
'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[Any]:
	# vision encoder
	if "img_encoder.pos_embed" in name:
		_snake_case   						=		name.replace('img_encoder.pos_embed'			,				'vision_model.embeddings.position_embeddings'	)
	if "img_encoder.patch_embed.proj" in name:
		_snake_case   						=		name.replace('img_encoder.patch_embed.proj'			,				'vision_model.embeddings.patch_embeddings.projection'	)
	if "img_encoder.patch_embed.norm" in name:
		_snake_case   						=		name.replace('img_encoder.patch_embed.norm'			,				'vision_model.embeddings.layernorm'	)
	if "img_encoder.layers" in name:
		_snake_case   						=		name.replace('img_encoder.layers'			,				'vision_model.encoder.stages'	)
	if "blocks" in name and "res" not in name:
		_snake_case   						=		name.replace('blocks'			,				'layers'	)
	if "attn" in name and "pre_assign" not in name:
		_snake_case   						=		name.replace('attn'			,				'self_attn'	)
	if "proj" in name and "self_attn" in name and "text" not in name:
		_snake_case   						=		name.replace('proj'			,				'out_proj'	)
	if "pre_assign_attn.attn.proj" in name:
		_snake_case   						=		name.replace('pre_assign_attn.attn.proj'			,				'pre_assign_attn.attn.out_proj'	)
	if "norm1" in name:
		_snake_case   						=		name.replace('norm1'			,				'layer_norm1'	)
	if "norm2" in name and "pre_assign" not in name:
		_snake_case   						=		name.replace('norm2'			,				'layer_norm2'	)
	if "img_encoder.norm" in name:
		_snake_case   						=		name.replace('img_encoder.norm'			,				'vision_model.layernorm'	)
	# text encoder
	if "text_encoder.token_embedding" in name:
		_snake_case   						=		name.replace('text_encoder.token_embedding'			,				'text_model.embeddings.token_embedding'	)
	if "text_encoder.positional_embedding" in name:
		_snake_case   						=		name.replace('text_encoder.positional_embedding'			,				'text_model.embeddings.position_embedding.weight'	)
	if "text_encoder.transformer.resblocks." in name:
		_snake_case   						=		name.replace('text_encoder.transformer.resblocks.'			,				'text_model.encoder.layers.'	)
	if "ln_1" in name:
		_snake_case   						=		name.replace('ln_1'			,				'layer_norm1'	)
	if "ln_2" in name:
		_snake_case   						=		name.replace('ln_2'			,				'layer_norm2'	)
	if "c_fc" in name:
		_snake_case   						=		name.replace('c_fc'			,				'fc1'	)
	if "c_proj" in name:
		_snake_case   						=		name.replace('c_proj'			,				'fc2'	)
	if "text_encoder" in name:
		_snake_case   						=		name.replace('text_encoder'			,				'text_model'	)
	if "ln_final" in name:
		_snake_case   						=		name.replace('ln_final'			,				'final_layer_norm'	)
	# projection layers
	if "img_projector.linear_hidden." in name:
		_snake_case   						=		name.replace('img_projector.linear_hidden.'			,				'visual_projection.'	)
	if "img_projector.linear_out." in name:
		_snake_case   						=		name.replace('img_projector.linear_out.'			,				'visual_projection.3.'	)
	if "text_projector.linear_hidden" in name:
		_snake_case   						=		name.replace('text_projector.linear_hidden'			,				'text_projection'	)
	if "text_projector.linear_out" in name:
		_snake_case   						=		name.replace('text_projector.linear_out'			,				'text_projection.3'	)

	return name







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	for key in orig_state_dict.copy().keys():
		_snake_case   						=		orig_state_dict.pop(__A	)

		if "qkv" in key:
			# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
			# we need to split them up into separate matrices/vectors
			_snake_case   						=		key.split('.'	)
			_snake_case						, _snake_case   						=		int(key_split[2]	), int(key_split[4]	)
			_snake_case   						=		config.vision_config.hidden_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:]
		elif "in_proj" in key:
			# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
			# we need to split them up into separate matrices/vectors
			_snake_case   						=		key.split('.'	)
			_snake_case   						=		int(key_split[3]	)
			_snake_case   						=		config.text_config.hidden_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   						=		rename_key(__A	)
			# squeeze if necessary
			if (
			    "text_projection.0" in new_name
			    or "text_projection.3" in new_name
			    or "visual_projection.0" in new_name
			    or "visual_projection.3" in new_name
			):
				_snake_case   						=		val.squeeze_()
			else:
				_snake_case   						=		val

	return orig_state_dict







def 				SCREAMING_SNAKE_CASE__	(   )       ->	int:
	_snake_case   						=		'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case   						=		Image.open(requests.get(__A			,				stream=__A	).raw	)
	return im







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A="groupvit-gcc-yfcc"			,				__A=False	)       ->	Dict:
	_snake_case   						=		GroupViTConfig()
	_snake_case   						=		GroupViTModel(__A	).eval()

	_snake_case   						=		torch.load(__A			,				map_location='cpu'	)['model']
	_snake_case   						=		convert_state_dict(__A			,				__A	)
	_snake_case						, _snake_case   						=		model.load_state_dict(__A			,				strict=__A	)
	assert missing_keys == ["text_model.embeddings.position_ids"]
	assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__A	) == 0)

	# verify result
	_snake_case   						=		CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'	)
	_snake_case   						=		prepare_img()
	_snake_case   						=		processor(text=['a photo of a cat', 'a photo of a dog']			,				images=__A			,				padding=__A			,				return_tensors='pt'	)

	with torch.no_grad():
		_snake_case   						=		model(**__A	)

	if model_name == "groupvit-gcc-yfcc":
		_snake_case   						=		torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]	)
	elif model_name == "groupvit-gcc-redcaps":
		_snake_case   						=		torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]	)
	else:
		raise ValueError(F'Model name {model_name} not supported.'	)
	assert torch.allclose(outputs.logits_per_image			,				__A			,				atol=1e-3	)

	processor.save_pretrained(__A	)
	model.save_pretrained(__A	)
	print('Successfully saved processor and model to'			,				__A	)

	if push_to_hub:
		print('Pushing to the hub...'	)
		processor.push_to_hub(__A			,				organization='nielsr'	)
		model.push_to_hub(__A			,				organization='nielsr'	)


if __name__ == "__main__":
					lowercase    :							Tuple      =					argparse.ArgumentParser()
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
					)
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
					parser.add_argument(
					    "--model_name",
					    default="groupvit-gccy-fcc",
					    type=str,
					    help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
					)
					parser.add_argument(
					    "--push_to_hub",
					    action="store_true",
					    help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
					)
					lowercase    :							int      =					parser.parse_args()

					convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




import os
def      a      (			)    ->      Any:





      """simple docstring"""



      with open(os.path.dirname(A__     ) + '/p022_names.txt'     ) as file:
            _lowercase	       =str(file.readlines()[0]     )
            _lowercase	       =names.replace('"'      ,   ''     ).split(','     )

      names.sort()

      _lowercase	       =0
      _lowercase	       =0

      for i, name in enumerate(A__     ):
            for letter in name:
                  name_score += ord(A__     ) - 64

            total_score += (i + 1) * name_score
            _lowercase	       =0
      return total_score


if __name__ == "__main__":
  print(solution())


