


"""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     lowercase			()   ->						Tuple:
						SCREAMING_SNAKE_CASE													=					'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
						SCREAMING_SNAKE_CASE													=					Image.open(requests.get(SCREAMING_SNAKE_CASE_ ,			stream=SCREAMING_SNAKE_CASE_  ).raw  ).convert('RGB'  )

						return image


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					[]
						# 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     lowercase			(SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any] ,			SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						List[str]:
						SCREAMING_SNAKE_CASE													=					dct.pop(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					val


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any] ,			SCREAMING_SNAKE_CASE_		:				List[Any]  )   ->						List[Any]:
						for i in range(config.vision_config.num_hidden_layers  ):
												# read in original q and v biases
												SCREAMING_SNAKE_CASE													=					state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'  )
												SCREAMING_SNAKE_CASE													=					state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'  )

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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						int:
						SCREAMING_SNAKE_CASE													=					3_64 if 'coco' in model_name else 2_24
						SCREAMING_SNAKE_CASE													=					InstructBlipVisionConfig(image_size=SCREAMING_SNAKE_CASE_  ).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:
												SCREAMING_SNAKE_CASE													=					TaConfig.from_pretrained('google/flan-t5-xl' ,			dense_act_fn='gelu' ,			bos_token_id=1  ).to_dict()
						elif "t5-xxl" in model_name:
												SCREAMING_SNAKE_CASE													=					TaConfig.from_pretrained('google/flan-t5-xxl' ,			dense_act_fn='gelu' ,			bos_token_id=1  ).to_dict()
						elif "vicuna-7b" in model_name:
												SCREAMING_SNAKE_CASE													=					LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf' ,			vocab_size=3_20_01  ).to_dict()
						elif "vicuna-13b" in model_name:
												SCREAMING_SNAKE_CASE													=					LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf' ,			vocab_size=3_20_01  ).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
						SCREAMING_SNAKE_CASE													=					InstructBlipQFormerConfig(vocab_size=3_05_23  ).to_dict()
						SCREAMING_SNAKE_CASE													=					InstructBlipConfig(vision_config=SCREAMING_SNAKE_CASE_ ,			text_config=SCREAMING_SNAKE_CASE_ ,			qformer_config=SCREAMING_SNAKE_CASE_  )

						return config, image_size


@torch.no_grad()
def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				str=None ,			SCREAMING_SNAKE_CASE_		:				Tuple=False  )   ->						Tuple:
						SCREAMING_SNAKE_CASE													=					AutoTokenizer.from_pretrained('bert-base-uncased' ,			truncation_side='left'  )
						qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}  )

						if "t5" in model_name:
												SCREAMING_SNAKE_CASE													=					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>"})
												SCREAMING_SNAKE_CASE													=					LlamaTokenizerFast.from_pretrained(
												    'huggyllama/llama-7b' ,			truncation_side='left' ,			bos_token='</s>' ,			unk_token='</s>'  )
												tokenizer.add_special_tokens({'pad_token': '[PAD]'}  )

						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					get_blipa_config(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					InstructBlipForConditionalGeneration(SCREAMING_SNAKE_CASE_  ).eval()

						SCREAMING_SNAKE_CASE													=					{
						    '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'),
						}

						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					model_name_to_original[model_name]

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

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

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

						# read in qv biases
						read_in_q_v_bias(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

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

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

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

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

						original_model.to(SCREAMING_SNAKE_CASE_  )
						hf_model.to(SCREAMING_SNAKE_CASE_  )
						with torch.no_grad():
												if "vicuna" in model_name:
																		SCREAMING_SNAKE_CASE													=					original_model({'image': original_pixel_values, 'text_input': [prompt]}  ).logits
																		SCREAMING_SNAKE_CASE													=					hf_model(**SCREAMING_SNAKE_CASE_  ).logits
												else:
																		SCREAMING_SNAKE_CASE													=					original_model(
																		    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}  ).logits
																		SCREAMING_SNAKE_CASE													=					tokenizer('\n' ,			return_tensors='pt'  ).input_ids.to(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id ,			-1_00  )
																		SCREAMING_SNAKE_CASE													=					hf_model(**SCREAMING_SNAKE_CASE_ ,			labels=SCREAMING_SNAKE_CASE_  ).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
						SCREAMING_SNAKE_CASE													=					1E-4 if 'vicuna' in model_name else 1E-5
						assert torch.allclose(original_logits.to(logits.device  ) ,			SCREAMING_SNAKE_CASE_ ,			atol=SCREAMING_SNAKE_CASE_  )
						print('Looks ok!'  )

