


'''simple docstring'''






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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def 	_A				()   ->  Optional[int]:




     '''simple docstring'''



     _a			      =							'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
     _a			      =							Image.open(requests.get(lowerCAmelCase__				,						stream=lowerCAmelCase__		).raw		).convert('RGB'		)

     return image






def 	_A				(lowerCAmelCase__  :Dict		)   ->  Optional[Any]:




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

     # fmt: on
     return rename_keys






def 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :int		)   ->  Optional[Any]:




     '''simple docstring'''



     _a			      =							dct.pop(lowerCAmelCase__		)
     _a			      =							val






def 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :List[str]		)   ->  List[str]:




     '''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(lowerCAmelCase__				,						requires_grad=lowerCAmelCase__		), v_bias)		)
          _a			      =							qkv_bias






def 	_A				(lowerCAmelCase__  :int		)   ->  List[Any]:




     '''simple docstring'''



     _a			      =							3_64 if 'coco' in model_name else 2_24
     _a			      =							InstructBlipVisionConfig(image_size=lowerCAmelCase__		).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 "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()
     elif "vicuna-7b" in model_name:
          _a			      =							LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'				,						vocab_size=3_20_01		).to_dict()
     elif "vicuna-13b" in model_name:
          _a			      =							LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'				,						vocab_size=3_20_01		).to_dict()
     else:
          raise ValueError('Model name not supported'		)

     # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
     _a			      =							InstructBlipQFormerConfig(vocab_size=3_05_23		).to_dict()
     _a			      =							InstructBlipConfig(vision_config=lowerCAmelCase__				,						text_config=lowerCAmelCase__				,						qformer_config=lowerCAmelCase__		)

     return config, image_size






@torch.no_grad()
def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :List[str]=None				,						lowerCAmelCase__  :Any=False		)   ->  Optional[int]:




     '''simple docstring'''



     _a			      =							AutoTokenizer.from_pretrained('bert-base-uncased'				,						truncation_side='left'		)
     qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}		)

     if "t5" in model_name:
          _a			      =							TaTokenizerFast.from_pretrained('google/flan-t5-xl'				,						truncation_side='left'		)
     elif "vicuna" in model_name:
          # the following was used in the original implementation:
          # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
          # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
          # tokenizer.add_special_tokens({"bos_token": "</s>"})
          # tokenizer.add_special_tokens({"eos_token": "</s>"})
          # tokenizer.add_special_tokens({"unk_token": "</s>"})
          _a			      =							LlamaTokenizerFast.from_pretrained(
              'huggyllama/llama-7b'				,						truncation_side='left'				,						bos_token='</s>'				,						unk_token='</s>'		)
          tokenizer.add_special_tokens({'pad_token': '[PAD]'}		)

     _a      ,   _a			      =							get_blipa_config(lowerCAmelCase__		)
     _a			      =							InstructBlipForConditionalGeneration(lowerCAmelCase__		).eval()

     _a			      =							{
         'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
         'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
         'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
         'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
     }

     _a      ,   _a			      =							model_name_to_original[model_name]

     # load original model
     print('Loading original model...'		)
     _a			      =							'cuda:1' if torch.cuda.is_available() else 'cpu'
     _a			      =							'cuda:2' if torch.cuda.is_available() else 'cpu'
     _a      ,   _a      ,   _a			      =							load_model_and_preprocess(
         name=lowerCAmelCase__				,						model_type=lowerCAmelCase__				,						is_eval=lowerCAmelCase__				,						device=lowerCAmelCase__		)
     original_model.eval()
     print('Done!'		)

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

     # some keys can be renamed efficiently
     for key, val in state_dict.copy().items():
          _a			      =							state_dict.pop(lowerCAmelCase__		)
          if key.startswith('Qformer.bert'		):
               _a			      =							key.replace('Qformer.bert'				,						'qformer'		)
          if "attention.self" in key:
               _a			      =							key.replace('self'				,						'attention'		)
          if "llm_proj" in key:
               _a			      =							key.replace('llm_proj'				,						'language_projection'		)
          if "t5_proj" in key:
               _a			      =							key.replace('t5_proj'				,						'language_projection'		)
          if key.startswith('llm_model'		):
               _a			      =							key.replace('llm_model'				,						'language_model'		)
          if key.startswith('t5'		):
               _a			      =							key.replace('t5'				,						'language'		)
          _a			      =							val

     # read in qv biases
     read_in_q_v_bias(lowerCAmelCase__				,						lowerCAmelCase__		)

     # note: weights get loaded in torch.float32 by default
     hf_model.load_state_dict(lowerCAmelCase__				,						strict=lowerCAmelCase__		)

     _a			      =							load_demo_image()
     _a			      =							'What is unusual about this image?'

