"""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       (     )     ->Dict:
  a__:					int      							=     'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
  a__:					Any      							=     Image.open(requests.get(_SCREAMING_SNAKE_CASE		,					stream=_SCREAMING_SNAKE_CASE       ).raw       ).convert('RGB'       )

  return image







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







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  for i in range(config.vision_config.num_hidden_layers       ):
    # read in original q and v biases
    a__:					List[str]      							=     state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'       )
    a__:					Union[str, Any]      							=     state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'       )

    # next, set bias in the state dict
    a__:					str      							=     torch.cat((q_bias, torch.zeros_like(_SCREAMING_SNAKE_CASE		,					requires_grad=_SCREAMING_SNAKE_CASE       ), v_bias)       )
    a__:					Union[str, Any]      							=     qkv_bias







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Any:
  a__:					List[Any]      							=     364 if 'coco' in model_name else 224
  a__:					Union[str, Any]      							=     BlipaVisionConfig(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 "opt-2.7b" in model_name:
    a__:					List[Any]      							=     OPTConfig.from_pretrained('facebook/opt-2.7b'		,					eos_token_id=_SCREAMING_SNAKE_CASE       ).to_dict()
  elif "opt-6.7b" in model_name:
    a__:					List[str]      							=     OPTConfig.from_pretrained('facebook/opt-6.7b'		,					eos_token_id=_SCREAMING_SNAKE_CASE       ).to_dict()
  elif "t5-xl" in model_name:
    a__:					Dict      							=     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=_SCREAMING_SNAKE_CASE		,					text_config=_SCREAMING_SNAKE_CASE       )

  return config, image_size







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

  a__:					Dict      							=     BlipaForConditionalGeneration(_SCREAMING_SNAKE_CASE       ).eval()

  a__:					List[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'),
  }

  a__							,   a__:					Dict      							=     model_name_to_original[model_name]

  # load original model
  print('Loading original model...'       )
  a__:					Tuple      							=     'cuda' if torch.cuda.is_available() else 'cpu'
  a__							,   a__							,   a__:					Tuple      							=     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__:					int      							=     original_model.state_dict()
  a__:					List[str]      							=     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__:					Tuple      							=     state_dict.pop(_SCREAMING_SNAKE_CASE       )
    if key.startswith('Qformer.bert'       ):
      a__:					List[Any]      							=     key.replace('Qformer.bert'		,					'qformer'       )
    if "attention.self" in key:
      a__:					Tuple      							=     key.replace('self'		,					'attention'       )
    if "opt_proj" in key:
      a__:					int      							=     key.replace('opt_proj'		,					'language_projection'       )
    if "t5_proj" in key:
      a__:					List[str]      							=     key.replace('t5_proj'		,					'language_projection'       )
    if key.startswith('opt'       ):
      a__:					Union[str, Any]      							=     key.replace('opt'		,					'language'       )
    if key.startswith('t5'       ):
      a__:					List[Any]      							=     key.replace('t5'		,					'language'       )
    a__:					str      							=     val

  # read in qv biases
  read_in_q_v_bias(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

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

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

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

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

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

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

  a__:					Any      							=     original_model.generate({'image': original_pixel_values}       )
  a__:					str      							=     hf_model.generate(
      _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					do_sample=_SCREAMING_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:'		,					_SCREAMING_SNAKE_CASE       )
  a__:					Union[str, Any]      							=     input_ids.shape[1]
  a__:					Tuple      							=     processor.batch_decode(outputs[:, prompt_length:]		,					skip_special_tokens=_SCREAMING_SNAKE_CASE       )
  a__:					Optional[Any]      							=     [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'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)



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE = 1000000       )     ->int:
  a__:					Any      							=     limit + 1
  a__:					List[str]      							=     [0] * limit
  for first_term in range(1		,					_SCREAMING_SNAKE_CASE       ):
    for n in range(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
      a__:					Any      							=     first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  a__:					Optional[int]      							=     sum(1 for x in frequency[1:limit] if x == 10       )

  return count


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


