input_boxes=UpperCAmelCase_   , return_tensors=UpperCAmelCase_   , )

								return encoding_image_processor


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_="pt"   , ):
								if input_points is not None:
												if len(UpperCAmelCase_							) != len(UpperCAmelCase_							):
																lowerCamelCase	 =[
																    self._normalize_coordinates(self.target_size   , UpperCAmelCase_   , original_sizes[0]							) for point in input_points
																]
												else:
																lowerCamelCase	 =[
																    self._normalize_coordinates(self.target_size   , UpperCAmelCase_   , UpperCAmelCase_							)
																    for point, original_size in zip(UpperCAmelCase_   , UpperCAmelCase_							)
																]
												# check that all arrays have the same shape
												if not all(point.shape == input_points[0].shape for point in input_points							):
																if input_labels is not None:
																				lowerCamelCase ,  lowerCamelCase	 =self._pad_points_and_labels(UpperCAmelCase_   , UpperCAmelCase_							)

												lowerCamelCase	 =np.array(UpperCAmelCase_							)

								if input_labels is not None:
												lowerCamelCase	 =np.array(UpperCAmelCase_							)

								if input_boxes is not None:
												if len(UpperCAmelCase_							) != len(UpperCAmelCase_							):
																lowerCamelCase	 =[
																    self._normalize_coordinates(self.target_size   , UpperCAmelCase_   , original_sizes[0]   , is_bounding_box=UpperCAmelCase_							)
																    for box in input_boxes
																]
												else:
																lowerCamelCase	 =[
																    self._normalize_coordinates(self.target_size   , UpperCAmelCase_   , UpperCAmelCase_   , is_bounding_box=UpperCAmelCase_							)
																    for box, original_size in zip(UpperCAmelCase_   , UpperCAmelCase_							)
																]
												lowerCamelCase	 =np.array(UpperCAmelCase_							)

								if input_boxes is not None:
												if return_tensors == "pt":
																lowerCamelCase	 =torch.from_numpy(UpperCAmelCase_							)
																# boxes batch size of 1 by default
																lowerCamelCase	 =input_boxes.unsqueeze(1							) if len(input_boxes.shape							) != 3 else input_boxes
												elif return_tensors == "tf":
																lowerCamelCase	 =tf.convert_to_tensor(UpperCAmelCase_							)
																# boxes batch size of 1 by default
																lowerCamelCase	 =tf.expand_dims(UpperCAmelCase_   , 1							) if len(input_boxes.shape							) != 3 else input_boxes
												encoding_image_processor.update({"""input_boxes""": input_boxes}							)
								if input_points is not None:
												if return_tensors == "pt":
																lowerCamelCase	 =torch.from_numpy(UpperCAmelCase_							)
																# point batch size of 1 by default
																lowerCamelCase	 =input_points.unsqueeze(1							) if len(input_points.shape							) != 4 else input_points
												elif return_tensors == "tf":
																lowerCamelCase	 =tf.convert_to_tensor(UpperCAmelCase_							)
																# point batch size of 1 by default
																lowerCamelCase	 =tf.expand_dims(UpperCAmelCase_   , 1							) if len(input_points.shape							) != 4 else input_points
												encoding_image_processor.update({"""input_points""": input_points}							)
								if input_labels is not None:
												if return_tensors == "pt":
																lowerCamelCase	 =torch.from_numpy(UpperCAmelCase_							)
																# point batch size of 1 by default
																lowerCamelCase	 =input_labels.unsqueeze(1							) if len(input_labels.shape							) != 3 else input_labels
												elif return_tensors == "tf":
																lowerCamelCase	 =tf.convert_to_tensor(UpperCAmelCase_							)
																# point batch size of 1 by default
																lowerCamelCase	 =tf.expand_dims(UpperCAmelCase_   , 1							) if len(input_labels.shape							) != 3 else input_labels
												encoding_image_processor.update({"""input_labels""": input_labels}							)

								return encoding_image_processor


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =max([point.shape[0] for point in input_points]							)
								lowerCamelCase	 =[]
								for i, point in enumerate(UpperCAmelCase_							):
												if point.shape[0] != expected_nb_points:
																lowerCamelCase	 =np.concatenate(
																    [point, np.zeros((expected_nb_points - point.shape[0], 2)							) + self.point_pad_value]   , axis=0							)
																lowerCamelCase	 =np.append(input_labels[i]   , [self.point_pad_value]							)
												processed_input_points.append(UpperCAmelCase_							)
								lowerCamelCase	 =processed_input_points
								return input_points, input_labels


