
'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def      UpperCamelCase_				(      A__						):
	a_      				=						SwinConfig(image_size=1_92						)

	if "base" in model_name:
		a_      				=						6
		a_      				=						1_28
		a_      				=						(2, 2, 18, 2)
		a_      				=						(4, 8, 16, 32)
	elif "large" in model_name:
		a_      				=						12
		a_      				=						1_92
		a_      				=						(2, 2, 18, 2)
		a_      				=						(6, 12, 24, 48)
	else:
		raise ValueError("""Model not supported, only supports base and large variants"""						)

	a_      				=						window_size
	a_      				=						embed_dim
	a_      				=						depths
	a_      				=						num_heads

	return config






def      UpperCamelCase_				(      A__						):





	if "encoder.mask_token" in name:
		a_      				=						name.replace("""encoder.mask_token"""  ,      """embeddings.mask_token"""						)
	if "encoder.patch_embed.proj" in name:
		a_      				=						name.replace("""encoder.patch_embed.proj"""  ,      """embeddings.patch_embeddings.projection"""						)
	if "encoder.patch_embed.norm" in name:
		a_      				=						name.replace("""encoder.patch_embed.norm"""  ,      """embeddings.norm"""						)
	if "attn.proj" in name:
		a_      				=						name.replace("""attn.proj"""  ,      """attention.output.dense"""						)
	if "attn" in name:
		a_      				=						name.replace("""attn"""  ,      """attention.self"""						)
	if "norm1" in name:
		a_      				=						name.replace("""norm1"""  ,      """layernorm_before"""						)
	if "norm2" in name:
		a_      				=						name.replace("""norm2"""  ,      """layernorm_after"""						)
	if "mlp.fc1" in name:
		a_      				=						name.replace("""mlp.fc1"""  ,      """intermediate.dense"""						)
	if "mlp.fc2" in name:
		a_      				=						name.replace("""mlp.fc2"""  ,      """output.dense"""						)

	if name == "encoder.norm.weight":
		a_      				=						'''layernorm.weight'''
	if name == "encoder.norm.bias":
		a_      				=						'''layernorm.bias'''

	if "decoder" in name:
		pass
	else:
		a_      				=						'''swin.''' + name

	return name






def      UpperCamelCase_				(      A__  ,      A__						):





	for key in orig_state_dict.copy().keys():
		a_      				=						orig_state_dict.pop(UpperCamelCase__						)

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

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

	return orig_state_dict






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__						):
	a_      				=						torch.load(UpperCamelCase__  ,      map_location="""cpu"""						)['''model''']

	a_      				=						get_swin_config(UpperCamelCase__						)
	a_      				=						SwinForMaskedImageModeling(UpperCamelCase__						)
	model.eval()

	a_      				=						convert_state_dict(UpperCamelCase__  ,      UpperCamelCase__						)
	model.load_state_dict(UpperCamelCase__						)

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

	a_      				=						ViTImageProcessor(size={"""height""": 1_92, """width""": 1_92}						)
	a_      				=						Image.open(requests.get(UpperCamelCase__  ,      stream=UpperCamelCase__						).raw						)
	a_      				=						image_processor(images=UpperCamelCase__  ,      return_tensors="""pt"""						)

	with torch.no_grad():
		a_      				=						model(**UpperCamelCase__						).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(UpperCamelCase__						)

		print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
		image_processor.save_pretrained(UpperCamelCase__						)

	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__":
		lowercase__											=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.'
		)

		lowercase__											=parser.parse_args()
		convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Dict					)      ->    List[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   [0 for i in range(r + 1					)]
						# nc0 = 1
						__SCREAMING_SNAKE_CASE:				Dict           =   1
						for i in range(1       ,			n + 1					):
												# to compute current row from previous row.
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   min(UpperCamelCase__       ,			UpperCamelCase__					)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]




print(binomial_coefficient(n=10, r=5))



