import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False  ,			):
    '''simple docstring'''







    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE  ,			exist_ok=SCREAMING_SNAKE_CASE	)
    # 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(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			f=output_path.as_posix()  ,			input_names=SCREAMING_SNAKE_CASE  ,			output_names=SCREAMING_SNAKE_CASE  ,			dynamic_axes=SCREAMING_SNAKE_CASE  ,			do_constant_folding=SCREAMING_SNAKE_CASE  ,			use_external_data_format=SCREAMING_SNAKE_CASE  ,			enable_onnx_checker=SCREAMING_SNAKE_CASE  ,			opset_version=SCREAMING_SNAKE_CASE  ,			)
    else:
        export(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			f=output_path.as_posix()  ,			input_names=SCREAMING_SNAKE_CASE  ,			output_names=SCREAMING_SNAKE_CASE  ,			dynamic_axes=SCREAMING_SNAKE_CASE  ,			do_constant_folding=SCREAMING_SNAKE_CASE  ,			opset_version=SCREAMING_SNAKE_CASE  ,			)



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = False	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        __UpperCamelCase     :List[Any]			  =		'''cuda'''
    elif fpaa and not torch.cuda.is_available():
        raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''	)
    else:
        __UpperCamelCase     :Tuple			  =		'''cpu'''
    __UpperCamelCase     :Union[str, Any]			  =		StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE  ,			torch_dtype=SCREAMING_SNAKE_CASE	).to(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		Path(SCREAMING_SNAKE_CASE	)

    # TEXT ENCODER
    __UpperCamelCase     :str			  =		pipeline.text_encoder.config.max_position_embeddings
    __UpperCamelCase     :int			  =		pipeline.text_encoder.config.hidden_size
    __UpperCamelCase     :int			  =		pipeline.tokenizer(
        '''A sample prompt'''  ,			padding='''max_length'''  ,			max_length=pipeline.tokenizer.model_max_length  ,			truncation=SCREAMING_SNAKE_CASE  ,			return_tensors='''pt'''  ,			)
    onnx_export(
        pipeline.text_encoder  ,			model_args=(text_input.input_ids.to(device=SCREAMING_SNAKE_CASE  ,			dtype=torch.intaa	))  ,			output_path=output_path / '''text_encoder''' / '''model.onnx'''  ,			ordered_input_names=['''input_ids''']  ,			output_names=['''last_hidden_state''', '''pooler_output''']  ,			dynamic_axes={
            '''input_ids''': {0: '''batch''', 1: '''sequence'''},
        }  ,			opset=SCREAMING_SNAKE_CASE  ,			)
    del pipeline.text_encoder

    # UNET
    __UpperCamelCase     :Dict			  =		pipeline.unet.config.in_channels
    __UpperCamelCase     :Union[str, Any]			  =		pipeline.unet.config.sample_size
    __UpperCamelCase     :Optional[Any]			  =		output_path / '''unet''' / '''model.onnx'''
    onnx_export(
        pipeline.unet  ,			model_args=(
            torch.randn(2  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).to(device=SCREAMING_SNAKE_CASE  ,			dtype=SCREAMING_SNAKE_CASE	),
            torch.randn(2	).to(device=SCREAMING_SNAKE_CASE  ,			dtype=SCREAMING_SNAKE_CASE	),
            torch.randn(2  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).to(device=SCREAMING_SNAKE_CASE  ,			dtype=SCREAMING_SNAKE_CASE	),
            False,
        )  ,			output_path=SCREAMING_SNAKE_CASE  ,			ordered_input_names=['''sample''', '''timestep''', '''encoder_hidden_states''', '''return_dict''']  ,			output_names=['''out_sample''']  ,			dynamic_axes={
            '''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
            '''timestep''': {0: '''batch'''},
            '''encoder_hidden_states''': {0: '''batch''', 1: '''sequence'''},
        }  ,			opset=SCREAMING_SNAKE_CASE  ,			use_external_data_format=SCREAMING_SNAKE_CASE  ,			)
    __UpperCamelCase     :Optional[Any]			  =		str(unet_path.absolute().as_posix()	)
    __UpperCamelCase     :Union[str, Any]			  =		os.path.dirname(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Any			  =		onnx.load(SCREAMING_SNAKE_CASE	)
    # clean up existing tensor files
    shutil.rmtree(SCREAMING_SNAKE_CASE	)
    os.mkdir(SCREAMING_SNAKE_CASE	)
    # collate external tensor files into one
    onnx.save_model(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			save_as_external_data=SCREAMING_SNAKE_CASE  ,			all_tensors_to_one_file=SCREAMING_SNAKE_CASE  ,			location='''weights.pb'''  ,			convert_attribute=SCREAMING_SNAKE_CASE  ,			)
    del pipeline.unet