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=False							):
								lowerCamelCase ,  lowerCamelCase	 =original_size
								lowerCamelCase ,  lowerCamelCase	 =self.image_processor._get_preprocess_shape(UpperCAmelCase_   , longest_edge=UpperCAmelCase_							)
								lowerCamelCase	 =deepcopy(UpperCAmelCase_							).astype(UpperCAmelCase_							)

								if is_bounding_box:
												lowerCamelCase	 =coords.reshape(-1   , 2   , 2							)

								lowerCamelCase	 =coords[..., 0] * (new_w / old_w)
								lowerCamelCase	 =coords[..., 1] * (new_h / old_h)

								if is_bounding_box:
												lowerCamelCase	 =coords.reshape(-1   , 4							)

								return coords


				def _snake_case       (		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , ):
								if input_points is not None:
												if hasattr(UpperCAmelCase_   , """numpy"""							):  # Checks for TF or Torch tensor
																lowerCamelCase	 =input_points.numpy().tolist()

												if not isinstance(UpperCAmelCase_   , UpperCAmelCase_							) or not isinstance(input_points[0]   , UpperCAmelCase_							):
																raise ValueError("""Input points must be a list of list of floating points."""							)
												lowerCamelCase	 =[np.array(UpperCAmelCase_							) for input_point in input_points]
								else:
												lowerCamelCase	 =None

								if input_labels is not None:
												if hasattr(UpperCAmelCase_   , """numpy"""							):
																lowerCamelCase	 =input_labels.numpy().tolist()

												if not isinstance(UpperCAmelCase_   , UpperCAmelCase_							) or not isinstance(input_labels[0]   , UpperCAmelCase_							):
																raise ValueError("""Input labels must be a list of list integers."""							)
												lowerCamelCase	 =[np.array(UpperCAmelCase_							) for label in input_labels]
								else:
												lowerCamelCase	 =None

								if input_boxes is not None:
												if hasattr(UpperCAmelCase_   , """numpy"""							):
																lowerCamelCase	 =input_boxes.numpy().tolist()

												if (
												    not isinstance(UpperCAmelCase_   , UpperCAmelCase_							)
												    or not isinstance(input_boxes[0]   , UpperCAmelCase_							)
												    or not isinstance(input_boxes[0][0]   , UpperCAmelCase_							)
												):
																raise ValueError("""Input boxes must be a list of list of list of floating points."""							)
												lowerCamelCase	 =[np.array(UpperCAmelCase_							).astype(np.floataa							) for box in input_boxes]
								else:
												lowerCamelCase	 =None

								return input_points, input_labels, input_boxes


				@property
				def _snake_case       (		self							):
								lowerCamelCase	 =self.image_processor.model_input_names
								return list(dict.fromkeys(UpperCAmelCase_							)							)


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								return self.image_processor.post_process_masks(*UpperCAmelCase_   , **UpperCAmelCase_							)












import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase__						:			Tuple									=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False			,      )						->  int:
				output_path.parent.mkdir(parents=_UpperCAmelCase			,      exist_ok=_UpperCAmelCase				)
				# 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(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      use_external_data_format=_UpperCAmelCase			,      enable_onnx_checker=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )
				else:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = False				)						->  Tuple:
				lowerCamelCase	 =torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								lowerCamelCase	 ="""cuda"""
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""				)
				else:
								lowerCamelCase	 ="""cpu"""
				lowerCamelCase	 =Path(_UpperCAmelCase				)

				# VAE DECODER
				lowerCamelCase	 =AutoencoderKL.from_pretrained(model_path + """/vae"""				)
				lowerCamelCase	 =vae_decoder.config.latent_channels
				# forward only through the decoder part
				lowerCamelCase	 =vae_decoder.decode
				onnx_export(
				    _UpperCAmelCase			,      model_args=(
				        torch.randn(1			,      _UpperCAmelCase			,      25			,      25				).to(device=_UpperCAmelCase			,      dtype=_UpperCAmelCase				),
				        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=_UpperCAmelCase			,      )
				del vae_decoder


if __name__ == "__main__":
							UpperCAmelCase__						:			Union[str, Any]									=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__						:			Union[str, Any]									=parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print('''SD: Done: ONNX''')