     # create processor
     _a			      =							BlipImageProcessor(
         size={'height': image_size, 'width': image_size}				,						image_mean=lowerCAmelCase__				,						image_std=lowerCAmelCase__		)
     _a			      =							InstructBlipProcessor(
         image_processor=lowerCAmelCase__				,						tokenizer=lowerCAmelCase__				,						qformer_tokenizer=lowerCAmelCase__				,						)
     _a			      =							processor(images=lowerCAmelCase__				,						text=lowerCAmelCase__				,						return_tensors='pt'		).to(lowerCAmelCase__		)

     # make sure processor creates exact same pixel values
     _a			      =							vis_processors['eval'](lowerCAmelCase__		).unsqueeze(0		).to(lowerCAmelCase__		)
     _a			      =							inputs.pixel_values
     assert torch.allclose(original_pixel_values.to(pixel_values.device		)				,						lowerCAmelCase__		)

     original_model.to(lowerCAmelCase__		)
     hf_model.to(lowerCAmelCase__		)
     with torch.no_grad():
          if "vicuna" in model_name:
               _a			      =							original_model({'image': original_pixel_values, 'text_input': [prompt]}		).logits
               _a			      =							hf_model(**lowerCAmelCase__		).logits
          else:
               _a			      =							original_model(
                   {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}		).logits
               _a			      =							tokenizer('\n'				,						return_tensors='pt'		).input_ids.to(lowerCAmelCase__		)
               _a			      =							label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id				,						-1_00		)
               _a			      =							hf_model(**lowerCAmelCase__				,						labels=lowerCAmelCase__		).logits

     print('First values of original logits:'				,						original_logits[0, :3, :3]		)
     print('First values of HF logits:'				,						logits[0, :3, :3]		)

     # assert values
     assert original_logits.shape == logits.shape
     _a			      =							1E-4 if 'vicuna' in model_name else 1E-5
     assert torch.allclose(original_logits.to(logits.device		)				,						lowerCAmelCase__				,						atol=lowerCAmelCase__		)
     print('Looks ok!'		)

     print('Generating with original model...'		)
     _a			      =							original_model.generate({'image': original_pixel_values, 'prompt': prompt}				,						num_beams=5		)

     # important: we need to cast the weights of the HF model to the appropriate type
     print('Generating with HF model...'		)
     _a			      =							hf_model.generate(
         **lowerCAmelCase__				,						do_sample=lowerCAmelCase__				,						num_beams=5				,						max_length=2_56				,						min_length=1				,						top_p=0.9				,						repetition_penalty=1.5				,						length_penalty=1.0				,						temperature=1				,						)
     if "vicuna" in model_name:
          # convert output id 0 to 2 (eos_token_id)
          # TODO add this in the generate method?
          _a			      =							2
     print('Original generation:'				,						lowerCAmelCase__		)
     _a			      =							processor.batch_decode(lowerCAmelCase__				,						skip_special_tokens=lowerCAmelCase__		)
     _a			      =							[text.strip() for text in output_text]
     print('HF generation:'				,						lowerCAmelCase__		)

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

     if push_to_hub:
          processor.push_to_hub(f'Salesforce/{model_name}'		)
          hf_model.push_to_hub(f'Salesforce/{model_name}'		)


if __name__ == "__main__":
 a_					:    List[Any]          =    argparse.ArgumentParser()
 a_					:    Optional[Any]          =    [
     "instructblip-vicuna-7b",
     "instructblip-vicuna-13b",
     "instructblip-flan-t5-xl",
     "instructblip-flan-t5-xxl",
 ]
 parser.add_argument(
     "--model_name",
     default="instructblip-flan-t5-xl",
     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)





'''simple docstring'''






class  a     :
 def __init__( self						)					->							List[Any]:
      _a			      =							''
      _a			      =							''
      _a			      =							[]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      if m == -1:
           return n + 1
      elif n == -1:
           return m + 1
      elif self.dp[m][n] > -1:
           return self.dp[m][n]
      else:
           if self.worda[m] == self.worda[n]:
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
           else:
                _a			      =							self.__min_dist_top_down_dp(__magic_name__      ,				n - 1						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				__magic_name__						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
                _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

           return self.dp[m][n]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							[[-1 for _ in range(len(__magic_name__						)						)] for _ in range(len(__magic_name__						)						)]

      return self.__min_dist_top_down_dp(len(__magic_name__						) - 1      ,				len(__magic_name__						) - 1						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							len(__magic_name__						)
      _a			      =							len(__magic_name__						)
      _a			      =							[[0 for _ in range(n + 1						)] for _ in range(m + 1						)]

      for i in range(m + 1						):
           for j in range(n + 1						):
                if i == 0:  # first string is empty
                     _a			      =							j
                elif j == 0:  # second string is empty
                     _a			      =							i
                elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                     _a			      =							self.dp[i - 1][j - 1]
                else:
                     _a			      =							self.dp[i][j - 1]
                     _a			      =							self.dp[i - 1][j]
                     _a			      =							self.dp[i - 1][j - 1]
                     _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
      return self.dp[m][n]


if __name__ == "__main__":
 a_					:    Dict          =    EditDistance()

 print("****************** Testing Edit Distance DP Algorithm ******************")
 print()

 a_					:    Tuple          =    input("Enter the first string: ").strip()
 a_					:    Dict          =    input("Enter the second string: ").strip()

 print()
 print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
 print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
 print()
 print("*************** End of Testing Edit Distance DP Algorithm ***************")

