


"""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
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD

def        lowercase							(		) ->      Tuple:
		__a           =			'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
		__a           =			Image.open(requests.get(__SCREAMING_SNAKE_CASE    ,					stream=__SCREAMING_SNAKE_CASE			).raw			).convert('''RGB'''			)

		return image

def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      List[Any]:
		__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.layernorm.weight''')			)
		rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''')			)

		# fmt: on
		return rename_keys

def        lowercase							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Optional[int]			) ->      List[str]:
		__a           =			dct.pop(__SCREAMING_SNAKE_CASE			)
		__a           =			val

def        lowercase							(		lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     List[Any]			) ->      Union[str, Any]:
		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(__SCREAMING_SNAKE_CASE    ,					requires_grad=__SCREAMING_SNAKE_CASE			), v_bias)			)
				__a           =			qkv_bias

def        lowercase							(		lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Union[str, Any]			) ->      Union[str, Any]:
		__a           =			364 if 'coco' in model_name else 224
		__a           =			BlipaVisionConfig(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 "opt-2.7b" in model_name:
				__a           =			OPTConfig.from_pretrained('''facebook/opt-2.7b'''    ,					eos_token_id=__SCREAMING_SNAKE_CASE			).to_dict()
		elif "opt-6.7b" in model_name:
				__a           =			OPTConfig.from_pretrained('''facebook/opt-6.7b'''    ,					eos_token_id=__SCREAMING_SNAKE_CASE			).to_dict()
		elif "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()

		__a           =			BlipaConfig(vision_config=__SCREAMING_SNAKE_CASE    ,					text_config=__SCREAMING_SNAKE_CASE			)

		return config, image_size

@torch.no_grad()
def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     str=None    ,					lowerCAmelCase__ :     Any=False			) ->      int:
		__a           =			(
		    AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''			)
		    if 'opt' in model_name
		    else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''			)
		)
		__a           =			tokenizer('''\n'''    ,					add_special_tokens=__SCREAMING_SNAKE_CASE			).input_ids[0]
		__a           =			get_blipa_config(__SCREAMING_SNAKE_CASE    ,					eos_token_id=__SCREAMING_SNAKE_CASE			)

		__a           =			BlipaForConditionalGeneration(__SCREAMING_SNAKE_CASE			).eval()

		__a           =			{
		    'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
		    'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
		    'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
		    'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
		    'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
		    'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
		    'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
		}

		__a           =			model_name_to_original[model_name]

		# load original model
		print('''Loading original model...'''			)
		__a           =			'cuda' if torch.cuda.is_available() else 'cpu'
		__a           =			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
		__a           =			original_model.state_dict()
		__a           =			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():
				__a           =			state_dict.pop(__SCREAMING_SNAKE_CASE			)
				if key.startswith('''Qformer.bert'''			):
						__a           =			key.replace('''Qformer.bert'''    ,					'''qformer'''			)
				if "attention.self" in key:
						__a           =			key.replace('''self'''    ,					'''attention'''			)
				if "opt_proj" in key:
						__a           =			key.replace('''opt_proj'''    ,					'''language_projection'''			)
				if "t5_proj" in key:
						__a           =			key.replace('''t5_proj'''    ,					'''language_projection'''			)
				if key.startswith('''opt'''			):
						__a           =			key.replace('''opt'''    ,					'''language'''			)
				if key.startswith('''t5'''			):
						__a           =			key.replace('''t5'''    ,					'''language'''			)
				__a           =			val

		# read in qv biases
		read_in_q_v_bias(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE			)

		__a           =			hf_model.load_state_dict(__SCREAMING_SNAKE_CASE    ,					strict=__SCREAMING_SNAKE_CASE			)
		assert len(__SCREAMING_SNAKE_CASE			) == 0
		assert unexpected_keys == ["qformer.embeddings.position_ids"]

