"""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
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[str]:
  a__:     Optional[int]										=	'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
  a__:     Union[str, Any]										=	Image.open(requests.get(UpperCamelCase_		,     stream=UpperCamelCase_   ).raw   ).convert('RGB'   )

  return image







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Tuple:
  a__:     Dict										=	[]
  # 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   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  a__:     Optional[int]										=	dct.pop(UpperCamelCase_   )
  a__:     List[Any]										=	val







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:


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

    # next, set bias in the state dict
    a__:     Any										=	torch.cat((q_bias, torch.zeros_like(UpperCamelCase_		,     requires_grad=UpperCamelCase_   ), v_bias)   )
    a__:     Tuple										=	qkv_bias







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Tuple:
  a__:     Optional[Any]										=	364 if 'coco' in model_name else 224
  a__:     Optional[int]										=	BlipaVisionConfig(image_size=UpperCamelCase_   ).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:
    a__:     str										=	OPTConfig.from_pretrained('facebook/opt-2.7b'		,     eos_token_id=UpperCamelCase_   ).to_dict()
  elif "opt-6.7b" in model_name:
    a__:     List[Any]										=	OPTConfig.from_pretrained('facebook/opt-6.7b'		,     eos_token_id=UpperCamelCase_   ).to_dict()
  elif "t5-xl" in model_name:
    a__:     List[Any]										=	TaConfig.from_pretrained('google/flan-t5-xl'		,     dense_act_fn='gelu'		,     bos_token_id=1   ).to_dict()
  elif "t5-xxl" in model_name:
    a__:     Tuple										=	TaConfig.from_pretrained('google/flan-t5-xxl'		,     dense_act_fn='gelu'		,     bos_token_id=1   ).to_dict()

  a__:     Any										=	BlipaConfig(vision_config=UpperCamelCase_		,     text_config=UpperCamelCase_   )

  return config, image_size







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

  a__:     List[str]										=	BlipaForConditionalGeneration(UpperCamelCase_   ).eval()

  a__:     Optional[int]										=	{
      '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'),
  }

  a__							,    a__:     List[str]										=	model_name_to_original[model_name]

  # load original model
  print('Loading original model...'   )
  a__:     Dict										=	'cuda' if torch.cuda.is_available() else 'cpu'
  a__							,    a__							,    a__:     Tuple										=	load_model_and_preprocess(
      name=UpperCamelCase_		,     model_type=UpperCamelCase_		,     is_eval=UpperCamelCase_		,     device=UpperCamelCase_   )
  original_model.eval()
  print('Done!'   )

  # update state dict keys
  a__:     Union[str, Any]										=	original_model.state_dict()
  a__:     int										=	create_rename_keys(UpperCamelCase_   )
  for src, dest in rename_keys:
    rename_key(UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_   )

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

  # read in qv biases
  read_in_q_v_bias(UpperCamelCase_		,     UpperCamelCase_   )

  a__							,    a__:     Dict										=	hf_model.load_state_dict(UpperCamelCase_		,     strict=UpperCamelCase_   )
  assert len(UpperCamelCase_   ) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

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

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

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

  original_model.to(UpperCamelCase_   )
  hf_model.to(UpperCamelCase_   )
  with torch.no_grad():
    if "opt" in model_name:
      a__:     Optional[int]										=	original_model({'image': original_pixel_values, 'text_input': ['']}   ).logits
      a__:     Dict										=	hf_model(UpperCamelCase_		,     UpperCamelCase_   ).logits
    else:
      a__:     List[str]										=	original_model(
          {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}   ).logits
      a__:     Dict										=	input_ids.masked_fill(input_ids == tokenizer.pad_token_id		,     -100   )
      a__:     Dict										=	hf_model(UpperCamelCase_		,     UpperCamelCase_		,     labels=UpperCamelCase_   ).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":
    a__:     str										=	torch.tensor(
        [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]		,     device=UpperCamelCase_   )
    assert torch.allclose(logits[0, :3, :3]		,     UpperCamelCase_		,     atol=1e-4   )
  elif model_name == "blip2-flan-t5-xl-coco":
    a__:     Union[str, Any]										=	torch.tensor(
        [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]		,     device=UpperCamelCase_   )
  else:
    # cast to same type
    a__:     str										=	logits.dtype
    assert torch.allclose(original_logits.to(UpperCamelCase_   )		,     UpperCamelCase_		,     atol=1e-2   )
  print('Looks ok!'   )

  print('Generating a caption...'   )
  a__:     Optional[Any]										=	''
  a__:     str										=	tokenizer(UpperCamelCase_		,     return_tensors='pt'   ).input_ids.to(UpperCamelCase_   )

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

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

  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 os
from math import logaa






def 							_lowercase							(     UpperCamelCase_ = "base_exp.txt"		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= 0
					for i, line in enumerate(open(os.path.join(os.path.dirname(UpperCamelCase_		)	,	UpperCamelCase_		)		)		):
										SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= list(map(UpperCamelCase_	,	line.split(','		)		)		)
										if x * logaa(UpperCamelCase_		) > largest:
															SCREAMING_SNAKE_CASE__													= x * logaa(UpperCamelCase_		)
															SCREAMING_SNAKE_CASE__													= i + 1
					return result


if __name__ == "__main__":
				print(solution())





