


import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
   print("""Googling.....""")
   UpperCAmelCase      =				"""https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
   UpperCAmelCase      =				requests.get(url, headers={"""UserAgent""": UserAgent().random})
   # res.raise_for_status()
   with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
      for data in res.iter_content(1_0000):
         out_file.write(data)
   UpperCAmelCase      =				BeautifulSoup(res.text, """html.parser""")
   UpperCAmelCase      =				list(soup.select(""".eZt8xd"""))[:5]

   print(len(links))
   for link in links:
      if link.text == "Maps":
         webbrowser.open(link.get("""href"""))
      else:
         webbrowser.open(f'''https://google.com{link.get('href')}''')



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   ()->  Optional[Any]:




      """simple docstring"""

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

      return image






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      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.layernorm.weight''') )
      rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''') )

      # fmt: on
      return rename_keys






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      snake_case_	=				dct.pop(SCREAMING_SNAKE_CASE )
      snake_case_	=				val






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(config.vision_config.num_hidden_layers ):
            # read in original q and v biases
            snake_case_	=				state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias''' )
            snake_case_	=				state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias''' )

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

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

      snake_case_	=				BlipaConfig(vision_config=SCREAMING_SNAKE_CASE , text_config=SCREAMING_SNAKE_CASE )

      return config, image_size






@torch.no_grad()
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=False )->  List[Any]:




      """simple docstring"""

      snake_case_	=				(
          AutoTokenizer.from_pretrained('''facebook/opt-2.7b''' )
          if '''opt''' in model_name
          else AutoTokenizer.from_pretrained('''google/flan-t5-xl''' )
      )
      snake_case_	=				tokenizer('''\n''' , add_special_tokens=SCREAMING_SNAKE_CASE ).input_ids[0]
      snake_case_	,   snake_case_	=				get_blipa_config(SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE )

      snake_case_	=				BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE ).eval()

      snake_case_	=				{
          '''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'''),
      }

      snake_case_	,   snake_case_	=				model_name_to_original[model_name]

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

      # read in qv biases
      read_in_q_v_bias(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

      snake_case_	,   snake_case_	=				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"]

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

      # create processor
      snake_case_	=				BlipImageProcessor(
          size={'''height''': image_size, '''width''': image_size} , image_mean=SCREAMING_SNAKE_CASE , image_std=SCREAMING_SNAKE_CASE )
      snake_case_	=				BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
      snake_case_	=				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:
                  snake_case_	=				original_model({'''image''': original_pixel_values, '''text_input''': ['''''']} ).logits
                  snake_case_	=				hf_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).logits
            else:
                  snake_case_	=				original_model(
                      {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']} ).logits
                  snake_case_	=				input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
                  snake_case_	=				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":
            snake_case_	=				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=SCREAMING_SNAKE_CASE )
            assert torch.allclose(logits[0, :3, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 )
      elif model_name == "blip2-flan-t5-xl-coco":
            snake_case_	=				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=SCREAMING_SNAKE_CASE )
      else:
            # cast to same type
            snake_case_	=				logits.dtype
            assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE , atol=1E-2 )
      print('''Looks ok!''' )

      print('''Generating a caption...''' )
      snake_case_	=				''''''
      snake_case_	=				tokenizer(SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).input_ids.to(SCREAMING_SNAKE_CASE )

      snake_case_	=				original_model.generate({'''image''': original_pixel_values} )
      snake_case_	=				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 )
      snake_case_	=				input_ids.shape[1]
      snake_case_	=				processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=SCREAMING_SNAKE_CASE )
      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'''nielsr/{model_name}''' )
            hf_model.push_to_hub(f'''nielsr/{model_name}''' )


if __name__ == "__main__":
   UpperCAmelCase      =   argparse.ArgumentParser()
   UpperCAmelCase      =   [
       """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""",
   )

   UpperCAmelCase      =   parser.parse_args()

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