


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     __lowerCamelCase						(			):




      """simple docstring"""





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

      return image
def     __lowerCamelCase						(			UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						: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     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Optional[Any]						):




      """simple docstring"""





      a						:Union[str, Any]										=	dct.pop(UpperCAmelCase_						)
      a						:Optional[Any]										=	val
def     __lowerCamelCase						(			UpperCAmelCase_						:		Any			,  UpperCAmelCase_						:		Dict						):




      """simple docstring"""





      for i in range(config.vision_config.num_hidden_layers						):
            # read in original q and v biases
            a						:Any										=	state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''						)
            a						:List[Any]										=	state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''						)

            # next, set bias in the state dict
            a						:str										=	torch.cat((q_bias, torch.zeros_like(UpperCAmelCase_			,  requires_grad=UpperCAmelCase_						), v_bias)						)
            a						:Any										=	qkv_bias
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		List[str]						):




      """simple docstring"""





      a						:str										=	364 if '''coco''' in model_name else 224
      a						:Tuple										=	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:
            a						:int										=	OPTConfig.from_pretrained('''facebook/opt-2.7b'''			,  eos_token_id=UpperCAmelCase_						).to_dict()
      elif "opt-6.7b" in model_name:
            a						:Any										=	OPTConfig.from_pretrained('''facebook/opt-6.7b'''			,  eos_token_id=UpperCAmelCase_						).to_dict()
      elif "t5-xl" in model_name:
            a						:Any										=	TaConfig.from_pretrained('''google/flan-t5-xl'''			,  dense_act_fn='''gelu'''			,  bos_token_id=1						).to_dict()
      elif "t5-xxl" in model_name:
            a						:str										=	TaConfig.from_pretrained('''google/flan-t5-xxl'''			,  dense_act_fn='''gelu'''			,  bos_token_id=1						).to_dict()

      a						:Union[str, Any]										=	BlipaConfig(vision_config=UpperCAmelCase_			,  text_config=UpperCAmelCase_						)

      return config, image_size
@torch.no_grad()
def     __lowerCamelCase						(			UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		Union[str, Any]=None			,  UpperCAmelCase_						:		Dict=False						):




      """simple docstring"""





      a						:int										=	(
          AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''						)
          if '''opt''' in model_name
          else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''						)
      )
      a						:Union[str, Any]										=	tokenizer('''\n'''			,  add_special_tokens=UpperCAmelCase_						).input_ids[0]
      a						:Any										=	get_blipa_config(UpperCAmelCase_			,  eos_token_id=UpperCAmelCase_						)

      a						:Optional[Any]										=	BlipaForConditionalGeneration(UpperCAmelCase_						).eval()

      a						:List[str]										=	{
          '''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						:str										=	model_name_to_original[model_name]

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

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

      # read in qv biases
      read_in_q_v_bias(UpperCAmelCase_			,  UpperCAmelCase_						)

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

      a						:Dict										=	load_demo_image()
      a						:int										=	vis_processors['''eval'''](UpperCAmelCase_						).unsqueeze(0						).to(UpperCAmelCase_						)
      a						:Optional[Any]										=	tokenizer(['''\n''']			,  return_tensors='''pt'''						).input_ids.to(UpperCAmelCase_						)

      # create processor
      a						:str										=	BlipImageProcessor(
          size={'''height''': image_size, '''width''': image_size}			,  image_mean=UpperCAmelCase_			,  image_std=UpperCAmelCase_						)
      a						:str										=	BlipaProcessor(image_processor=UpperCAmelCase_			,  tokenizer=UpperCAmelCase_						)
      a						:Optional[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:
                  a						:Optional[int]										=	original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}						).logits
                  a						:Optional[int]										=	hf_model(UpperCAmelCase_			,  UpperCAmelCase_						).logits
            else:
                  a						:int										=	original_model(
                      {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}						).logits
                  a						:int										=	input_ids.masked_fill(input_ids == tokenizer.pad_token_id			,  -100						)
                  a						:Union[str, Any]										=	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":
            a						:str										=	torch.tensor(
                [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]			,  device=UpperCAmelCase_						)
            assert torch.allclose(logits[0, :3, :3]			,  UpperCAmelCase_			,  atol=1E-4						)
      elif model_name == "blip2-flan-t5-xl-coco":
            a						:List[str]										=	torch.tensor(
                [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]			,  device=UpperCAmelCase_						)
      else:
            # cast to same type
            a						:Optional[Any]										=	logits.dtype
            assert torch.allclose(original_logits.to(UpperCAmelCase_						)			,  UpperCAmelCase_			,  atol=1E-2						)
      print('''Looks ok!'''						)

      print('''Generating a caption...'''						)
      a						:str										=	''''''
      a						:str										=	tokenizer(UpperCAmelCase_			,  return_tensors='''pt'''						).input_ids.to(UpperCAmelCase_						)

      a						:Optional[Any]										=	original_model.generate({'''image''': original_pixel_values}						)
      a						:Union[str, Any]										=	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_						)
      a						:str										=	input_ids.shape[1]
      a						:Union[str, Any]										=	processor.batch_decode(outputs[:, prompt_length:]			,  skip_special_tokens=UpperCAmelCase_						)
      a						:Tuple										=	[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__":
     snake_case    :   List[str]	    =					argparse.ArgumentParser()
     snake_case    :   Optional[Any]	    =					[
         '''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''',
     )

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

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









import math
def     __lowerCamelCase						(			UpperCAmelCase_						:		float			,  UpperCAmelCase_						:		float						):




      """simple docstring"""





      return math.pow(UpperCAmelCase_			,  2						) - a
def     __lowerCamelCase						(			UpperCAmelCase_						:		float						):




      """simple docstring"""





      return 2 * x
def     __lowerCamelCase						(			UpperCAmelCase_						:		float						):




      """simple docstring"""





      a						:int										=	2.0

      while start <= a:
            a						:int										=	math.pow(UpperCAmelCase_			,  2						)

      return start
def     __lowerCamelCase						(			UpperCAmelCase_						:		float			,  UpperCAmelCase_						:		int = 9999			,  UpperCAmelCase_						:		float = 0.00000000000001						):




      """simple docstring"""





      if a < 0:
            raise ValueError('''math domain error'''						)

      a						:List[Any]										=	get_initial_point(UpperCAmelCase_						)

      for _ in range(UpperCAmelCase_						):
            a						:Optional[int]										=	value
            a						:int										=	value - fx(UpperCAmelCase_			,  UpperCAmelCase_						) / fx_derivative(UpperCAmelCase_						)
            if abs(prev_value - value						) < tolerance:
                  return value

      return value


if __name__ == "__main__":
     from doctest import testmod

     testmod()





