"""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 							SCREAMING_SNAKE_CASE  (    ) ->   int:
  _lowerCAmelCase		: Optional[Any]						    =  """https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
  _lowerCAmelCase		: List[Any]						    =  Image.open(requests.get(_lowerCamelCase    ,stream=_lowerCamelCase			).raw			).convert("""RGB"""			)

  return image





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple			) ->   str:
  _lowerCAmelCase		: 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.embeddings.layernorm.weight""")			)
  rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.embeddings.layernorm.bias""")			)

  # fmt: on
  return rename_keys





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: int    ,_lowerCamelCase		: List[Any]			) ->   List[Any]:
  _lowerCAmelCase		: str						    =  dct.pop(_lowerCamelCase			)
  _lowerCAmelCase		: Dict						    =  val





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: List[str]			) ->   str:
  for i in range(config.vision_config.num_hidden_layers			):
    # read in original q and v biases
    _lowerCAmelCase		: int						    =  state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias"			)
    _lowerCAmelCase		: List[Any]						    =  state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias"			)

    # next, set bias in the state dict
    _lowerCAmelCase		: Any						    =  torch.cat((q_bias, torch.zeros_like(_lowerCamelCase    ,requires_grad=_lowerCamelCase			), v_bias)			)
    _lowerCAmelCase		: Optional[Any]						    =  qkv_bias





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]			) ->   List[str]:
  _lowerCAmelCase		: int						    =  364 if """coco""" in model_name else 224
  _lowerCAmelCase		: int						    =  InstructBlipVisionConfig(image_size=_lowerCamelCase			).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:
    _lowerCAmelCase		: str						    =  TaConfig.from_pretrained("""google/flan-t5-xl"""    ,dense_act_fn="""gelu"""    ,bos_token_id=1			).to_dict()
  elif "t5-xxl" in model_name:
    _lowerCAmelCase		: Optional[Any]						    =  TaConfig.from_pretrained("""google/flan-t5-xxl"""    ,dense_act_fn="""gelu"""    ,bos_token_id=1			).to_dict()
  elif "vicuna-7b" in model_name:
    _lowerCAmelCase		: Dict						    =  LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""    ,vocab_size=32001			).to_dict()
  elif "vicuna-13b" in model_name:
    _lowerCAmelCase		: Tuple						    =  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
  _lowerCAmelCase		: List[Any]						    =  InstructBlipQFormerConfig(vocab_size=30523			).to_dict()
  _lowerCAmelCase		: Optional[Any]						    =  InstructBlipConfig(vision_config=_lowerCamelCase    ,text_config=_lowerCamelCase    ,qformer_config=_lowerCamelCase			)

  return config, image_size





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: List[Any]=None    ,_lowerCamelCase		: Any=False			) ->   Any:
  _lowerCAmelCase		: int						    =  AutoTokenizer.from_pretrained("""bert-base-uncased"""    ,truncation_side="""left"""			)
  qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}			)

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

  _lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  get_blipa_config(_lowerCamelCase			)
  _lowerCAmelCase		: Union[str, Any]						    =  InstructBlipForConditionalGeneration(_lowerCamelCase			).eval()

  _lowerCAmelCase		: List[Any]						    =  {
      """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"""),
  }

  _lowerCAmelCase    ,			_lowerCAmelCase		: Union[str, Any]						    =  model_name_to_original[model_name]

  # load original model
  print("""Loading original model..."""			)
  _lowerCAmelCase		: Union[str, Any]						    =  """cuda:1""" if torch.cuda.is_available() else """cpu"""
  _lowerCAmelCase		: Optional[int]						    =  """cuda:2""" if torch.cuda.is_available() else """cpu"""
  _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Tuple						    =  load_model_and_preprocess(
      name=_lowerCamelCase    ,model_type=_lowerCamelCase    ,is_eval=_lowerCamelCase    ,device=_lowerCamelCase			)
  original_model.eval()
  print("""Done!"""			)

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

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    _lowerCAmelCase		: int						    =  state_dict.pop(_lowerCamelCase			)
    if key.startswith("""Qformer.bert"""			):
      _lowerCAmelCase		: Tuple						    =  key.replace("""Qformer.bert"""    ,"""qformer"""			)
    if "attention.self" in key:
      _lowerCAmelCase		: int						    =  key.replace("""self"""    ,"""attention"""			)
    if "llm_proj" in key:
      _lowerCAmelCase		: Dict						    =  key.replace("""llm_proj"""    ,"""language_projection"""			)
    if "t5_proj" in key:
      _lowerCAmelCase		: Dict						    =  key.replace("""t5_proj"""    ,"""language_projection"""			)
    if key.startswith("""llm_model"""			):
      _lowerCAmelCase		: str						    =  key.replace("""llm_model"""    ,"""language_model"""			)
    if key.startswith("""t5"""			):
      _lowerCAmelCase		: Optional[Any]						    =  key.replace("""t5"""    ,"""language"""			)
    _lowerCAmelCase		: Dict						    =  val

