




'''simple docstring'''


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


_snake_case				:				List[str]       							=		version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def 		snake_case_   (UpperCamelCase	:  Tuple			,						UpperCamelCase	:  List[str]			,						UpperCamelCase	:  Dict			,						UpperCamelCase	:  Any			,						UpperCamelCase	:  str			,						UpperCamelCase	:  Dict			,						UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  Optional[int]=False			,						):





							'''simple docstring'''


							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 		snake_case_   (UpperCamelCase	:  Tuple			,						UpperCamelCase	:  List[str]			,						UpperCamelCase	:  Tuple			,						UpperCamelCase	:  Tuple = False				):





							'''simple docstring'''


							_a     					=    torch.floataa if fpaa else torch.floataa
							if fpaa and torch.cuda.is_available():
														_a     					=    '''cuda'''
							elif fpaa and not torch.cuda.is_available():
														raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''				)
							else:
														_a     					=    '''cpu'''
							_a     					=    StableDiffusionPipeline.from_pretrained(UpperCamelCase			,						torch_dtype=UpperCamelCase				).to(UpperCamelCase				)
							_a     					=    Path(UpperCamelCase				)

							# TEXT ENCODER
							_a     					=    pipeline.text_encoder.config.max_position_embeddings
							_a     					=    pipeline.text_encoder.config.hidden_size
							_a     					=    pipeline.tokenizer(
							    '''A sample prompt'''			,						padding='''max_length'''			,						max_length=pipeline.tokenizer.model_max_length			,						truncation=UpperCamelCase			,						return_tensors='''pt'''			,						)
							onnx_export(
							    pipeline.text_encoder			,						model_args=(text_input.input_ids.to(device=UpperCamelCase			,						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=UpperCamelCase			,						)
							del pipeline.text_encoder

							# UNET
							_a     					=    pipeline.unet.config.in_channels
							_a     					=    pipeline.unet.config.sample_size
							_a     					=    output_path / '''unet''' / '''model.onnx'''
							onnx_export(
							    pipeline.unet			,						model_args=(
							        torch.randn(2			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				).to(device=UpperCamelCase			,						dtype=UpperCamelCase				),
							        torch.randn(2				).to(device=UpperCamelCase			,						dtype=UpperCamelCase				),
							        torch.randn(2			,						UpperCamelCase			,						UpperCamelCase				).to(device=UpperCamelCase			,						dtype=UpperCamelCase				),
							        False,
							    )			,						output_path=UpperCamelCase			,						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=UpperCamelCase			,						use_external_data_format=UpperCamelCase			,						)
							_a     					=    str(unet_path.absolute().as_posix()				)
							_a     					=    os.path.dirname(UpperCamelCase				)
							_a     					=    onnx.load(UpperCamelCase				)
							# clean up existing tensor files
							shutil.rmtree(UpperCamelCase				)
							os.mkdir(UpperCamelCase				)
							# collate external tensor files into one
							onnx.save_model(
							    UpperCamelCase			,						UpperCamelCase			,						save_as_external_data=UpperCamelCase			,						all_tensors_to_one_file=UpperCamelCase			,						location='''weights.pb'''			,						convert_attribute=UpperCamelCase			,						)
							del pipeline.unet

							# VAE ENCODER
							_a     					=    pipeline.vae
							_a     					=    vae_encoder.config.in_channels
							_a     					=    vae_encoder.config.sample_size
							# need to get the raw tensor output (sample) from the encoder
							_a     					=    lambda UpperCamelCase			,						UpperCamelCase				: vae_encoder.encode(UpperCamelCase			,						UpperCamelCase				)[0].sample()
							onnx_export(
							    UpperCamelCase			,						model_args=(
							        torch.randn(1			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				).to(device=UpperCamelCase			,						dtype=UpperCamelCase				),
							        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=UpperCamelCase			,						)

