





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def lowerCAmelCase__(__snake_case							)				->      Optional[int]:







	'''simple docstring'''







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

	return name





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Optional[Any]:







	'''simple docstring'''







	for key in orig_state_dict.copy().keys():
		lowerCamelCase__								=		orig_state_dict.pop(__snake_case							)

		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
			lowerCamelCase__								=		key.split('''.'''							)
			lowerCamelCase__				,	lowerCamelCase__								=		int(key_split[2]							), int(key_split[4]							)
			lowerCamelCase__								=		config.vision_config.hidden_size
			if "weight" in key:
				lowerCamelCase__								=		val[:dim, :]
				lowerCamelCase__								=		val[dim : dim * 2, :]
				lowerCamelCase__								=		val[-dim:, :]
			else:
				lowerCamelCase__								=		val[:dim]
				lowerCamelCase__								=		val[dim : dim * 2]
				lowerCamelCase__								=		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
			lowerCamelCase__								=		key.split('''.'''							)
			lowerCamelCase__								=		int(key_split[3]							)
			lowerCamelCase__								=		config.text_config.hidden_size
			if "weight" in key:
				lowerCamelCase__								=		val[:dim, :]
				lowerCamelCase__								=		val[
				    dim : dim * 2, :
				]
				lowerCamelCase__								=		val[-dim:, :]
			else:
				lowerCamelCase__								=		val[:dim]
				lowerCamelCase__								=		val[dim : dim * 2]
				lowerCamelCase__								=		val[-dim:]
		else:
			lowerCamelCase__								=		rename_key(__snake_case							)
			# 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
			):
				lowerCamelCase__								=		val.squeeze_()
			else:
				lowerCamelCase__								=		val

	return orig_state_dict





def lowerCAmelCase__()				->      Any:







	'''simple docstring'''







	lowerCamelCase__								=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCamelCase__								=		Image.open(requests.get(__snake_case       ,stream=__snake_case							).raw							)
	return im





@torch.no_grad()
def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case="groupvit-gcc-yfcc"       ,__snake_case=False							)				->      Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__								=		GroupViTConfig()
	lowerCamelCase__								=		GroupViTModel(__snake_case							).eval()

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

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

	with torch.no_grad():
		lowerCamelCase__								=		model(**__snake_case							)

	if model_name == "groupvit-gcc-yfcc":
		lowerCamelCase__								=		torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]							)
	elif model_name == "groupvit-gcc-redcaps":
		lowerCamelCase__								=		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       ,__snake_case       ,atol=1E-3							)

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

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


if __name__ == "__main__":
			_a 						=      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`.",
			)
			_a 						=      parser.parse_args()

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







from __future__ import annotations
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      0
		lowercase__            =      len(SCREAMING_SNAKE_CASE     ) - 1

		while i < j:
				if nums[i] + nums[j] == target:
						return [i, j]
				elif nums[i] + nums[j] < target:
						lowercase__            =      i + 1
				else:
						lowercase__            =      j - 1

		return []


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(f"""{two_pointer([2, 7, 11, 15], 9) = }""")


