



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 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Tuple									= '''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
		lowerCAmelCase_			:     Optional[Any]									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw ).convert('''RGB''' )

		return image


def 			UpperCamelCase(  __UpperCamelCase							:		str ):
		lowerCAmelCase_			:     str									= []
		# 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 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		Dict     ,__UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     int									= dct.pop(__UpperCamelCase )
		lowerCAmelCase_			:     Dict									= val


def 			UpperCamelCase(  __UpperCamelCase							:		List[Any]     ,__UpperCamelCase							:		Optional[Any] ):
		for i in range(config.vision_config.num_hidden_layers ):
				# read in original q and v biases
				lowerCAmelCase_			:     str									= state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias""" )
				lowerCAmelCase_			:     int									= state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias""" )

				# next, set bias in the state dict
				lowerCAmelCase_			:     Dict									= torch.cat((q_bias, torch.zeros_like(__UpperCamelCase     ,requires_grad=__UpperCamelCase ), v_bias) )
				lowerCAmelCase_			:     Tuple									= qkv_bias


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     Dict									= 364 if '''coco''' in model_name else 224
		lowerCAmelCase_			:     Any									= BlipaVisionConfig(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 "opt-2.7b" in model_name:
				lowerCAmelCase_			:     Union[str, Any]									= OPTConfig.from_pretrained('''facebook/opt-2.7b'''     ,eos_token_id=__UpperCamelCase ).to_dict()
		elif "opt-6.7b" in model_name:
				lowerCAmelCase_			:     Dict									= OPTConfig.from_pretrained('''facebook/opt-6.7b'''     ,eos_token_id=__UpperCamelCase ).to_dict()
		elif "t5-xl" in model_name:
				lowerCAmelCase_			:     int									= TaConfig.from_pretrained('''google/flan-t5-xl'''     ,dense_act_fn='''gelu'''     ,bos_token_id=1 ).to_dict()
		elif "t5-xxl" in model_name:
				lowerCAmelCase_			:     List[Any]									= TaConfig.from_pretrained('''google/flan-t5-xxl'''     ,dense_act_fn='''gelu'''     ,bos_token_id=1 ).to_dict()

		lowerCAmelCase_			:     Optional[Any]									= BlipaConfig(vision_config=__UpperCamelCase     ,text_config=__UpperCamelCase )

		return config, image_size


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

		lowerCAmelCase_			:     Union[str, Any]									= BlipaForConditionalGeneration(__UpperCamelCase ).eval()

		lowerCAmelCase_			:     Tuple									= {
		    '''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'''),
		}

		lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= model_name_to_original[model_name]

		# load original model
		print('''Loading original model...''' )
		lowerCAmelCase_			:     Optional[int]									= '''cuda''' if torch.cuda.is_available() else '''cpu'''
		lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Tuple									= load_model_and_preprocess(
		    name=__UpperCamelCase     ,model_type=__UpperCamelCase     ,is_eval=__UpperCamelCase     ,device=__UpperCamelCase )
		original_model.eval()
		print('''Done!''' )

		# update state dict keys
		lowerCAmelCase_			:     Tuple									= original_model.state_dict()
		lowerCAmelCase_			:     int									= 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():
				lowerCAmelCase_			:     Optional[Any]									= state_dict.pop(__UpperCamelCase )
				if key.startswith('''Qformer.bert''' ):
						lowerCAmelCase_			:     List[str]									= key.replace('''Qformer.bert'''     ,'''qformer''' )
				if "attention.self" in key:
						lowerCAmelCase_			:     Union[str, Any]									= key.replace('''self'''     ,'''attention''' )
				if "opt_proj" in key:
						lowerCAmelCase_			:     Any									= key.replace('''opt_proj'''     ,'''language_projection''' )
				if "t5_proj" in key:
						lowerCAmelCase_			:     List[Any]									= key.replace('''t5_proj'''     ,'''language_projection''' )
				if key.startswith('''opt''' ):
						lowerCAmelCase_			:     Union[str, Any]									= key.replace('''opt'''     ,'''language''' )
				if key.startswith('''t5''' ):
						lowerCAmelCase_			:     Optional[int]									= key.replace('''t5'''     ,'''language''' )
				lowerCAmelCase_			:     List[str]									= val

