



def 		UpperCamelCase     (       snake_case__	:   list[int]	,					snake_case__	:   list[int]	,					snake_case__	:   int     )     ->					bool:
   return not any(
       neighbour == 1 and colored_vertices[i] == color
       for i, neighbour in enumerate(snake_case__     )     )
def 		UpperCamelCase     (       snake_case__	:   list[list[int]]	,					snake_case__	:   int	,					snake_case__	:   list[int]	,					snake_case__	:   int     )     ->					bool:

   # Base Case
   if index == len(snake_case__     ):
      return True

   # Recursive Step
   for i in range(snake_case__     ):
      if valid_coloring(graph[index]	,					snake_case__	,					snake_case__     ):
         # Color current vertex
         UpperCamelCase   :  Union[str, Any]						     =     i
         # Validate coloring
         if util_color(snake_case__	,					snake_case__	,					snake_case__	,					index + 1     ):
            return True
         # Backtrack
         UpperCamelCase   :  Optional[int]						     =     -1
   return False


def 		UpperCamelCase     (       snake_case__	:   list[list[int]]	,					snake_case__	:   int     )     ->					list[int]:
   UpperCamelCase   :  List[str]						     =     [-1] * len(snake_case__     )

   if util_color(snake_case__	,					snake_case__	,					snake_case__	,					0     ):
      return colored_vertices

   return []








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

   return image
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					List[Any]:
   UpperCamelCase   :  Optional[int]						     =     []
   # 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 		UpperCamelCase     (       snake_case__	:   Optional[Any]	,					snake_case__	:   Dict	,					snake_case__	:   Optional[int]     )     ->					Optional[int]:
   UpperCamelCase   :  Dict						     =     dct.pop(snake_case__     )
   UpperCamelCase   :  str						     =     val
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   Union[str, Any]     )     ->					Optional[int]:
   for i in range(config.vision_config.num_hidden_layers     ):
      # read in original q and v biases
      UpperCamelCase   :  Optional[Any]						     =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""     )
      UpperCamelCase   :  int						     =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""     )

      # next, set bias in the state dict
      UpperCamelCase   :  int						     =     torch.cat((q_bias, torch.zeros_like(snake_case__	,					requires_grad=snake_case__     ), v_bias)     )
      UpperCamelCase   :  Tuple						     =     qkv_bias
def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[Any]     )     ->					Dict:
   UpperCamelCase   :  str						     =     364 if 'coco' in model_name else 224
   UpperCamelCase   :  Union[str, Any]						     =     BlipaVisionConfig(image_size=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:
      UpperCamelCase   :  List[Any]						     =     OPTConfig.from_pretrained('facebook/opt-2.7b'	,					eos_token_id=snake_case__     ).to_dict()
   elif "opt-6.7b" in model_name:
      UpperCamelCase   :  int						     =     OPTConfig.from_pretrained('facebook/opt-6.7b'	,					eos_token_id=snake_case__     ).to_dict()
   elif "t5-xl" in model_name:
      UpperCamelCase   :  List[str]						     =     TaConfig.from_pretrained('google/flan-t5-xl'	,					dense_act_fn='gelu'	,					bos_token_id=1     ).to_dict()
   elif "t5-xxl" in model_name:
      UpperCamelCase   :  int						     =     TaConfig.from_pretrained('google/flan-t5-xxl'	,					dense_act_fn='gelu'	,					bos_token_id=1     ).to_dict()

   UpperCamelCase   :  Any						     =     BlipaConfig(vision_config=snake_case__	,					text_config=snake_case__     )

   return config, image_size
@torch.no_grad()
def 		UpperCamelCase     (       snake_case__	:   int	,					snake_case__	:   Dict=None	,					snake_case__	:   int=False     )     ->					List[Any]:
   UpperCamelCase   :  str						     =     (
       AutoTokenizer.from_pretrained('facebook/opt-2.7b'     )
       if 'opt' in model_name
       else AutoTokenizer.from_pretrained('google/flan-t5-xl'     )
   )
   UpperCamelCase   :  int						     =     tokenizer('\n'	,					add_special_tokens=snake_case__     ).input_ids[0]
   UpperCamelCase     ,					UpperCamelCase   :  Union[str, Any]						     =     get_blipa_config(snake_case__	,					eos_token_id=snake_case__     )

   UpperCamelCase   :  Dict						     =     BlipaForConditionalGeneration(snake_case__     ).eval()

   UpperCamelCase   :  Optional[Any]						     =     {
       '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'),
   }

   UpperCamelCase     ,					UpperCamelCase   :  Optional[Any]						     =     model_name_to_original[model_name]

   # load original model
   print('Loading original model...'     )
   UpperCamelCase   :  List[str]						     =     'cuda' if torch.cuda.is_available() else 'cpu'
   UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Tuple						     =     load_model_and_preprocess(
       name=snake_case__	,					model_type=snake_case__	,					is_eval=snake_case__	,					device=snake_case__     )
   original_model.eval()
   print('Done!'     )

