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

 return image






def 							A						(    snake_case				:List[Any]		)				->     List[Any]:
 __UpperCamelCase      =	[]
 # 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						(    snake_case				:List[str]    , snake_case				:Optional[Any]    , snake_case				:Union[str, Any]		)				->     List[str]:
 __UpperCamelCase      =	dct.pop(snake_case		)
 __UpperCamelCase      =	val






def 							A						(    snake_case				:str    , snake_case				:Optional[int]		)				->     Tuple:
 for i in range(config.vision_config.num_hidden_layers		):
  # read in original q and v biases
  __UpperCamelCase      =	state_dict.pop(f'visual_encoder.blocks.{i}.attn.q_bias'		)
  __UpperCamelCase      =	state_dict.pop(f'visual_encoder.blocks.{i}.attn.v_bias'		)

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






def 							A						(    snake_case				:Union[str, Any]		)				->     Union[str, Any]:
 __UpperCamelCase      =	3_6_4 if 'coco' in model_name else 2_2_4
 __UpperCamelCase      =	InstructBlipVisionConfig(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 "t5-xl" in model_name:
  __UpperCamelCase      =	TaConfig.from_pretrained('google/flan-t5-xl'    , dense_act_fn='gelu'    , bos_token_id=1		).to_dict()
 elif "t5-xxl" in model_name:
  __UpperCamelCase      =	TaConfig.from_pretrained('google/flan-t5-xxl'    , dense_act_fn='gelu'    , bos_token_id=1		).to_dict()
 elif "vicuna-7b" in model_name:
  __UpperCamelCase      =	LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'    , vocab_size=3_2_0_0_1		).to_dict()
 elif "vicuna-13b" in model_name:
  __UpperCamelCase      =	LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'    , vocab_size=3_2_0_0_1		).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
 __UpperCamelCase      =	InstructBlipQFormerConfig(vocab_size=3_0_5_2_3		).to_dict()
 __UpperCamelCase      =	InstructBlipConfig(vision_config=snake_case    , text_config=snake_case    , qformer_config=snake_case		)

 return config, image_size






@torch.no_grad()
def 							A						(    snake_case				:List[str]    , snake_case				:List[Any]=None    , snake_case				:Tuple=False		)				->     Union[str, Any]:
 __UpperCamelCase      =	AutoTokenizer.from_pretrained('bert-base-uncased'    , truncation_side='left'		)
 qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}		)

 if "t5" in model_name:
  __UpperCamelCase      =	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>"})
  __UpperCamelCase      =	LlamaTokenizerFast.from_pretrained(
      'huggyllama/llama-7b'    , truncation_side='left'    , bos_token='</s>'    , unk_token='</s>'		)
  tokenizer.add_special_tokens({'pad_token': '[PAD]'}		)

 __UpperCamelCase  ,     __UpperCamelCase      =	get_blipa_config(snake_case		)
 __UpperCamelCase      =	InstructBlipForConditionalGeneration(snake_case		).eval()

 __UpperCamelCase      =	{
     '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'),
 }

 __UpperCamelCase  ,     __UpperCamelCase      =	model_name_to_original[model_name]

 # load original model
 print('Loading original model...'		)
 __UpperCamelCase      =	'cuda:1' if torch.cuda.is_available() else 'cpu'
 __UpperCamelCase      =	'cuda:2' if torch.cuda.is_available() else 'cpu'
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	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      =	original_model.state_dict()
 __UpperCamelCase      =	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      =	state_dict.pop(snake_case		)
  if key.startswith('Qformer.bert'		):
   __UpperCamelCase      =	key.replace('Qformer.bert'    , 'qformer'		)
  if "attention.self" in key:
   __UpperCamelCase      =	key.replace('self'    , 'attention'		)
  if "llm_proj" in key:
   __UpperCamelCase      =	key.replace('llm_proj'    , 'language_projection'		)
  if "t5_proj" in key:
   __UpperCamelCase      =	key.replace('t5_proj'    , 'language_projection'		)
  if key.startswith('llm_model'		):
   __UpperCamelCase      =	key.replace('llm_model'    , 'language_model'		)
  if key.startswith('t5'		):
   __UpperCamelCase      =	key.replace('t5'    , 'language'		)
  __UpperCamelCase      =	val

 # read in qv biases
 read_in_q_v_bias(snake_case    , snake_case		)

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

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

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

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

 original_model.to(snake_case		)
 hf_model.to(snake_case		)
 with torch.no_grad():
  if "vicuna" in model_name:
   __UpperCamelCase      =	original_model({'image': original_pixel_values, 'text_input': [prompt]}		).logits
   __UpperCamelCase      =	hf_model(**snake_case		).logits
  else:
   __UpperCamelCase      =	original_model(
       {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}		).logits
   __UpperCamelCase      =	tokenizer('\n'    , return_tensors='pt'		).input_ids.to(snake_case		)
   __UpperCamelCase      =	label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id    , -1_0_0		)
   __UpperCamelCase      =	hf_model(**snake_case    , labels=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
 __UpperCamelCase      =	1e-4 if 'vicuna' in model_name else 1e-5
 assert torch.allclose(original_logits.to(logits.device		)    , snake_case    , atol=snake_case		)
 print('Looks ok!'		)

 print('Generating with original model...'		)
 __UpperCamelCase      =	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...'		)
 __UpperCamelCase      =	hf_model.generate(
     **snake_case    , do_sample=snake_case    , num_beams=5    , max_length=2_5_6    , 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?
  __UpperCamelCase      =	2
 print('Original generation:'    , snake_case		)
 __UpperCamelCase      =	processor.batch_decode(snake_case    , skip_special_tokens=snake_case		)
 __UpperCamelCase      =	[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'Salesforce/{model_name}'		)
  hf_model.push_to_hub(f'Salesforce/{model_name}'		)


if __name__ == "__main__":
      UpperCamelCase     :    int       =			argparse.ArgumentParser()
      UpperCamelCase     :    str       =			[
          "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     :    Any       =			parser.parse_args()

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





"""simple docstring"""

def 							A						(    snake_case				:int		)				->     bool:
 return sum(i for i in range(1    , number // 2 + 1		) if number % i == 0		) == number


if __name__ == "__main__":
      print("Program to check whether a number is a Perfect number or not...")
      UpperCamelCase     :    Union[str, Any]       =			int(input("Enter number: ").strip())
      print(f'''{number} is {"" if perfect(number) else "not "}a Perfect Number.''')