  # read in qv biases
  read_in_q_v_bias(_lowerCamelCase    ,_lowerCamelCase			)

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

  _lowerCAmelCase		: int						    =  load_demo_image()
  _lowerCAmelCase		: Dict						    =  """What is unusual about this image?"""

  # create processor
  _lowerCAmelCase		: int						    =  BlipImageProcessor(
      size={"""height""": image_size, """width""": image_size}    ,image_mean=_lowerCamelCase    ,image_std=_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  InstructBlipProcessor(
      image_processor=_lowerCamelCase    ,tokenizer=_lowerCamelCase    ,qformer_tokenizer=_lowerCamelCase    ,)
  _lowerCAmelCase		: Optional[Any]						    =  processor(images=_lowerCamelCase    ,text=_lowerCamelCase    ,return_tensors="""pt"""			).to(_lowerCamelCase			)

  # make sure processor creates exact same pixel values
  _lowerCAmelCase		: Any						    =  vis_processors["""eval"""](_lowerCamelCase			).unsqueeze(0			).to(_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  inputs.pixel_values
  assert torch.allclose(original_pixel_values.to(pixel_values.device			)    ,_lowerCamelCase			)

  original_model.to(_lowerCamelCase			)
  hf_model.to(_lowerCamelCase			)
  with torch.no_grad():
    if "vicuna" in model_name:
      _lowerCAmelCase		: Dict						    =  original_model({"""image""": original_pixel_values, """text_input""": [prompt]}			).logits
      _lowerCAmelCase		: str						    =  hf_model(**_lowerCamelCase			).logits
    else:
      _lowerCAmelCase		: Dict						    =  original_model(
          {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}			).logits
      _lowerCAmelCase		: Union[str, Any]						    =  tokenizer("""\n"""    ,return_tensors="""pt"""			).input_ids.to(_lowerCamelCase			)
      _lowerCAmelCase		: List[Any]						    =  label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id    ,-100			)
      _lowerCAmelCase		: str						    =  hf_model(**_lowerCamelCase    ,labels=_lowerCamelCase			).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
  _lowerCAmelCase		: Optional[Any]						    =  1e-4 if """vicuna""" in model_name else 1e-5
  assert torch.allclose(original_logits.to(logits.device			)    ,_lowerCamelCase    ,atol=_lowerCamelCase			)
  print("""Looks ok!"""			)

  print("""Generating with original model..."""			)
  _lowerCAmelCase		: str						    =  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..."""			)
  _lowerCAmelCase		: int						    =  hf_model.generate(
      **_lowerCamelCase    ,do_sample=_lowerCamelCase    ,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?
    _lowerCAmelCase		: List[str]						    =  2
  print("""Original generation:"""    ,_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  processor.batch_decode(_lowerCamelCase    ,skip_special_tokens=_lowerCamelCase			)
  _lowerCAmelCase		: Union[str, Any]						    =  [text.strip() for text in output_text]
  print("""HF generation:"""    ,_lowerCamelCase			)

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

  if push_to_hub:
    processor.push_to_hub(f"Salesforce/{model_name}"			)
    hf_model.push_to_hub(f"Salesforce/{model_name}"			)


if __name__ == "__main__":
     _a   :	Tuple 					=     argparse.ArgumentParser()
     _a   :	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',
     )

     _a   :	Any 					=     parser.parse_args()

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





"""simple docstring"""




from PIL import Image





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Image    ,_lowerCamelCase		: int			) ->   Image:
  _lowerCAmelCase		: Any						    =  (259 * (level + 255)) / (255 * (259 - level))

  def contrast(_lowerCamelCase		: int			) -> int:
    return int(128 + factor * (c - 128)			)

  return img.point(_lowerCamelCase			)


if __name__ == "__main__":
     # Load image
     with Image.open('image_data/lena.jpg') as img:
          # Change contrast to 170
          _a   :	str 					=     change_contrast(img, 170)
          cont_img.save('image_data/lena_high_contrast.png', format='png')




