

"""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   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	SwinvaConfig()
	lowerCAmelCase__	:						Optional[Any]  				=	swinva_name.split('''_''' )

	lowerCAmelCase__	:						int  				=	name_split[1]
	if "to" in name_split[3]:
		lowerCAmelCase__	:						str  				=	int(name_split[3][-3:] )
	else:
		lowerCAmelCase__	:						Any  				=	int(name_split[3] )
	if "to" in name_split[2]:
		lowerCAmelCase__	:						Tuple  				=	int(name_split[2][-2:] )
	else:
		lowerCAmelCase__	:						Dict  				=	int(name_split[2][6:] )

	if model_size == "tiny":
		lowerCAmelCase__	:						int  				=	96
		lowerCAmelCase__	:						Dict  				=	(2, 2, 6, 2)
		lowerCAmelCase__	:						Union[str, Any]  				=	(3, 6, 12, 24)
	elif model_size == "small":
		lowerCAmelCase__	:						Union[str, Any]  				=	96
		lowerCAmelCase__	:						Optional[int]  				=	(2, 2, 18, 2)
		lowerCAmelCase__	:						int  				=	(3, 6, 12, 24)
	elif model_size == "base":
		lowerCAmelCase__	:						Optional[Any]  				=	1_28
		lowerCAmelCase__	:						Optional[Any]  				=	(2, 2, 18, 2)
		lowerCAmelCase__	:						Any  				=	(4, 8, 16, 32)
	else:
		lowerCAmelCase__	:						Optional[int]  				=	1_92
		lowerCAmelCase__	:						Union[str, Any]  				=	(2, 2, 18, 2)
		lowerCAmelCase__	:						Optional[Any]  				=	(6, 12, 24, 48)

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

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		lowerCAmelCase__	:						List[str]  				=	2_18_41
		lowerCAmelCase__	:						List[str]  				=	'''huggingface/label-files'''
		lowerCAmelCase__	:						Any  				=	'''imagenet-22k-id2label.json'''
		lowerCAmelCase__	:						Optional[int]  				=	json.load(open(hf_hub_download(__a						,     __a						,     repo_type='''dataset''' )						,     '''r''' ) )
		lowerCAmelCase__	:						int  				=	{int(__a ): v for k, v in idalabel.items()}
		lowerCAmelCase__	:						str  				=	idalabel
		lowerCAmelCase__	:						Tuple  				=	{v: k for k, v in idalabel.items()}

	else:
		lowerCAmelCase__	:						Any  				=	10_00
		lowerCAmelCase__	:						Tuple  				=	'''huggingface/label-files'''
		lowerCAmelCase__	:						Any  				=	'''imagenet-1k-id2label.json'''
		lowerCAmelCase__	:						List[Any]  				=	json.load(open(hf_hub_download(__a						,     __a						,     repo_type='''dataset''' )						,     '''r''' ) )
		lowerCAmelCase__	:						List[str]  				=	{int(__a ): v for k, v in idalabel.items()}
		lowerCAmelCase__	:						List[str]  				=	idalabel
		lowerCAmelCase__	:						Union[str, Any]  				=	{v: k for k, v in idalabel.items()}

	lowerCAmelCase__	:						Dict  				=	img_size
	lowerCAmelCase__	:						Union[str, Any]  				=	num_classes
	lowerCAmelCase__	:						Union[str, Any]  				=	embed_dim
	lowerCAmelCase__	:						str  				=	depths
	lowerCAmelCase__	:						Optional[Any]  				=	num_heads
	lowerCAmelCase__	:						str  				=	window_size

	return config

def   __SCREAMING_SNAKE_CASE  (      A_ ):






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

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

	return name

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):






	for key in orig_state_dict.copy().keys():
		lowerCAmelCase__	:						Optional[Any]  				=	orig_state_dict.pop(__a )

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

			if "weight" in key:
				lowerCAmelCase__	:						Optional[Any]  				=	val[:dim, :]
				lowerCAmelCase__	:						List[str]  				=	val[dim : dim * 2, :]
				lowerCAmelCase__	:						Dict  				=	val[-dim:, :]
			else:
				lowerCAmelCase__	:						Any  				=	val[:dim]
				lowerCAmelCase__	:						List[str]  				=	val[
				    dim : dim * 2
				]
				lowerCAmelCase__	:						List[str]  				=	val[-dim:]
		else:
			lowerCAmelCase__	:						Dict  				=	val

	return orig_state_dict

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	timm.create_model(__a						,     pretrained=__a )
	timm_model.eval()

	lowerCAmelCase__	:						int  				=	get_swinva_config(__a )
	lowerCAmelCase__	:						Any  				=	SwinvaForImageClassification(__a )
	model.eval()

	lowerCAmelCase__	:						Any  				=	convert_state_dict(timm_model.state_dict()						,     __a )
	model.load_state_dict(__a )

	lowerCAmelCase__	:						Optional[int]  				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

	lowerCAmelCase__	:						List[Any]  				=	AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''						,     '''-''' ) ) )
	lowerCAmelCase__	:						Tuple  				=	Image.open(requests.get(__a						,     stream=__a ).raw )
	lowerCAmelCase__	:						int  				=	image_processor(images=__a						,     return_tensors='''pt''' )

	lowerCAmelCase__	:						Union[str, Any]  				=	timm_model(inputs['''pixel_values'''] )
	lowerCAmelCase__	:						Tuple  				=	model(**__a ).logits

	assert torch.allclose(__a						,     __a						,     atol=1e-3 )

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

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

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


if __name__ == "__main__":
							__UpperCamelCase  :      int							      =		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.'''
							)

							__UpperCamelCase  :      int							      =		parser.parse_args()
							convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)












"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


UpperCAmelCase_   : Any   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def __init__(						self     :  Union[str, Any]						,	*lowercase_     :  List[str]						,	**lowercase_     :  List[str]):




          '''simple docstring'''


          warnings.warn(
              '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
              ''' Please use SegformerImageProcessor instead.'''						,	lowercase_						,	)
          super().__init__(*lowercase_						,	**lowercase_)