		__a           =			load_demo_image()
		__a           =			vis_processors['eval'](__SCREAMING_SNAKE_CASE			).unsqueeze(0			).to(__SCREAMING_SNAKE_CASE			)
		__a           =			tokenizer(['''\n''']    ,					return_tensors='''pt'''			).input_ids.to(__SCREAMING_SNAKE_CASE			)

		# create processor
		__a           =			BlipImageProcessor(
		    size={'''height''': image_size, '''width''': image_size}    ,					image_mean=__SCREAMING_SNAKE_CASE    ,					image_std=__SCREAMING_SNAKE_CASE			)
		__a           =			BlipaProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,					tokenizer=__SCREAMING_SNAKE_CASE			)
		__a           =			processor(images=__SCREAMING_SNAKE_CASE    ,					return_tensors='''pt'''			).pixel_values.to(__SCREAMING_SNAKE_CASE			)

		# make sure processor creates exact same pixel values
		assert torch.allclose(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE			)

		original_model.to(__SCREAMING_SNAKE_CASE			)
		hf_model.to(__SCREAMING_SNAKE_CASE			)
		with torch.no_grad():
				if "opt" in model_name:
						__a           =			original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}			).logits
						__a           =			hf_model(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE			).logits
				else:
						__a           =			original_model(
						    {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}			).logits
						__a           =			input_ids.masked_fill(input_ids == tokenizer.pad_token_id    ,					-100			)
						__a           =			hf_model(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					labels=__SCREAMING_SNAKE_CASE			).logits

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

		# assert values
		if model_name == "blip2-flan-t5-xl":
				__a           =			torch.tensor(
				    [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]    ,					device=__SCREAMING_SNAKE_CASE			)
				assert torch.allclose(logits[0, :3, :3]    ,					__SCREAMING_SNAKE_CASE    ,					atol=1e-4			)
		elif model_name == "blip2-flan-t5-xl-coco":
				__a           =			torch.tensor(
				    [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]    ,					device=__SCREAMING_SNAKE_CASE			)
		else:
				# cast to same type
				__a           =			logits.dtype
				assert torch.allclose(original_logits.to(__SCREAMING_SNAKE_CASE			)    ,					__SCREAMING_SNAKE_CASE    ,					atol=1e-2			)
		print('''Looks ok!'''			)

		print('''Generating a caption...'''			)
		__a           =			''
		__a           =			tokenizer(__SCREAMING_SNAKE_CASE    ,					return_tensors='''pt'''			).input_ids.to(__SCREAMING_SNAKE_CASE			)

		__a           =			original_model.generate({'''image''': original_pixel_values}			)
		__a           =			hf_model.generate(
		    __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					do_sample=__SCREAMING_SNAKE_CASE    ,					num_beams=5    ,					max_length=30    ,					min_length=1    ,					top_p=0.9    ,					repetition_penalty=1.0    ,					length_penalty=1.0    ,					temperature=1    ,					)
		print('''Original generation:'''    ,					__SCREAMING_SNAKE_CASE			)
		__a           =			input_ids.shape[1]
		__a           =			processor.batch_decode(outputs[:, prompt_length:]    ,					skip_special_tokens=__SCREAMING_SNAKE_CASE			)
		__a           =			[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'''nielsr/{model_name}'''			)
				hf_model.push_to_hub(f'''nielsr/{model_name}'''			)


if __name__ == "__main__":
				lowercase_			     =  argparse.ArgumentParser()
				lowercase_			     =  [
				    "blip2-opt-2.7b",
				    "blip2-opt-6.7b",
				    "blip2-opt-2.7b-coco",
				    "blip2-opt-6.7b-coco",
				    "blip2-flan-t5-xl",
				    "blip2-flan-t5-xl-coco",
				    "blip2-flan-t5-xxl",
				]
				parser.add_argument(
				    "--model_name",
				    default="blip2-opt-2.7b",
				    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",
				)

				lowercase_			     =  parser.parse_args()

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







"""simple docstring"""







from __future__ import annotations



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list      ):
     if not nums:
          raise ValueError('List is empty'      )
     return sum(__SCREAMING_SNAKE_CASE      ) / len(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

