




"""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


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





def 							lowercase					(snake_case__   : List[Any]      ,       snake_case__   : tuple      ,       snake_case__   : Path      ,       snake_case__   : Tuple      ,       snake_case__   : List[str]      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Union[str, Any]      ,       snake_case__   : Union[str, Any]=False      ,       )     ->  str:


				'''simple docstring'''






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





@torch.no_grad()
def 							lowercase					(snake_case__   : str      ,       snake_case__   : str      ,       snake_case__   : int      ,       snake_case__   : bool = False )     ->  Optional[Any]:


				'''simple docstring'''






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

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

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

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

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

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

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

				onnx_pipeline.save_pretrained(snake_case__ )
				print("""ONNX pipeline saved to"""      ,       snake_case__ )

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


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

		a      =							parser.parse_args()

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











"""simple docstring"""







import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Optional[int]=13						,						lowerCAmelCase				:			List[str]=30						,						lowerCAmelCase				:			Dict=2						,						lowerCAmelCase				:			Any=3						,						lowerCAmelCase				:			str=True						,						lowerCAmelCase				:			List[Any]=True						,						lowerCAmelCase				:			Dict=32						,						lowerCAmelCase				:			Dict=5						,						lowerCAmelCase				:			str=4						,						lowerCAmelCase				:			List[Any]=37						,						lowerCAmelCase				:			List[Any]="gelu"						,						lowerCAmelCase				:			List[Any]=0.1						,						lowerCAmelCase				:			List[Any]=0.1						,						lowerCAmelCase				:			Optional[int]=10						,						lowerCAmelCase				:			List[str]=0.02						,						lowerCAmelCase				:			List[str]=None						,						):
										lowerCAmelCase  =				parent
										lowerCAmelCase  =				batch_size
										lowerCAmelCase  =				image_size
										lowerCAmelCase  =				patch_size
										lowerCAmelCase  =				num_channels
										lowerCAmelCase  =				is_training
										lowerCAmelCase  =				use_labels
										lowerCAmelCase  =				hidden_size
										lowerCAmelCase  =				num_hidden_layers
										lowerCAmelCase  =				num_attention_heads
										lowerCAmelCase  =				intermediate_size
										lowerCAmelCase  =				hidden_act
										lowerCAmelCase  =				hidden_dropout_prob
										lowerCAmelCase  =				attention_probs_dropout_prob
										lowerCAmelCase  =				type_sequence_label_size
										lowerCAmelCase  =				initializer_range
										lowerCAmelCase  =				scope

										# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										lowerCAmelCase  =				(image_size // patch_size) ** 2
										lowerCAmelCase  =				num_patches + 1


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										lowerCAmelCase  =				None
										if self.use_labels:
														lowerCAmelCase  =				ids_tensor([self.batch_size]						,						self.type_sequence_label_size					)

										lowerCAmelCase  =				self.get_config()

										return config, pixel_values, labels


						def   __lowercase (							self				:			Tuple					):
										return ViTMSNConfig(
										    image_size=self.image_size						,						patch_size=self.patch_size						,						num_channels=self.num_channels						,						hidden_size=self.hidden_size						,						num_hidden_layers=self.num_hidden_layers						,						num_attention_heads=self.num_attention_heads						,						intermediate_size=self.intermediate_size						,						hidden_act=self.hidden_act						,						hidden_dropout_prob=self.hidden_dropout_prob						,						attention_probs_dropout_prob=self.attention_probs_dropout_prob						,						initializer_range=self.initializer_range						,						)


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Tuple					):
										lowerCAmelCase  =				ViTMSNModel(config=lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)					)


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.type_sequence_label_size
										lowerCAmelCase  =				ViTMSNForImageClassification(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()
										lowerCAmelCase  =				model(lowerCAmelCase						,						labels=lowerCAmelCase					)
										print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""					)
										print("""Labels: {labels}"""					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)					)

										# test greyscale images
										lowerCAmelCase  =				1
										lowerCAmelCase  =				ViTMSNForImageClassification(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()

										lowerCAmelCase  =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)					)




						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				self.prepare_config_and_inputs()
										lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				config_and_inputs
										lowerCAmelCase  =				{"""pixel_values""": pixel_values}
										return config, inputs_dict






@require_torch
class       SCREAMING_SNAKE_CASE__					(					_a						,				_a						,				unittest.TestCase					):
						_a    						=						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						_a    						=						(
						    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						False


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				ViTMSNModelTester(self					)
										lowerCAmelCase  =				ConfigTester(self						,						config_class=lowerCAmelCase						,						has_text_modality=lowerCAmelCase						,						hidden_size=37					)


						def   __lowercase (							self				:			Dict					):
										self.config_tester.run_common_tests()


						@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""					)
						def   __lowercase (							self				:			Optional[Any]					):
										pass


						def   __lowercase (							self				:			int					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowerCAmelCase  =				model_class(lowerCAmelCase					)
														self.assertIsInstance(model.get_input_embeddings()						,						(nn.Module)					)
														lowerCAmelCase  =				model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(lowerCAmelCase						,						nn.Linear					)					)


						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowerCAmelCase  =				model_class(lowerCAmelCase					)
														lowerCAmelCase  =				inspect.signature(model.forward					)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														lowerCAmelCase  =				[*signature.parameters.keys()]

														lowerCAmelCase  =				["""pixel_values"""]
														self.assertListEqual(arg_names[:1]						,						lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase					)




						@slow
						def   __lowercase (							self				:			Optional[Any]					):
										for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase  =				ViTMSNModel.from_pretrained(lowerCAmelCase					)
														self.assertIsNotNone(lowerCAmelCase					)





def 							lowercase					()     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
				return image






@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@cached_property
						def   __lowercase (							self				:			Dict					):
										return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""					) if is_vision_available() else None




						@slow
						def   __lowercase (							self				:			Tuple					):
										torch.manual_seed(2					)
										lowerCAmelCase  =				ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""					).to(lowerCAmelCase					)

										lowerCAmelCase  =				self.default_image_processor
										lowerCAmelCase  =				prepare_img()
										lowerCAmelCase  =				image_processor(images=lowerCAmelCase						,						return_tensors="""pt"""					).to(lowerCAmelCase					)

										# forward pass
										with torch.no_grad():
														lowerCAmelCase  =				model(**lowerCAmelCase					)

										# verify the logits
										lowerCAmelCase  =				torch.Size((1, 1000)					)
										self.assertEqual(outputs.logits.shape						,						lowerCAmelCase					)

										lowerCAmelCase  =				torch.tensor([-0.0803, -0.4454, -0.2375]					).to(lowerCAmelCase					)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]						,						lowerCAmelCase						,						atol=1e-4					)					)





