






'''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  :       Dict  )    -> List[Any]:







			"""simple docstring"""
			__snake_case				:      List[Any]     							= SwinvaConfig()
			__snake_case				:      List[Any]     							= swinva_name.split('_'  )

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

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

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

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

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

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

			return config




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> Optional[Any]:







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

			if "head" in name:
						__snake_case				:      List[str]     							= name.replace('head'  ,		'classifier'  )
			else:
						__snake_case				:      Tuple     							= 'swinv2.' + name

			return name




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  ,		A  :       Union[str, Any]  )    -> List[str]:







			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						__snake_case				:      int     							= orig_state_dict.pop(A  )

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

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

			return orig_state_dict




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       Optional[Any]  )    -> Any:







			"""simple docstring"""
			__snake_case				:      int     							= timm.create_model(A  ,		pretrained=A  )
			timm_model.eval()

			__snake_case				:      Any     							= get_swinva_config(A  )
			__snake_case				:      List[Any]     							= SwinvaForImageClassification(A  )
			model.eval()

			__snake_case				:      List[str]     							= convert_state_dict(timm_model.state_dict()  ,		A  )
			model.load_state_dict(A  )

			__snake_case				:      Optional[Any]     							= 'http://images.cocodataset.org/val2017/000000039769.jpg'

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

			__snake_case				:      Optional[Any]     							= timm_model(inputs['pixel_values']  )
			__snake_case				:      str     							= 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__":
						__A    	=   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    	=   parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







'''simple docstring'''






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__A    	=   abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  )    -> str:







   """simple docstring"""
   from diffusers.utils.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(A  )





def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Optional[int]:







   """simple docstring"""
   from diffusers.utils.testing_utils import pytest_terminal_summary_main

   __snake_case				:      Any     							= terminalreporter.config.getoption('--make-reports'  )
   if make_reports:
      pytest_terminal_summary_main(A  ,		id=A  )