


"""simple docstring"""


import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def  __snake_case       (   )      ->		Optional[int]:







		"""simple docstring"""





		A					       = 'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
		A					       = Image.open(requests.get(UpperCamelCase__ ,			stream=UpperCamelCase__	).raw	).convert('RGB'	)

		return image







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







		"""simple docstring"""





		A					       = []
		# fmt: off

		# vision encoder
		rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')	)
		rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')	)
		rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')	)
		rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')	)
		rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')	)
		rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')	)

		for i in range(config.vision_config.num_hidden_layers	):
				rename_keys.append((f'visual_encoder.blocks.{i}.norm1.weight', f'vision_model.encoder.layers.{i}.layer_norm1.weight')	)
				rename_keys.append((f'visual_encoder.blocks.{i}.norm1.bias', f'vision_model.encoder.layers.{i}.layer_norm1.bias')	)
				rename_keys.append((f'visual_encoder.blocks.{i}.norm2.weight', f'vision_model.encoder.layers.{i}.layer_norm2.weight')	)
				rename_keys.append((f'visual_encoder.blocks.{i}.norm2.bias', f'vision_model.encoder.layers.{i}.layer_norm2.bias')	)
				rename_keys.append((f'visual_encoder.blocks.{i}.attn.qkv.weight', f'vision_model.encoder.layers.{i}.self_attn.qkv.weight')	)
				rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.weight', f'vision_model.encoder.layers.{i}.self_attn.projection.weight',)	)
				rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.bias', f'vision_model.encoder.layers.{i}.self_attn.projection.bias')	)
				rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.weight', f'vision_model.encoder.layers.{i}.mlp.fc1.weight')	)
				rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.bias', f'vision_model.encoder.layers.{i}.mlp.fc1.bias')	)
				rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.weight', f'vision_model.encoder.layers.{i}.mlp.fc2.weight')	)
				rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.bias', f'vision_model.encoder.layers.{i}.mlp.fc2.bias')	)

		# QFormer
		rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')	)
		rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')	)

		# fmt: on
		return rename_keys







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Union[str, Any]:







		"""simple docstring"""





		A					       = dct.pop(UpperCamelCase__	)
		A					       = val







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







		"""simple docstring"""





		for i in range(config.vision_config.num_hidden_layers	):
				# read in original q and v biases
				A					       = state_dict.pop(f'visual_encoder.blocks.{i}.attn.q_bias'	)
				A					       = state_dict.pop(f'visual_encoder.blocks.{i}.attn.v_bias'	)

				# next, set bias in the state dict
				A					       = torch.cat((q_bias, torch.zeros_like(UpperCamelCase__ ,			requires_grad=UpperCamelCase__	), v_bias)	)
				A					       = qkv_bias







def  __snake_case       (   UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		A					       = 364 if 'coco' in model_name else 224
		A					       = InstructBlipVisionConfig(image_size=UpperCamelCase__	).to_dict()

		# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
		# seems like flan-T5 models don't have bos_token_id properly set?
		if "t5-xl" in model_name:
				A					       = TaConfig.from_pretrained('google/flan-t5-xl' ,			dense_act_fn='gelu' ,			bos_token_id=1	).to_dict()
		elif "t5-xxl" in model_name:
				A					       = TaConfig.from_pretrained('google/flan-t5-xxl' ,			dense_act_fn='gelu' ,			bos_token_id=1	).to_dict()
		elif "vicuna-7b" in model_name:
				A					       = LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf' ,			vocab_size=32001	).to_dict()
		elif "vicuna-13b" in model_name:
				A					       = LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf' ,			vocab_size=32001	).to_dict()
		else:
				raise ValueError('Model name not supported'	)

		# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
		A					       = InstructBlipQFormerConfig(vocab_size=30523	).to_dict()
		A					       = InstructBlipConfig(vision_config=UpperCamelCase__ ,			text_config=UpperCamelCase__ ,			qformer_config=UpperCamelCase__	)

		return config, image_size







@torch.no_grad()
def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__=None ,			UpperCamelCase__=False	)      ->		Optional[int]:







