





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=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_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase = False			):






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								snake_case_						:   Tuple           =  '''cuda'''
				elif fpaa and not torch.cuda.is_available():
								raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''			)
				else:
								snake_case_						:   Union[str, Any]           =  '''cpu'''
				snake_case_						:   int           =  Path(A__			)

				# VAE DECODER
				snake_case_						:   List[str]           =  AutoencoderKL.from_pretrained(model_path + '''/vae'''			)
				snake_case_						:   Union[str, Any]           =  vae_decoder.config.latent_channels
				# forward only through the decoder part
				snake_case_						:   str           =  vae_decoder.decode
				onnx_export(
				    A__			,    model_args=(
				        torch.randn(1			,    A__			,    25			,    25			).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__":
	lowerCAmelCase_    =    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=1_4,
	    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''')

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







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[Any]	      =				['''image_processor''', '''tokenizer''']
			lowerCamelCase_					:					List[Any]	      =				'''BlipImageProcessor'''
			lowerCamelCase_					:					Union[str, Any]	      =				'''AutoTokenizer'''






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )

							# add QFormer tokenizer
							snake_case_						:   Optional[Any]           =  qformer_tokenizer






			def __call__(self ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = True ,							__magic_name__ = False ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = 0 ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = True ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       BatchFeature:


							'''simple docstring'''



							if images is None and text is None:
											raise ValueError('''You have to specify at least images or text.'''   )

							snake_case_						:   Tuple           =  BatchFeature()

							if text is not None:
											snake_case_						:   Tuple           =  self.tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											encoding.update(__magic_name__   )
											snake_case_						:   Optional[Any]           =  self.qformer_tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											snake_case_						:   Optional[int]           =  qformer_text_encoding.pop('''input_ids'''   )
											snake_case_						:   Tuple           =  qformer_text_encoding.pop('''attention_mask'''   )

							if images is not None:
											snake_case_						:   Any           =  self.image_processor(__magic_name__ ,							return_tensors=__magic_name__   )
											encoding.update(__magic_name__   )

							return encoding






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							return self.tokenizer.batch_decode(*__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							return self.tokenizer.decode(*__magic_name__ ,							**__magic_name__   )






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  self.tokenizer.model_input_names
							snake_case_						:   List[Any]           =  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							if os.path.isfile(__magic_name__   ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''   )
							os.makedirs(__magic_name__ ,							exist_ok=__magic_name__   )
							snake_case_						:   Any           =  os.path.join(__magic_name__ ,							'''qformer_tokenizer'''   )
							self.qformer_tokenizer.save_pretrained(__magic_name__   )
							return super().save_pretrained(__magic_name__ ,							**__magic_name__   )






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  AutoTokenizer.from_pretrained(__magic_name__ ,							subfolder='''qformer_tokenizer'''   )
							snake_case_						:   str           =  cls._get_arguments_from_pretrained(__magic_name__ ,							**__magic_name__   )
							args.append(__magic_name__   )
							return cls(*__magic_name__   )
