






'''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  __UpperCAmelCase						(					)	->      Dict:

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

   return image



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						Optional[Any]     )	->      Optional[int]:

   """simple docstring"""
   __a					  =     dct.pop(SCREAMING_SNAKE_CASE__     )
   __a					  =     val



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Tuple     )	->      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(SCREAMING_SNAKE_CASE__,       requires_grad=SCREAMING_SNAKE_CASE__     ), v_bias)     )
      __a					  =     qkv_bias



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any]     )	->      List[Any]:

   """simple docstring"""
   __a					  =     364 if 'coco' in model_name else 224
   __a					  =     InstructBlipVisionConfig(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 "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=32001     ).to_dict()
   elif "vicuna-13b" in model_name:
      __a					  =     LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf',       vocab_size=32001     ).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=30523     ).to_dict()
   __a					  =     InstructBlipConfig(vision_config=SCREAMING_SNAKE_CASE__,       text_config=SCREAMING_SNAKE_CASE__,       qformer_config=SCREAMING_SNAKE_CASE__     )

   return config, image_size



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

   """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(SCREAMING_SNAKE_CASE__     )
   __a					  =     InstructBlipForConditionalGeneration(SCREAMING_SNAKE_CASE__     ).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=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
   __a					  =     original_model.state_dict()
   __a					  =     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():
      __a					  =     state_dict.pop(SCREAMING_SNAKE_CASE__     )
      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(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

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

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

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

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

   original_model.to(SCREAMING_SNAKE_CASE__     )
   hf_model.to(SCREAMING_SNAKE_CASE__     )
   with torch.no_grad():
      if "vicuna" in model_name:
         __a					  =     original_model({'image': original_pixel_values, 'text_input': [prompt]}     ).logits
         __a					  =     hf_model(**SCREAMING_SNAKE_CASE__     ).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(SCREAMING_SNAKE_CASE__     )
         __a					  =     label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id,       -100     )
         __a					  =     hf_model(**SCREAMING_SNAKE_CASE__,       labels=SCREAMING_SNAKE_CASE__     ).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     ),       SCREAMING_SNAKE_CASE__,       atol=SCREAMING_SNAKE_CASE__     )
   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(
       **SCREAMING_SNAKE_CASE__,       do_sample=SCREAMING_SNAKE_CASE__,       num_beams=5,       max_length=256,       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:',       SCREAMING_SNAKE_CASE__     )
   __a					  =     processor.batch_decode(SCREAMING_SNAKE_CASE__,       skip_special_tokens=SCREAMING_SNAKE_CASE__     )
   __a					  =     [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"""Salesforce/{model_name}"""     )
      hf_model.push_to_hub(f"""Salesforce/{model_name}"""     )


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   argparse.ArgumentParser()
 __UpperCamelCase   :							Tuple         =   [
     """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""",
 )

 __UpperCamelCase   :							List[str]         =   parser.parse_args()

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







'''simple docstring'''



import os



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str = "matrix.txt"     )	->      int:

   """simple docstring"""
   with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__     ),       SCREAMING_SNAKE_CASE__     )     ) as in_file:
      __a					  =     in_file.read()

   __a					  =     [[int(SCREAMING_SNAKE_CASE__     ) for cell in row.split(','     )] for row in data.strip().splitlines()]
   __a					  =     [[0 for cell in row] for row in grid]
   __a					  =     len(grid[0]     )

   __a					  =     [[0 for i in range(SCREAMING_SNAKE_CASE__     )] for j in range(SCREAMING_SNAKE_CASE__     )]
   __a					  =     grid[0][0]
   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     grid[0][i] + dp[0][i - 1]
   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     grid[i][0] + dp[i - 1][0]

   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      for j in range(1,       SCREAMING_SNAKE_CASE__     ):
         __a					  =     grid[i][j] + min(dp[i - 1][j],       dp[i][j - 1]     )

   return dp[-1][-1]


if __name__ == "__main__":
 print(f"""{solution() = }""")