		"""simple docstring"""





		A					       = AutoTokenizer.from_pretrained('bert-base-uncased' ,			truncation_side='left'	)
		qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}	)

		if "t5" in model_name:
				A					       = TaTokenizerFast.from_pretrained('google/flan-t5-xl' ,			truncation_side='left'	)
		elif "vicuna" in model_name:
				# the following was used in the original implementation:
				# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
				# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
				# tokenizer.add_special_tokens({"bos_token": "</s>"})
				# tokenizer.add_special_tokens({"eos_token": "</s>"})
				# tokenizer.add_special_tokens({"unk_token": "</s>"})
				A					       = LlamaTokenizerFast.from_pretrained(
				    'huggyllama/llama-7b' ,			truncation_side='left' ,			bos_token='</s>' ,			unk_token='</s>'	)
				tokenizer.add_special_tokens({'pad_token': '[PAD]'}	)

		A       , A					       = get_blipa_config(UpperCamelCase__	)
		A					       = InstructBlipForConditionalGeneration(UpperCamelCase__	).eval()

		A					       = {
		    'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
		    'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
		    'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
		    'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
		}

		A       , A					       = model_name_to_original[model_name]

		# load original model
		print('Loading original model...'	)
		A					       = 'cuda:1' if torch.cuda.is_available() else 'cpu'
		A					       = 'cuda:2' if torch.cuda.is_available() else 'cpu'
		A       , A       , A					       = load_model_and_preprocess(
		    name=UpperCamelCase__ ,			model_type=UpperCamelCase__ ,			is_eval=UpperCamelCase__ ,			device=UpperCamelCase__	)
		original_model.eval()
		print('Done!'	)

		# update state dict keys
		A					       = original_model.state_dict()
		A					       = create_rename_keys(UpperCamelCase__	)
		for src, dest in rename_keys:
				rename_key(UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)

		# some keys can be renamed efficiently
		for key, val in state_dict.copy().items():
				A					       = state_dict.pop(UpperCamelCase__	)
				if key.startswith('Qformer.bert'	):
						A					       = key.replace('Qformer.bert' ,			'qformer'	)
				if "attention.self" in key:
						A					       = key.replace('self' ,			'attention'	)
				if "llm_proj" in key:
						A					       = key.replace('llm_proj' ,			'language_projection'	)
				if "t5_proj" in key:
						A					       = key.replace('t5_proj' ,			'language_projection'	)
				if key.startswith('llm_model'	):
						A					       = key.replace('llm_model' ,			'language_model'	)
				if key.startswith('t5'	):
						A					       = key.replace('t5' ,			'language'	)
				A					       = val

		# read in qv biases
		read_in_q_v_bias(UpperCamelCase__ ,			UpperCamelCase__	)

		# note: weights get loaded in torch.float32 by default
		hf_model.load_state_dict(UpperCamelCase__ ,			strict=UpperCamelCase__	)

		A					       = load_demo_image()
		A					       = 'What is unusual about this image?'

		# create processor
		A					       = BlipImageProcessor(
		    size={'height': image_size, 'width': image_size} ,			image_mean=UpperCamelCase__ ,			image_std=UpperCamelCase__	)
		A					       = InstructBlipProcessor(
		    image_processor=UpperCamelCase__ ,			tokenizer=UpperCamelCase__ ,			qformer_tokenizer=UpperCamelCase__ ,			)
		A					       = processor(images=UpperCamelCase__ ,			text=UpperCamelCase__ ,			return_tensors='pt'	).to(UpperCamelCase__	)