						print('Generating with original model...'  )
						SCREAMING_SNAKE_CASE													=					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...'  )
						SCREAMING_SNAKE_CASE													=					hf_model.generate(
						    **SCREAMING_SNAKE_CASE_ ,			do_sample=SCREAMING_SNAKE_CASE_ ,			num_beams=5 ,			max_length=2_56 ,			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?
												SCREAMING_SNAKE_CASE													=					2
						print('Original generation:' ,			SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					processor.batch_decode(SCREAMING_SNAKE_CASE_ ,			skip_special_tokens=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					[text.strip() for text in output_text]
						print('HF generation:' ,			SCREAMING_SNAKE_CASE_  )

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

						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						=      argparse.ArgumentParser()
	__UpperCamelCase						=      [
	    '''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						=      parser.parse_args()

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






"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						str:
						SCREAMING_SNAKE_CASE													=					OmegaConf.load(SCREAMING_SNAKE_CASE_  )
						if display:
												print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE_  )  )  )
						return config


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Tuple=None ,			SCREAMING_SNAKE_CASE_		:				Tuple=None  )   ->						Any:
						if conf_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.yaml'
						SCREAMING_SNAKE_CASE													=					load_config(SCREAMING_SNAKE_CASE_ ,			display=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					VQModel(**config.model.params  )
						if ckpt_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.pt'
						SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location=SCREAMING_SNAKE_CASE_  )
						if ".ckpt" in ckpt_path:
												SCREAMING_SNAKE_CASE													=					sd['state_dict']
						model.load_state_dict(SCREAMING_SNAKE_CASE_ ,			strict=SCREAMING_SNAKE_CASE_  )
						model.to(SCREAMING_SNAKE_CASE_  )
						del sd
						return model


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					model.encode(SCREAMING_SNAKE_CASE_  )
						print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'  )
						SCREAMING_SNAKE_CASE													=					model.decode(SCREAMING_SNAKE_CASE_  )
						return xrec


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						Dict:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					string.rsplit('.' ,			1  )
						if reload:
												SCREAMING_SNAKE_CASE													=					importlib.import_module(SCREAMING_SNAKE_CASE_  )
												importlib.reload(SCREAMING_SNAKE_CASE_  )
						return getattr(importlib.import_module(SCREAMING_SNAKE_CASE_ ,			package=SCREAMING_SNAKE_CASE_  ) ,			cls  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Any  )   ->						Dict:
						if "target" not in config:
												raise KeyError('Expected key `target` to instantiate.'  )
						return get_obj_from_str(config['target']  )(**config.get('params' ,			{}  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				List[Any]=True ,			SCREAMING_SNAKE_CASE_		:				int=True  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					instantiate_from_config(SCREAMING_SNAKE_CASE_  )
						if sd is not None:
												model.load_state_dict(SCREAMING_SNAKE_CASE_  )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				List[str]  )   ->						Any:
						# load the specified checkpoint
						if ckpt:
												SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location='cpu'  )
												SCREAMING_SNAKE_CASE													=					pl_sd['global_step']
												print(F'loaded model from global step {global_step}.'  )
						else:
												SCREAMING_SNAKE_CASE													=					{'state_dict': None}
												SCREAMING_SNAKE_CASE													=					None
						SCREAMING_SNAKE_CASE													=					load_model_from_config(config.model ,			pl_sd['state_dict'] ,			gpu=SCREAMING_SNAKE_CASE_ ,			eval_mode=SCREAMING_SNAKE_CASE_  )['model']
						return model, global_step