    # VAE ENCODER
    __UpperCamelCase     :str			  =		pipeline.vae
    __UpperCamelCase     :str			  =		vae_encoder.config.in_channels
    __UpperCamelCase     :Union[str, Any]			  =		vae_encoder.config.sample_size
    # need to get the raw tensor output (sample) from the encoder
    __UpperCamelCase     :Optional[int]			  =		lambda SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	: vae_encoder.encode(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)[0].sample()
    onnx_export(
        SCREAMING_SNAKE_CASE  ,			model_args=(
            torch.randn(1  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).to(device=SCREAMING_SNAKE_CASE  ,			dtype=SCREAMING_SNAKE_CASE	),
            False,
        )  ,			output_path=output_path / '''vae_encoder''' / '''model.onnx'''  ,			ordered_input_names=['''sample''', '''return_dict''']  ,			output_names=['''latent_sample''']  ,			dynamic_axes={
            '''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
        }  ,			opset=SCREAMING_SNAKE_CASE  ,			)

    # VAE DECODER
    __UpperCamelCase     :str			  =		pipeline.vae
    __UpperCamelCase     :Optional[int]			  =		vae_decoder.config.latent_channels
    __UpperCamelCase     :str			  =		vae_decoder.config.out_channels
    # forward only through the decoder part
    __UpperCamelCase     :Dict			  =		vae_encoder.decode
    onnx_export(
        SCREAMING_SNAKE_CASE  ,			model_args=(
            torch.randn(1  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).to(device=SCREAMING_SNAKE_CASE  ,			dtype=SCREAMING_SNAKE_CASE	),
            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=SCREAMING_SNAKE_CASE  ,			)
    del pipeline.vae

    # SAFETY CHECKER
    if pipeline.safety_checker is not None:
        __UpperCamelCase     :Any			  =		pipeline.safety_checker
        __UpperCamelCase     :Tuple			  =		safety_checker.config.vision_config.num_channels
        __UpperCamelCase     :Any			  =		safety_checker.config.vision_config.image_size
        __UpperCamelCase     :Tuple			  =		safety_checker.forward_onnx
        onnx_export(
            pipeline.safety_checker  ,			model_args=(
                torch.randn(
                    1  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			).to(device=SCREAMING_SNAKE_CASE  ,			dtype=SCREAMING_SNAKE_CASE	),
                torch.randn(1  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).to(device=SCREAMING_SNAKE_CASE  ,			dtype=SCREAMING_SNAKE_CASE	),
            )  ,			output_path=output_path / '''safety_checker''' / '''model.onnx'''  ,			ordered_input_names=['''clip_input''', '''images''']  ,			output_names=['''out_images''', '''has_nsfw_concepts''']  ,			dynamic_axes={
                '''clip_input''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
                '''images''': {0: '''batch''', 1: '''height''', 2: '''width''', 3: '''channels'''},
            }  ,			opset=SCREAMING_SNAKE_CASE  ,			)
        del pipeline.safety_checker
        __UpperCamelCase     :Optional[int]			  =		OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker'''	)
        __UpperCamelCase     :List[Any]			  =		pipeline.feature_extractor
    else:
        __UpperCamelCase     :Any			  =		None
        __UpperCamelCase     :str			  =		None

    __UpperCamelCase     :Tuple			  =		OnnxStableDiffusionPipeline(
        vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_encoder'''	)  ,			vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_decoder'''	)  ,			text_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''text_encoder'''	)  ,			tokenizer=pipeline.tokenizer  ,			unet=OnnxRuntimeModel.from_pretrained(output_path / '''unet'''	)  ,			scheduler=pipeline.scheduler  ,			safety_checker=SCREAMING_SNAKE_CASE  ,			feature_extractor=SCREAMING_SNAKE_CASE  ,			requires_safety_checker=safety_checker is not None  ,			)

    onnx_pipeline.save_pretrained(SCREAMING_SNAKE_CASE	)
    print('''ONNX pipeline saved to'''  ,			SCREAMING_SNAKE_CASE	)

    del pipeline
    del onnx_pipeline
    __UpperCamelCase     :Union[str, Any]			  =		OnnxStableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE  ,			provider='''CPUExecutionProvider'''	)
    print('''ONNX pipeline is loadable'''	)


if __name__ == "__main__":
    __lowercase							 =					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''')

    __lowercase							 =					parser.parse_args()

    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)












from __future__ import annotations




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int      )      ->	list[list[int]]:
    UpperCamelCase__							:				list[list[int]]          =      []
    create_all_state(1		,   __UpperCAmelCase		,   __UpperCAmelCase		,   []		,   __UpperCAmelCase      )
    return result




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			list[int]		,   __UpperCAmelCase:			list[list[int]]		,   )      ->	None:
    if level == 0:
        total_list.append(current_list[:]      )
        return

    for i in range(__UpperCAmelCase		,   total_number - level + 2      ):
        current_list.append(__UpperCAmelCase      )
        create_all_state(i + 1		,   __UpperCAmelCase		,   level - 1		,   __UpperCAmelCase		,   __UpperCAmelCase      )
        current_list.pop()




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list[list[int]]      )      ->	None:
    for i in total_list:
        print(*__UpperCAmelCase      )


if __name__ == "__main__":
  UpperCAmelCase_     							= 4
  UpperCAmelCase_     							= 2
  UpperCAmelCase_     							= generate_all_combinations(n, k)
  print_all_state(total_list)

