

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 						UpperCamelCase (lowercase_:			List[Any] )					->				Dict:
	A__							:   List[Any]					       =		SwinConfig(image_size=192 )

	if "base" in model_name:
		A__							:   Any					       =		6
		A__							:   List[Any]					       =		128
		A__							:   Tuple					       =		(2, 2, 18, 2)
		A__							:   Optional[int]					       =		(4, 8, 16, 32)
	elif "large" in model_name:
		A__							:   Dict					       =		12
		A__							:   int					       =		192
		A__							:   Dict					       =		(2, 2, 18, 2)
		A__							:   Any					       =		(6, 12, 24, 48)
	else:
		raise ValueError("""Model not supported, only supports base and large variants""" )

	A__							:   Tuple					       =		window_size
	A__							:   int					       =		embed_dim
	A__							:   str					       =		depths
	A__							:   Any					       =		num_heads

	return config

def 						UpperCamelCase (lowercase_:			Tuple )					->				Union[str, Any]:
	if "encoder.mask_token" in name:
		A__							:   Optional[Any]					       =		name.replace("""encoder.mask_token"""   ,				"""embeddings.mask_token""" )
	if "encoder.patch_embed.proj" in name:
		A__							:   Optional[Any]					       =		name.replace("""encoder.patch_embed.proj"""   ,				"""embeddings.patch_embeddings.projection""" )
	if "encoder.patch_embed.norm" in name:
		A__							:   Union[str, Any]					       =		name.replace("""encoder.patch_embed.norm"""   ,				"""embeddings.norm""" )
	if "attn.proj" in name:
		A__							:   Optional[Any]					       =		name.replace("""attn.proj"""   ,				"""attention.output.dense""" )
	if "attn" in name:
		A__							:   List[Any]					       =		name.replace("""attn"""   ,				"""attention.self""" )
	if "norm1" in name:
		A__							:   Tuple					       =		name.replace("""norm1"""   ,				"""layernorm_before""" )
	if "norm2" in name:
		A__							:   List[Any]					       =		name.replace("""norm2"""   ,				"""layernorm_after""" )
	if "mlp.fc1" in name:
		A__							:   Union[str, Any]					       =		name.replace("""mlp.fc1"""   ,				"""intermediate.dense""" )
	if "mlp.fc2" in name:
		A__							:   int					       =		name.replace("""mlp.fc2"""   ,				"""output.dense""" )

	if name == "encoder.norm.weight":
		A__							:   Dict					       =		"""layernorm.weight"""
	if name == "encoder.norm.bias":
		A__							:   Optional[int]					       =		"""layernorm.bias"""

	if "decoder" in name:
		pass
	else:
		A__							:   str					       =		"""swin.""" + name

	return name

def 						UpperCamelCase (lowercase_:			Optional[Any]   ,				lowercase_:			int )					->				Optional[Any]:
	for key in orig_state_dict.copy().keys():
		A__							:   int					       =		orig_state_dict.pop(lowercase_ )

		if "attn_mask" in key:
			pass
		elif "qkv" in key:
			A__							:   Union[str, Any]					       =		key.split(""".""" )
			A__							:   Optional[Any]					       =		int(key_split[2] )
			A__							:   str					       =		int(key_split[4] )
			A__							:   str					       =		model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				A__							:   int					       =		val[:dim, :]
				A__							:   List[str]					       =		val[
				    dim : dim * 2, :
				]
				A__							:   Any					       =		val[-dim:, :]
			else:
				A__							:   Union[str, Any]					       =		val[
				    :dim
				]
				A__							:   Any					       =		val[
				    dim : dim * 2
				]
				A__							:   Union[str, Any]					       =		val[
				    -dim:
				]
		else:
			A__							:   Union[str, Any]					       =		val

	return orig_state_dict

def 						UpperCamelCase (lowercase_:			Optional[int]   ,				lowercase_:			Any   ,				lowercase_:			Optional[int]   ,				lowercase_:			Any )					->				int:
	A__							:   Optional[Any]					       =		torch.load(lowercase_   ,				map_location="""cpu""" )["""model"""]

	A__							:   str					       =		get_swin_config(lowercase_ )
	A__							:   Union[str, Any]					       =		SwinForMaskedImageModeling(lowercase_ )
	model.eval()

	A__							:   Any					       =		convert_state_dict(lowercase_   ,				lowercase_ )
	model.load_state_dict(lowercase_ )

	A__							:   str					       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""

	A__							:   Optional[Any]					       =		ViTImageProcessor(size={"""height""": 192, """width""": 192} )
	A__							:   List[Any]					       =		Image.open(requests.get(lowercase_   ,				stream=lowercase_ ).raw )
	A__							:   List[str]					       =		image_processor(images=lowercase_   ,				return_tensors="""pt""" )

	with torch.no_grad():
		A__							:   Union[str, Any]					       =		model(**lowercase_ ).logits

	print(outputs.keys() )
	print("""Looks ok!""" )

	if pytorch_dump_folder_path is not None:
		print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
		model.save_pretrained(lowercase_ )

		print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
		image_processor.save_pretrained(lowercase_ )

	if push_to_hub:
		print(f"""Pushing model and image processor for {model_name} to hub""" )
		model.push_to_hub(f"""microsoft/{model_name}""" )
		image_processor.push_to_hub(f"""microsoft/{model_name}""" )


if __name__ == "__main__":
	A_	:			Optional[int]  	=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='swin-base-simmim-window6-192',
	    type=str,
	    choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
	    help='Name of the Swin SimMIM model you\'d like to convert.',
	)
	parser.add_argument(
	    '--checkpoint_path',
	    default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
	    type=str,
	    help='Path to the original PyTorch checkpoint (.pth file).',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	A_	:			Optional[int]  	=      parser.parse_args()
	convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






import requests

A_	:			List[Any]  	=      'YOUR API KEY'

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str = giphy_api_key )					->				list:
	A__							:   Dict					       =		"""+""".join(query.split() )
	A__							:   Optional[int]					       =		f"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
	A__							:   Any					       =		requests.get(lowercase_ ).json()["""data"""]
	return [gif["url"] for gif in gifs]


if __name__ == "__main__":
	print('\n'.join(get_gifs('space ship')))



