

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   (    )					->   List[Any]:





      """simple docstring"""



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

      return image
def   a   (    A__			:     str  )					->   Any:





      """simple docstring"""



      _lowercase			 =[]
      # 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__			:     Dict   ,     A__			:     List[str]   ,     A__			:     str  )					->   List[Any]:





      """simple docstring"""



      _lowercase			 =dct.pop(A__  )
      _lowercase			 =val
def   a   (    A__			:     int   ,     A__			:     Union[str, Any]  )					->   List[str]:





      """simple docstring"""



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

            # next, set bias in the state dict
            _lowercase			 =torch.cat((q_bias, torch.zeros_like(A__   ,     requires_grad=A__  ), v_bias)  )
            _lowercase			 =qkv_bias
def   a   (    A__			:     str   ,     A__			:     Dict  )					->   Any:





      """simple docstring"""



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

      _lowercase			 =BlipaConfig(vision_config=A__   ,     text_config=A__  )

      return config, image_size
@torch.no_grad()
def   a   (    A__			:     Optional[int]   ,     A__			:     str=None   ,     A__			:     Optional[Any]=False  )					->   Tuple:





      """simple docstring"""



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

      _lowercase			 =BlipaForConditionalGeneration(A__  ).eval()

      _lowercase			 ={
          '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'),
      }

      _lowercase , _lowercase			 =model_name_to_original[model_name]

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

      # update state dict keys
      _lowercase			 =original_model.state_dict()
      _lowercase			 =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():
            _lowercase			 =state_dict.pop(A__  )
            if key.startswith('Qformer.bert'  ):
                  _lowercase			 =key.replace('Qformer.bert'   ,     'qformer'  )
            if "attention.self" in key:
                  _lowercase			 =key.replace('self'   ,     'attention'  )
            if "opt_proj" in key:
                  _lowercase			 =key.replace('opt_proj'   ,     'language_projection'  )
            if "t5_proj" in key:
                  _lowercase			 =key.replace('t5_proj'   ,     'language_projection'  )
            if key.startswith('opt'  ):
                  _lowercase			 =key.replace('opt'   ,     'language'  )
            if key.startswith('t5'  ):
                  _lowercase			 =key.replace('t5'   ,     'language'  )
            _lowercase			 =val

      # read in qv biases
      read_in_q_v_bias(A__   ,     A__  )

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

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

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

      print('Generating a caption...'  )
      _lowercase			 =''
      _lowercase			 =tokenizer(A__   ,     return_tensors='pt'  ).input_ids.to(A__  )

      _lowercase			 =original_model.generate({'image': original_pixel_values}  )
      _lowercase			 =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__  )
      _lowercase			 =input_ids.shape[1]
      _lowercase			 =processor.batch_decode(outputs[:, prompt_length:]   ,     skip_special_tokens=A__  )
      _lowercase			 =[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_             = 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)





import requests

lowercase_             =     'YOUR API KEY'
def   a   (    A__			:     str   ,     A__			:     str = giphy_api_key  )					->   list:





      """simple docstring"""



      _lowercase			 ='+'.join(query.split()  )
      _lowercase			 =F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
      _lowercase			 =requests.get(A__  ).json()['data']
      return [gif["url"] for gif in gifs]


if __name__ == "__main__":
  print('\n'.join(get_gifs('space ship')))


