





'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Union[str, Any]:
	snake_case__	       =   SwinvaConfig()
	snake_case__	       =   swinva_name.split('_'							)

	snake_case__	       =   name_split[1]
	if "to" in name_split[3]:
		snake_case__	       =   int(name_split[3][-3:]							)
	else:
		snake_case__	       =   int(name_split[3]							)
	if "to" in name_split[2]:
		snake_case__	       =   int(name_split[2][-2:]							)
	else:
		snake_case__	       =   int(name_split[2][6:]							)

	if model_size == "tiny":
		snake_case__	       =   96
		snake_case__	       =   (2, 2, 6, 2)
		snake_case__	       =   (3, 6, 12, 24)
	elif model_size == "small":
		snake_case__	       =   96
		snake_case__	       =   (2, 2, 18, 2)
		snake_case__	       =   (3, 6, 12, 24)
	elif model_size == "base":
		snake_case__	       =   1_28
		snake_case__	       =   (2, 2, 18, 2)
		snake_case__	       =   (4, 8, 16, 32)
	else:
		snake_case__	       =   1_92
		snake_case__	       =   (2, 2, 18, 2)
		snake_case__	       =   (6, 12, 24, 48)

	if "to" in swinva_name:
		snake_case__	       =   (12, 12, 12, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		snake_case__	       =   2_18_41
		snake_case__	       =   'huggingface/label-files'
		snake_case__	       =   'imagenet-22k-id2label.json'
		snake_case__	       =   json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type='dataset'							)					,				'r'							)							)
		snake_case__	       =   {int(UpperCAmelCase_							): v for k, v in idalabel.items()}
		snake_case__	       =   idalabel
		snake_case__	       =   {v: k for k, v in idalabel.items()}

	else:
		snake_case__	       =   10_00
		snake_case__	       =   'huggingface/label-files'
		snake_case__	       =   'imagenet-1k-id2label.json'
		snake_case__	       =   json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type='dataset'							)					,				'r'							)							)
		snake_case__	       =   {int(UpperCAmelCase_							): v for k, v in idalabel.items()}
		snake_case__	       =   idalabel
		snake_case__	       =   {v: k for k, v in idalabel.items()}

	snake_case__	       =   img_size
	snake_case__	       =   num_classes
	snake_case__	       =   embed_dim
	snake_case__	       =   depths
	snake_case__	       =   num_heads
	snake_case__	       =   window_size

	return config




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Union[str, Any]:
	if "patch_embed.proj" in name:
		snake_case__	       =   name.replace('patch_embed.proj'					,				'embeddings.patch_embeddings.projection'							)
	if "patch_embed.norm" in name:
		snake_case__	       =   name.replace('patch_embed.norm'					,				'embeddings.norm'							)
	if "layers" in name:
		snake_case__	       =   'encoder.' + name
	if "attn.proj" in name:
		snake_case__	       =   name.replace('attn.proj'					,				'attention.output.dense'							)
	if "attn" in name:
		snake_case__	       =   name.replace('attn'					,				'attention.self'							)
	if "norm1" in name:
		snake_case__	       =   name.replace('norm1'					,				'layernorm_before'							)
	if "norm2" in name:
		snake_case__	       =   name.replace('norm2'					,				'layernorm_after'							)
	if "mlp.fc1" in name:
		snake_case__	       =   name.replace('mlp.fc1'					,				'intermediate.dense'							)
	if "mlp.fc2" in name:
		snake_case__	       =   name.replace('mlp.fc2'					,				'output.dense'							)
	if "q_bias" in name:
		snake_case__	       =   name.replace('q_bias'					,				'query.bias'							)
	if "k_bias" in name:
		snake_case__	       =   name.replace('k_bias'					,				'key.bias'							)
	if "v_bias" in name:
		snake_case__	       =   name.replace('v_bias'					,				'value.bias'							)
	if "cpb_mlp" in name:
		snake_case__	       =   name.replace('cpb_mlp'					,				'continuous_position_bias_mlp'							)
	if name == "norm.weight":
		snake_case__	       =   'layernorm.weight'
	if name == "norm.bias":
		snake_case__	       =   'layernorm.bias'