		# read in qv biases
		read_in_q_v_bias(__UpperCamelCase     ,__UpperCamelCase )

		lowerCAmelCase_					,  lowerCAmelCase_			:     Dict									= hf_model.load_state_dict(__UpperCamelCase     ,strict=__UpperCamelCase )
		assert len(__UpperCamelCase ) == 0
		assert unexpected_keys == ["qformer.embeddings.position_ids"]

		lowerCAmelCase_			:     List[str]									= load_demo_image()
		lowerCAmelCase_			:     Any									= vis_processors['''eval'''](__UpperCamelCase ).unsqueeze(0 ).to(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= tokenizer(['''\n''']     ,return_tensors='''pt''' ).input_ids.to(__UpperCamelCase )

		# create processor
		lowerCAmelCase_			:     Optional[int]									= BlipImageProcessor(
		    size={'''height''': image_size, '''width''': image_size}     ,image_mean=__UpperCamelCase     ,image_std=__UpperCamelCase )
		lowerCAmelCase_			:     Optional[Any]									= BlipaProcessor(image_processor=__UpperCamelCase     ,tokenizer=__UpperCamelCase )
		lowerCAmelCase_			:     int									= processor(images=__UpperCamelCase     ,return_tensors='''pt''' ).pixel_values.to(__UpperCamelCase )

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

		original_model.to(__UpperCamelCase )
		hf_model.to(__UpperCamelCase )
		with torch.no_grad():
				if "opt" in model_name:
						lowerCAmelCase_			:     List[str]									= original_model({'''image''': original_pixel_values, '''text_input''': ['''''']} ).logits
						lowerCAmelCase_			:     str									= hf_model(__UpperCamelCase     ,__UpperCamelCase ).logits
				else:
						lowerCAmelCase_			:     str									= original_model(
						    {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']} ).logits
						lowerCAmelCase_			:     Union[str, Any]									= input_ids.masked_fill(input_ids == tokenizer.pad_token_id     ,-100 )
						lowerCAmelCase_			:     Tuple									= hf_model(__UpperCamelCase     ,__UpperCamelCase     ,labels=__UpperCamelCase ).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":
				lowerCAmelCase_			:     Union[str, Any]									= torch.tensor(
				    [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]]     ,device=__UpperCamelCase )
				assert torch.allclose(logits[0, :3, :3]     ,__UpperCamelCase     ,atol=1e-4 )
		elif model_name == "blip2-flan-t5-xl-coco":
				lowerCAmelCase_			:     List[str]									= torch.tensor(
				    [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]]     ,device=__UpperCamelCase )
		else:
				# cast to same type
				lowerCAmelCase_			:     Dict									= logits.dtype
				assert torch.allclose(original_logits.to(__UpperCamelCase )     ,__UpperCamelCase     ,atol=1e-2 )
		print('''Looks ok!''' )

		print('''Generating a caption...''' )
		lowerCAmelCase_			:     Union[str, Any]									= ''''''
		lowerCAmelCase_			:     List[str]									= tokenizer(__UpperCamelCase     ,return_tensors='''pt''' ).input_ids.to(__UpperCamelCase )

		lowerCAmelCase_			:     Any									= original_model.generate({'''image''': original_pixel_values} )
		lowerCAmelCase_			:     Dict									= hf_model.generate(
		    __UpperCamelCase     ,__UpperCamelCase     ,do_sample=__UpperCamelCase     ,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:'''     ,__UpperCamelCase )
		lowerCAmelCase_			:     str									= input_ids.shape[1]
		lowerCAmelCase_			:     Optional[int]									= processor.batch_decode(outputs[:, prompt_length:]     ,skip_special_tokens=__UpperCamelCase )
		lowerCAmelCase_			:     Optional[int]									= [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"""nielsr/{model_name}""" )
				hf_model.push_to_hub(f"""nielsr/{model_name}""" )


if __name__ == "__main__":
						A__  :  int								=					argparse.ArgumentParser()
						A__  :  Tuple								=					[
						    '''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''',
						)

						A__  :  Optional[int]								=					parser.parse_args()

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








from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return [ord(SCREAMING_SNAKE_CASE__       ) - 96 for elem in plain]


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return "".join(chr(elem + 96       ) for elem in encoded       )


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	encode(input('''-> '''       ).strip().lower()       )
						print('''Encoded: '''  ,		SCREAMING_SNAKE_CASE__       )
						print('''Decoded:'''  ,		decode(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
					main()