   # update state dict keys
   UpperCamelCase   :  List[Any]						     =     original_model.state_dict()
   UpperCamelCase   :  Tuple						     =     create_rename_keys(snake_case__     )
   for src, dest in rename_keys:
      rename_key(snake_case__	,					snake_case__	,					snake_case__     )

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      UpperCamelCase   :  Optional[Any]						     =     state_dict.pop(snake_case__     )
      if key.startswith('Qformer.bert'     ):
         UpperCamelCase   :  List[str]						     =     key.replace('Qformer.bert'	,					'qformer'     )
      if "attention.self" in key:
         UpperCamelCase   :  Tuple						     =     key.replace('self'	,					'attention'     )
      if "opt_proj" in key:
         UpperCamelCase   :  Union[str, Any]						     =     key.replace('opt_proj'	,					'language_projection'     )
      if "t5_proj" in key:
         UpperCamelCase   :  Optional[Any]						     =     key.replace('t5_proj'	,					'language_projection'     )
      if key.startswith('opt'     ):
         UpperCamelCase   :  Dict						     =     key.replace('opt'	,					'language'     )
      if key.startswith('t5'     ):
         UpperCamelCase   :  Dict						     =     key.replace('t5'	,					'language'     )
      UpperCamelCase   :  Optional[int]						     =     val

   # read in qv biases
   read_in_q_v_bias(snake_case__	,					snake_case__     )

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

   UpperCamelCase   :  List[str]						     =     load_demo_image()
   UpperCamelCase   :  str						     =     vis_processors['eval'](snake_case__     ).unsqueeze(0     ).to(snake_case__     )
   UpperCamelCase   :  Any						     =     tokenizer(['\n']	,					return_tensors='pt'     ).input_ids.to(snake_case__     )

   # create processor
   UpperCamelCase   :  Optional[Any]						     =     BlipImageProcessor(
       size={'height': image_size, 'width': image_size}	,					image_mean=snake_case__	,					image_std=snake_case__     )
   UpperCamelCase   :  Any						     =     BlipaProcessor(image_processor=snake_case__	,					tokenizer=snake_case__     )
   UpperCamelCase   :  Optional[int]						     =     processor(images=snake_case__	,					return_tensors='pt'     ).pixel_values.to(snake_case__     )

   # make sure processor creates exact same pixel values
   assert torch.allclose(snake_case__	,					snake_case__     )

   original_model.to(snake_case__     )
   hf_model.to(snake_case__     )
   with torch.no_grad():
      if "opt" in model_name:
         UpperCamelCase   :  Tuple						     =     original_model({'image': original_pixel_values, 'text_input': ['']}     ).logits
         UpperCamelCase   :  str						     =     hf_model(snake_case__	,					snake_case__     ).logits
      else:
         UpperCamelCase   :  Tuple						     =     original_model(
             {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}     ).logits
         UpperCamelCase   :  List[Any]						     =     input_ids.masked_fill(input_ids == tokenizer.pad_token_id	,					-100     )
         UpperCamelCase   :  Optional[int]						     =     hf_model(snake_case__	,					snake_case__	,					labels=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":
      UpperCamelCase   :  List[str]						     =     torch.tensor(
          [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]	,					device=snake_case__     )
      assert torch.allclose(logits[0, :3, :3]	,					snake_case__	,					atol=1E-4     )
   elif model_name == "blip2-flan-t5-xl-coco":
      UpperCamelCase   :  Union[str, Any]						     =     torch.tensor(
          [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]	,					device=snake_case__     )
   else:
      # cast to same type
      UpperCamelCase   :  Optional[int]						     =     logits.dtype
      assert torch.allclose(original_logits.to(snake_case__     )	,					snake_case__	,					atol=1E-2     )
   print('Looks ok!'     )

   print('Generating a caption...'     )
   UpperCamelCase   :  Optional[int]						     =     ''
   UpperCamelCase   :  Union[str, Any]						     =     tokenizer(snake_case__	,					return_tensors='pt'     ).input_ids.to(snake_case__     )

   UpperCamelCase   :  str						     =     original_model.generate({'image': original_pixel_values}     )
   UpperCamelCase   :  str						     =     hf_model.generate(
       snake_case__	,					snake_case__	,					do_sample=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:'	,					snake_case__     )
   UpperCamelCase   :  Optional[int]						     =     input_ids.shape[1]
   UpperCamelCase   :  Union[str, Any]						     =     processor.batch_decode(outputs[:, prompt_length:]	,					skip_special_tokens=snake_case__     )
   UpperCamelCase   :  Dict						     =     [text.strip() for text in output_text]
   print('HF generation:'	,					snake_case__     )

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



