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)







import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Optional[Any]       					=IFPipeline
							lowercase_       :       List[str]       					=TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
							lowercase_       :       List[str]       					=TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase_       :       int       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()




							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)





							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)







@slow
@require_torch_gpu
class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def  A__      (				self):
												# if

												lowercase							=						IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''   ,variant='''fp16'''   ,torch_dtype=torch.floataa)

												lowercase							=						IFSuperResolutionPipeline.from_pretrained(
												    '''DeepFloyd/IF-II-L-v1.0'''   ,variant='''fp16'''   ,torch_dtype=torch.floataa   ,text_encoder=A__   ,tokenizer=A__)

												# pre compute text embeddings and remove T5 to save memory

												pipe_a.text_encoder.to('''cuda''')

												lowercase	,						lowercase							=						pipe_a.encode_prompt('''anime turtle'''   ,device='''cuda''')

												del pipe_a.tokenizer
												del pipe_a.text_encoder
												gc.collect()

												lowercase							=						None
												lowercase							=						None

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if(A__   ,A__   ,A__   ,A__)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# img2img

												lowercase							=						IFImgaImgPipeline(**pipe_a.components)
												lowercase							=						IFImgaImgSuperResolutionPipeline(**pipe_a.components)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if_imgaimg(A__   ,A__   ,A__   ,A__)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# inpainting

												lowercase							=						IFInpaintingPipeline(**pipe_a.components)
												lowercase							=						IFInpaintingSuperResolutionPipeline(**pipe_a.components)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if_inpainting(A__   ,A__   ,A__   ,A__)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)
												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_3 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_0 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,original_image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)





							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(1)).to(A__)

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)
												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,mask_image=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_0 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(1)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,mask_image=A__   ,original_image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)



def        UpperCamelCase    (							):
					'''simple docstring'''
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()