		# make sure processor creates exact same pixel values
		A					       = vis_processors['eval'](UpperCamelCase__	).unsqueeze(0	).to(UpperCamelCase__	)
		A					       = inputs.pixel_values
		assert torch.allclose(original_pixel_values.to(pixel_values.device	) ,			UpperCamelCase__	)

		original_model.to(UpperCamelCase__	)
		hf_model.to(UpperCamelCase__	)
		with torch.no_grad():
				if "vicuna" in model_name:
						A					       = original_model({'image': original_pixel_values, 'text_input': [prompt]}	).logits
						A					       = hf_model(**UpperCamelCase__	).logits
				else:
						A					       = original_model(
						    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}	).logits
						A					       = tokenizer('\n' ,			return_tensors='pt'	).input_ids.to(UpperCamelCase__	)
						A					       = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id ,			-100	)
						A					       = hf_model(**UpperCamelCase__ ,			labels=UpperCamelCase__	).logits

		print('First values of original logits:' ,			original_logits[0, :3, :3]	)
		print('First values of HF logits:' ,			logits[0, :3, :3]	)

		# assert values
		assert original_logits.shape == logits.shape
		A					       = 1E-4 if 'vicuna' in model_name else 1E-5
		assert torch.allclose(original_logits.to(logits.device	) ,			UpperCamelCase__ ,			atol=UpperCamelCase__	)
		print('Looks ok!'	)

		print('Generating with original model...'	)
		A					       = original_model.generate({'image': original_pixel_values, 'prompt': prompt} ,			num_beams=5	)

		# important: we need to cast the weights of the HF model to the appropriate type
		print('Generating with HF model...'	)
		A					       = hf_model.generate(
		    **UpperCamelCase__ ,			do_sample=UpperCamelCase__ ,			num_beams=5 ,			max_length=256 ,			min_length=1 ,			top_p=0.9 ,			repetition_penalty=1.5 ,			length_penalty=1.0 ,			temperature=1 ,			)
		if "vicuna" in model_name:
				# convert output id 0 to 2 (eos_token_id)
				# TODO add this in the generate method?
				A					       = 2
		print('Original generation:' ,			UpperCamelCase__	)
		A					       = processor.batch_decode(UpperCamelCase__ ,			skip_special_tokens=UpperCamelCase__	)
		A					       = [text.strip() for text in output_text]
		print('HF generation:' ,			UpperCamelCase__	)

		if pytorch_dump_folder_path is not None:
				processor.save_pretrained(UpperCamelCase__	)
				hf_model.save_pretrained(UpperCamelCase__	)

		if push_to_hub:
				processor.push_to_hub(f'Salesforce/{model_name}'	)
				hf_model.push_to_hub(f'Salesforce/{model_name}'	)


if __name__ == "__main__":
	UpperCamelCase						:				Optional[int]             =      argparse.ArgumentParser()
	UpperCamelCase						:				Tuple             =      [
	    "instructblip-vicuna-7b",
	    "instructblip-vicuna-13b",
	    "instructblip-flan-t5-xl",
	    "instructblip-flan-t5-xxl",
	]
	parser.add_argument(
	    "--model_name",
	    default="instructblip-flan-t5-xl",
	    choices=choices,
	    type=str,
	    help="Path to hf config.json of model to convert",
	)
	parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
	parser.add_argument(
	    "--push_to_hub",
	    action="store_true",
	    help="Whether to push the model and processor to the hub after converting",
	)

	UpperCamelCase						:				Dict             =      parser.parse_args()

	convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""


def  __snake_case       (   )      ->		int:







		"""simple docstring"""





		return 1







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else two_pence(x - 2	) + one_pence()







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else five_pence(x - 5	) + two_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else ten_pence(x - 10	) + five_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else twenty_pence(x - 20	) + ten_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else fifty_pence(x - 50	) + twenty_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else one_pound(x - 100	) + fifty_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else two_pound(x - 200	) + one_pound(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__ = 200	)      ->		int:







		"""simple docstring"""





		return two_pound(UpperCamelCase__	)


if __name__ == "__main__":
	print(solution(int(input().strip())))