	if "head" in name:
		snake_case__	       =   name.replace('head'					,				'classifier'							)
	else:
		snake_case__	       =   'swinv2.' + name

	return name




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	for key in orig_state_dict.copy().keys():
		snake_case__	       =   orig_state_dict.pop(UpperCAmelCase_							)

		if "mask" in key:
			continue
		elif "qkv" in key:
			snake_case__	       =   key.split('.'							)
			snake_case__	       =   int(key_split[1]							)
			snake_case__	       =   int(key_split[3]							)
			snake_case__	       =   model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				snake_case__	       =   val[:dim, :]
				snake_case__	       =   val[dim : dim * 2, :]
				snake_case__	       =   val[-dim:, :]
			else:
				snake_case__	       =   val[:dim]
				snake_case__	       =   val[
				    dim : dim * 2
				]
				snake_case__	       =   val[-dim:]
		else:
			snake_case__	       =   val

	return orig_state_dict




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	snake_case__	       =   timm.create_model(UpperCAmelCase_					,				pretrained=UpperCAmelCase_							)
	timm_model.eval()

	snake_case__	       =   get_swinva_config(UpperCAmelCase_							)
	snake_case__	       =   SwinvaForImageClassification(UpperCAmelCase_							)
	model.eval()

	snake_case__	       =   convert_state_dict(timm_model.state_dict()					,				UpperCAmelCase_							)
	model.load_state_dict(UpperCAmelCase_							)

	snake_case__	       =   'http://images.cocodataset.org/val2017/000000039769.jpg'

	snake_case__	       =   AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'					,				'-'							)							)							)
	snake_case__	       =   Image.open(requests.get(UpperCAmelCase_					,				stream=UpperCAmelCase_							).raw							)
	snake_case__	       =   image_processor(images=UpperCAmelCase_					,				return_tensors='pt'							)

	snake_case__	       =   timm_model(inputs['pixel_values']							)
	snake_case__	       =   model(**UpperCAmelCase_							).logits

	assert torch.allclose(UpperCAmelCase_					,				UpperCAmelCase_					,				atol=1E-3							)

	print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''							)
	model.save_pretrained(UpperCAmelCase_							)

	print(f'''Saving image processor to {pytorch_dump_folder_path}'''							)
	image_processor.save_pretrained(UpperCAmelCase_							)

	model.push_to_hub(
	    repo_path_or_name=Path(UpperCAmelCase_					,				UpperCAmelCase_							)					,				organization='nandwalritik'					,				commit_message='Add model'					,				)


if __name__ == "__main__":
			a__					:			List[str]          =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--swinv2_name''',
			    default='''swinv2_tiny_patch4_window8_256''',
			    type=str,
			    help='''Name of the Swinv2 timm model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)

			a__					:			Optional[Any]          =     parser.parse_args()
			convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)










'''simple docstring'''




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


a__					:			Optional[int]          =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=False					,				)   ->Optional[int]:
	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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = False							)   ->Union[str, Any]:
	snake_case__	       =   torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		snake_case__	       =   'cuda'
	elif fpaa and not torch.cuda.is_available():
		raise ValueError('`float16` model export is only supported on GPUs with CUDA'							)
	else:
		snake_case__	       =   'cpu'
	snake_case__	       =   Path(UpperCAmelCase_							)

	# VAE DECODER
	snake_case__	       =   AutoencoderKL.from_pretrained(model_path + '/vae'							)
	snake_case__	       =   vae_decoder.config.latent_channels
	# forward only through the decoder part
	snake_case__	       =   vae_decoder.decode
	onnx_export(
	    UpperCAmelCase_					,				model_args=(
	        torch.randn(1					,				UpperCAmelCase_					,				25					,				25							).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 vae_decoder


if __name__ == "__main__":
			a__					:			Optional[int]          =     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''')

			a__					:			List[str]          =     parser.parse_args()
			print(args.output_path)
			convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
			print('''SD: Done: ONNX''')



