



from abc import ABC, abstractmethod
from argparse import ArgumentParser



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	@staticmethod
	@abstractmethod
	def      UpperCamelCase_       (lowerCamelCase_		):
						"""simple docstring"""

						raise NotImplementedError()




	@abstractmethod
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						raise NotImplementedError()





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     a(      )		->       Optional[int]:
					"""simple docstring"""





					a					     =   "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
					a					     =   Image.open(requests.get(A ,     stream=A       ).raw       ).convert("RGB"       )

					return image






def     a(      A       :    Tuple       )		->       List[str]:
					"""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.layernorm.weight")       )
					rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")       )

					# fmt: on
					return rename_keys






def     a(      A       :    str ,     A       :    Tuple ,     A       :    Optional[int]       )		->       int:
					"""simple docstring"""





					a					     =   dct.pop(A       )
					a					     =   val






def     a(      A       :    Union[str, Any] ,     A       :    str       )		->       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(A ,     requires_grad=A       ), v_bias)       )
										a					     =   qkv_bias






def     a(      A       :    Union[str, Any] ,     A       :    Tuple       )		->       str:
					"""simple docstring"""





					a					     =   364 if "coco" in model_name else 224
					a					     =   BlipaVisionConfig(image_size=A       ).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=A       ).to_dict()
					elif "opt-6.7b" in model_name:
										a					     =   OPTConfig.from_pretrained("facebook/opt-6.7b" ,     eos_token_id=A       ).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=A ,     text_config=A       )

					return config, image_size






@torch.no_grad()
def     a(      A       :    Optional[int] ,     A       :    List[str]=None ,     A       :    Optional[int]=False       )		->       Any:
					"""simple docstring"""





					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=A       ).input_ids[0]
					a     ,				a					     =   get_blipa_config(A ,     eos_token_id=A       )

					a					     =   BlipaForConditionalGeneration(A       ).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     ,				a					     =   model_name_to_original[model_name]

					# load original model
					print("Loading original model..."       )
					a					     =   "cuda" if torch.cuda.is_available() else "cpu"
					a     ,				a     ,				a					     =   load_model_and_preprocess(
					    name=A ,     model_type=A ,     is_eval=A ,     device=A       )
					original_model.eval()
					print("Done!"       )

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

					# some keys can be renamed efficiently
					for key, val in state_dict.copy().items():
										a					     =   state_dict.pop(A       )
										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(A ,     A       )

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

					a					     =   load_demo_image()
					a					     =   vis_processors["eval"](A       ).unsqueeze(0       ).to(A       )
					a					     =   tokenizer(["\n"] ,     return_tensors="pt"       ).input_ids.to(A       )

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

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

					original_model.to(A       )
					hf_model.to(A       )
					with torch.no_grad():
										if "opt" in model_name:
															a					     =   original_model({"image": original_pixel_values, "text_input": [""]}       ).logits
															a					     =   hf_model(A ,     A       ).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(A ,     A ,     labels=A       ).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(
										    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] ,     device=A       )
										assert torch.allclose(logits[0, :3, :3] ,     A ,     atol=1e-4       )
					elif model_name == "blip2-flan-t5-xl-coco":
										a					     =   torch.tensor(
										    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] ,     device=A       )
					else:
										# cast to same type
										a					     =   logits.dtype
										assert torch.allclose(original_logits.to(A       ) ,     A ,     atol=1e-2       )
					print("Looks ok!"       )

					print("Generating a caption..."       )
					a					     =   ""
					a					     =   tokenizer(A ,     return_tensors="pt"       ).input_ids.to(A       )

					a					     =   original_model.generate({"image": original_pixel_values}       )
					a					     =   hf_model.generate(
					    A ,     A ,     do_sample=A ,     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:" ,     A       )
					a					     =   input_ids.shape[1]
					a					     =   processor.batch_decode(outputs[:, prompt_length:] ,     skip_special_tokens=A       )
					a					     =   [text.strip() for text in output_text]
					print("HF generation:" ,     A       )

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

					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: Dict      			=		argparse.ArgumentParser()
				_lowercase: 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",
				)

				_lowercase: Any      			=		parser.parse_args()

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