							# VAE DECODER
							_a     					=    pipeline.vae
							_a     					=    vae_decoder.config.latent_channels
							_a     					=    vae_decoder.config.out_channels
							# forward only through the decoder part
							_a     					=    vae_encoder.decode
							onnx_export(
							    UpperCamelCase			,						model_args=(
							        torch.randn(1			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				).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 pipeline.vae

							# SAFETY CHECKER
							if pipeline.safety_checker is not None:
														_a     					=    pipeline.safety_checker
														_a     					=    safety_checker.config.vision_config.num_channels
														_a     					=    safety_checker.config.vision_config.image_size
														_a     					=    safety_checker.forward_onnx
														onnx_export(
														    pipeline.safety_checker			,						model_args=(
														        torch.randn(
														            1			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						).to(device=UpperCamelCase			,						dtype=UpperCamelCase				),
														        torch.randn(1			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				).to(device=UpperCamelCase			,						dtype=UpperCamelCase				),
														    )			,						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=UpperCamelCase			,						)
														del pipeline.safety_checker
														_a     					=    OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker'''				)
														_a     					=    pipeline.feature_extractor
							else:
														_a     					=    None
														_a     					=    None

							_a     					=    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=UpperCamelCase			,						feature_extractor=UpperCamelCase			,						requires_safety_checker=safety_checker is not None			,						)

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

							del pipeline
							del onnx_pipeline
							_a     					=    OnnxStableDiffusionPipeline.from_pretrained(UpperCamelCase			,						provider='''CPUExecutionProvider'''				)
							print('''ONNX pipeline is loadable'''				)


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

				_snake_case				:				Any       							=		parser.parse_args()

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







'''simple docstring'''


import argparse
from collections import defaultdict

import yaml


_snake_case				:				int       							=		'docs/source/en/_toctree.yml'
def 		snake_case_   (UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							_a     					=    defaultdict(UpperCamelCase				)
							for doc in model_doc:
														counts[doc["local"]] += 1
							_a     					=    [key for key, value in counts.items() if value > 1]

							_a     					=    []
							for duplicate_key in duplicates:
														_a     					=    list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}				)
														if len(UpperCamelCase				) > 1:
																					raise ValueError(
																					    f'{duplicate_key} is present several times in the documentation table of content at '
																					    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																					    '''others.'''				)
														# Only add this once
														new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}				)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]				)

							# Sort
							return sorted(UpperCamelCase			,						key=lambda UpperCamelCase				: s["title"].lower()				)
def 		snake_case_   (UpperCamelCase	:  str=False				):





							'''simple docstring'''


							with open(UpperCamelCase			,						encoding='''utf-8'''				) as f:
														_a     					=    yaml.safe_load(f.read()				)

							# Get to the API doc
							_a     					=    0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_a     					=    content[api_idx]['''sections''']

							# Then to the model doc
							_a     					=    0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							_a     					=    api_doc[model_idx]['''sections''']

							_a     					=    [(idx, section) for idx, section in enumerate(UpperCamelCase				) if '''sections''' in section]
							_a     					=    False
							for idx, modality_doc in modalities_docs:
														_a     					=    modality_doc['''sections''']
														_a     					=    clean_model_doc_toc(UpperCamelCase				)

														if old_modality_doc != new_modality_doc:
																					_a     					=    True
																					if overwrite:
																												_a     					=    new_modality_doc

							if diff:
														if overwrite:
																					_a     					=    model_doc
																					_a     					=    api_doc
																					with open(UpperCamelCase			,						'''w'''			,						encoding='''utf-8'''				) as f:
																												f.write(yaml.dump(UpperCamelCase			,						allow_unicode=UpperCamelCase				)				)
														else:
																					raise ValueError(
																					    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''				)


if __name__ == "__main__":
				_snake_case				:				List[str]       							=		argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				_snake_case				:				List[Any]       							=		parser.parse_args()

				check_model_doc(args.fix_and_overwrite)

