




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


SCREAMING_SNAKE_CASE:     Any									= version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')


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


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase = False )->	Any:
		SCREAMING_SNAKE_CASE_					      =							torch.floataa if fpaa else torch.floataa
		if fpaa and torch.cuda.is_available():
				SCREAMING_SNAKE_CASE_					      =							'cuda'
		elif fpaa and not torch.cuda.is_available():
				raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
		else:
				SCREAMING_SNAKE_CASE_					      =							'cpu'
		SCREAMING_SNAKE_CASE_					      =							StableDiffusionPipeline.from_pretrained(lowerCAmelCase    ,						torch_dtype=lowerCAmelCase ).to(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							Path(lowerCAmelCase )

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

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

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

		# VAE DECODER
		SCREAMING_SNAKE_CASE_					      =							pipeline.vae
		SCREAMING_SNAKE_CASE_					      =							vae_decoder.config.latent_channels
		SCREAMING_SNAKE_CASE_					      =							vae_decoder.config.out_channels
		# forward only through the decoder part
		SCREAMING_SNAKE_CASE_					      =							vae_encoder.decode
		onnx_export(
		    lowerCAmelCase    ,						model_args=(
		        torch.randn(1    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase ).to(device=lowerCAmelCase    ,						dtype=lowerCAmelCase ),
		        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=lowerCAmelCase    ,						)
		del pipeline.vae

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

		SCREAMING_SNAKE_CASE_					      =							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=lowerCAmelCase    ,						feature_extractor=lowerCAmelCase    ,						requires_safety_checker=safety_checker is not None    ,						)

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

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     List[str]									= 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''')

						SCREAMING_SNAKE_CASE:     Tuple									= parser.parse_args()

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





import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE:     int									= ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE:     Tuple									= {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
						raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= ''' Hello world! cécé herlolip'''

SCREAMING_SNAKE_CASE:     Dict									= [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]


def 					_a							(					lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    '_float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							dct.pop(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							val


def 					_a							(					lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
		SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						'bart.large.cnn' ).eval()
		hub_interface.model.load_state_dict(sd['model'] )
		return hub_interface


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							emb.weight.shape
		SCREAMING_SNAKE_CASE_					      =							nn.Linear(lowerCAmelCase    ,						lowerCAmelCase    ,						bias=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							emb.weight.data
		return lin_layer


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		if not os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						lowerCAmelCase ).eval()
		else:
				SCREAMING_SNAKE_CASE_					      =							load_xsum_checkpoint(lowerCAmelCase )

		bart.model.upgrade_state_dict(bart.model.state_dict() )
		if hf_checkpoint_name is None:
				SCREAMING_SNAKE_CASE_					      =							checkpoint_path.replace('.'    ,						'-' )
		SCREAMING_SNAKE_CASE_					      =							BartConfig.from_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							bart.encode(lowerCAmelCase ).unsqueeze(0 )
		SCREAMING_SNAKE_CASE_					      =							BartTokenizer.from_pretrained(lowerCAmelCase ).encode(lowerCAmelCase    ,						return_tensors='pt' ).unsqueeze(0 )
		if not torch.eq(lowerCAmelCase    ,						lowerCAmelCase ).all():
				raise ValueError(
				    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )

		if checkpoint_path == "bart.large.mnli":
				SCREAMING_SNAKE_CASE_					      =							bart.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['model.decoder.embed_tokens.weight']
				for src, dest in mnli_rename_keys:
						rename_key(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							BartForSequenceClassification(lowerCAmelCase ).eval()
				model.load_state_dict(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							bart.predict('mnli'    ,						lowerCAmelCase    ,						return_logits=lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase )[0]  # logits
		else:  # no classification heads to worry about
				SCREAMING_SNAKE_CASE_					      =							bart.model.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['decoder.embed_tokens.weight']
				SCREAMING_SNAKE_CASE_					      =							bart.extract_features(lowerCAmelCase )
				if hf_checkpoint_name == "facebook/bart-large":
						SCREAMING_SNAKE_CASE_					      =							BartModel(lowerCAmelCase ).eval()
						model.load_state_dict(lowerCAmelCase )
						SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase ).model[0]
				else:
						SCREAMING_SNAKE_CASE_					      =							BartForConditionalGeneration(lowerCAmelCase ).eval()  # an existing summarization ckpt
						model.model.load_state_dict(lowerCAmelCase )
						if hasattr(lowerCAmelCase    ,						'lm_head' ):
								SCREAMING_SNAKE_CASE_					      =							make_linear_from_emb(model.model.shared )
						SCREAMING_SNAKE_CASE_					      =							model.model(lowerCAmelCase )[0]

    # Check results
		if fairseq_output.shape != new_model_outputs.shape:
				raise ValueError(
				    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
		if (fairseq_output != new_model_outputs).any().item():
				raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
		Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Dict									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
						)
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
						)
						SCREAMING_SNAKE_CASE:     List[str]									= parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)