





"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def __A						(   a_ :							List[str]     )->							Tuple:




		'''simple docstring'''






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

		return name





def __A						(   a_ :							Any		,		a_ :							List[str]     )->							Any:




		'''simple docstring'''






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

		return orig_state_dict





def __A						(   )->							List[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE       :     Any				= Image.open(requests.get(a_		,		stream=a_     ).raw     )
		return im





@torch.no_grad()
def __A						(   a_ :							List[Any]		,		a_ :							Any		,		a_ :							Any="groupvit-gcc-yfcc"		,		a_ :							Optional[int]=False     )->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= GroupViTConfig()
		SCREAMING_SNAKE_CASE       :     Optional[int]				= GroupViTModel(a_     ).eval()

		SCREAMING_SNAKE_CASE       :     str				= torch.load(a_		,		map_location='''cpu'''     )['''model''']
		SCREAMING_SNAKE_CASE       :     str				= convert_state_dict(a_		,		a_     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= 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
		SCREAMING_SNAKE_CASE       :     Tuple				= CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''     )
		SCREAMING_SNAKE_CASE       :     List[str]				= prepare_img()
		SCREAMING_SNAKE_CASE       :     List[str]				= processor(text=['''a photo of a cat''', '''a photo of a dog''']		,		images=a_		,		padding=a_		,		return_tensors='''pt'''     )

		with torch.no_grad():
				SCREAMING_SNAKE_CASE       :     str				= model(**a_     )

		if model_name == "groupvit-gcc-yfcc":
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.tensor([[13.3523, 6.3629]]     )
		elif model_name == "groupvit-gcc-redcaps":
				SCREAMING_SNAKE_CASE       :     List[Any]				= torch.tensor([[16.1873, 8.6230]]     )
		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__":
					lowerCamelCase__ :       str						     =			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`.",
					)
					lowerCamelCase__ :       Union[str, Any]						     =			parser.parse_args()

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







"""simple docstring"""






import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__ :       List[str]						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Optional[int]						     =			{"vocab_file": "vocab.json"}

lowerCamelCase__ :       Dict						     =			{
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCamelCase__ :       Optional[Any]						     =			{"mgp-str": 27}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  VOCAB_FILES_NAMES
	UpperCamelCase				=  PRETRAINED_VOCAB_FILES_MAP
	UpperCamelCase				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(						self    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Optional[int]="[GO]"		, lowerCamelCase_    :int="[GO]"		, lowerCamelCase_    :str="[s]"		, lowerCamelCase_    :Dict="[GO]"		, **lowerCamelCase_    :List[str]					)							->			Tuple:







			'''simple docstring'''




			super().__init__(
			    unk_token=lowerCamelCase_		, bos_token=lowerCamelCase_		, eos_token=lowerCamelCase_		, pad_token=lowerCamelCase_		, **lowerCamelCase_		, )

			with open(lowerCamelCase_		, encoding='''utf-8'''					) as vocab_handle:
					SCREAMING_SNAKE_CASE       :     int				= json.load(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {v: k for k, v in self.vocab.items()}





	@property
	def   __lowerCAmelCase (						self    :int					)							->			Dict:







			'''simple docstring'''




			return len(self.vocab					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			Dict:







			'''simple docstring'''




			return dict(self.vocab		, **self.added_tokens_encoder					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Optional[Any]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
			for s in text:
					char_tokens.extend(lowerCamelCase_					)
			return char_tokens





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Tuple					)							->			Optional[int]:







			'''simple docstring'''




			return self.vocab.get(lowerCamelCase_		, self.vocab.get(self.unk_token					)					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Dict					)							->			Optional[int]:







			'''simple docstring'''




			return self.decoder.get(lowerCamelCase_					)







	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :str		, lowerCamelCase_    :Optional[str] = None					)							->			Tuple[str]:







			'''simple docstring'''




			if not os.path.isdir(lowerCamelCase_					):
					logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_					)					)
					return
			SCREAMING_SNAKE_CASE       :     List[Any]				= os.path.join(
			    lowerCamelCase_		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

			with open(lowerCamelCase_		, '''w'''		, encoding='''utf-8'''					) as f:
					f.write(json.dumps(self.vocab		, indent=2		, sort_keys=lowerCamelCase_		, ensure_ascii=lowerCamelCase_					) + '''\n'''					)

			return (vocab_file,)





