
"""simple docstring"""

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def 							A						(    snake_case				:str		)				->     List[Any]:


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

	return name






def 							A						(    snake_case				:Any    , snake_case				:List[str]		)				->     Any:


	for key in orig_state_dict.copy().keys():
		__UpperCamelCase      =	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
			__UpperCamelCase      =	key.split('.'		)
			__UpperCamelCase  ,     __UpperCamelCase      =	int(key_split[2]		), int(key_split[4]		)
			__UpperCamelCase      =	config.vision_config.hidden_size
			if "weight" in key:
				__UpperCamelCase      =	val[:dim, :]
				__UpperCamelCase      =	val[dim : dim * 2, :]
				__UpperCamelCase      =	val[-dim:, :]
			else:
				__UpperCamelCase      =	val[:dim]
				__UpperCamelCase      =	val[dim : dim * 2]
				__UpperCamelCase      =	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
			__UpperCamelCase      =	key.split('.'		)
			__UpperCamelCase      =	int(key_split[3]		)
			__UpperCamelCase      =	config.text_config.hidden_size
			if "weight" in key:
				__UpperCamelCase      =	val[:dim, :]
				__UpperCamelCase      =	val[
				    dim : dim * 2, :
				]
				__UpperCamelCase      =	val[-dim:, :]
			else:
				__UpperCamelCase      =	val[:dim]
				__UpperCamelCase      =	val[dim : dim * 2]
				__UpperCamelCase      =	val[-dim:]
		else:
			__UpperCamelCase      =	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
			):
				__UpperCamelCase      =	val.squeeze_()
			else:
				__UpperCamelCase      =	val

	return orig_state_dict






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






@torch.no_grad()
def 							A						(    snake_case				:Union[str, Any]    , snake_case				:Optional[Any]    , snake_case				:List[str]="groupvit-gcc-yfcc"    , snake_case				:Tuple=False		)				->     Optional[Any]:
	__UpperCamelCase      =	GroupViTConfig()
	__UpperCamelCase      =	GroupViTModel(__A		).eval()

	__UpperCamelCase      =	torch.load(__A    , map_location='cpu'		)['model']
	__UpperCamelCase      =	convert_state_dict(__A    , __A		)
	__UpperCamelCase  ,     __UpperCamelCase      =	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
	__UpperCamelCase      =	CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'		)
	__UpperCamelCase      =	prepare_img()
	__UpperCamelCase      =	processor(text=['a photo of a cat', 'a photo of a dog']    , images=__A    , padding=__A    , return_tensors='pt'		)

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

	if model_name == "groupvit-gcc-yfcc":
		__UpperCamelCase      =	torch.tensor([[13.3_523, 6.3_629]]		)
	elif model_name == "groupvit-gcc-redcaps":
		__UpperCamelCase      =	torch.tensor([[16.1_873, 8.6_230]]		)
	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__":
						UpperCamelCase     :    Any       =			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`.",
						)
						UpperCamelCase     :    Optional[int]       =			parser.parse_args()

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





"""simple docstring"""

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	ArgumentParser('Transformers CLI tool'    , usage='transformers-cli <command> [<args>]'		)
 __UpperCamelCase      =	parser.add_subparsers(help='transformers-cli command helpers'		)

 # Register commands
 ConvertCommand.register_subcommand(snake_case		)
 DownloadCommand.register_subcommand(snake_case		)
 EnvironmentCommand.register_subcommand(snake_case		)
 RunCommand.register_subcommand(snake_case		)
 ServeCommand.register_subcommand(snake_case		)
 UserCommands.register_subcommand(snake_case		)
 AddNewModelCommand.register_subcommand(snake_case		)
 AddNewModelLikeCommand.register_subcommand(snake_case		)
 LfsCommands.register_subcommand(snake_case		)
 PTtoTFCommand.register_subcommand(snake_case		)

 # Let's go
 __UpperCamelCase      =	parser.parse_args()

 if not hasattr(snake_case    , 'func'		):
  parser.print_help()
  exit(1		)

 # Run
 __UpperCamelCase      =	args.func(snake_case		)
 service.run()


if __name__ == "__main__":
      main()



