






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        lowerCAmelCase (      )       ->							str:







  '''simple docstring'''







  _a							=	"https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
  _a							=	Image.open(requests.get(UpperCamelCase_     , stream=UpperCamelCase_					).raw					).convert("RGB"					)

  return image






def        lowerCAmelCase (      UpperCamelCase_:							List[Any]					)       ->							str:







  '''simple docstring'''







  _a							=	[]
  # 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        lowerCAmelCase (      UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							List[str]     , UpperCamelCase_:							Dict					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	dct.pop(UpperCamelCase_					)
  _a							=	val






def        lowerCAmelCase (      UpperCamelCase_:							Dict     , UpperCamelCase_:							Optional[Any]					)       ->							Optional[int]:







  '''simple docstring'''







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

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






def        lowerCAmelCase (      UpperCamelCase_:							Optional[Any]     , UpperCamelCase_:							Tuple					)       ->							int:







  '''simple docstring'''







  _a							=	364 if "coco" in model_name else 224
  _a							=	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							=	OPTConfig.from_pretrained("facebook/opt-2.7b"     , eos_token_id=UpperCamelCase_					).to_dict()
  elif "opt-6.7b" in model_name:
    _a							=	OPTConfig.from_pretrained("facebook/opt-6.7b"     , eos_token_id=UpperCamelCase_					).to_dict()
  elif "t5-xl" in model_name:
    _a							=	TaConfig.from_pretrained("google/flan-t5-xl"     , dense_act_fn="gelu"     , bos_token_id=1					).to_dict()
  elif "t5-xxl" in model_name:
    _a							=	TaConfig.from_pretrained("google/flan-t5-xxl"     , dense_act_fn="gelu"     , bos_token_id=1					).to_dict()

  _a							=	BlipaConfig(vision_config=UpperCamelCase_     , text_config=UpperCamelCase_					)

  return config, image_size






@torch.no_grad()
def        lowerCAmelCase (      UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							int=None     , UpperCamelCase_:							int=False					)       ->							Optional[Any]:







  '''simple docstring'''







  _a							=	(
      AutoTokenizer.from_pretrained("facebook/opt-2.7b"					)
      if "opt" in model_name
      else AutoTokenizer.from_pretrained("google/flan-t5-xl"					)
  )
  _a							=	tokenizer("\n"     , add_special_tokens=UpperCamelCase_					).input_ids[0]
  _a			,	_a							=	get_blipa_config(UpperCamelCase_     , eos_token_id=UpperCamelCase_					)

  _a							=	BlipaForConditionalGeneration(UpperCamelCase_					).eval()

  _a							=	{
      "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							=	model_name_to_original[model_name]

  # load original model
  print("Loading original model..."					)
  _a							=	"cuda" if torch.cuda.is_available() else "cpu"
  _a			,	_a			,	_a							=	load_model_and_preprocess(
      name=UpperCamelCase_     , model_type=UpperCamelCase_     , is_eval=UpperCamelCase_     , device=UpperCamelCase_					)
  original_model.eval()
  print("Done!"					)

  # update state dict keys
  _a							=	original_model.state_dict()
  _a							=	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							=	state_dict.pop(UpperCamelCase_					)
    if key.startswith("Qformer.bert"					):
      _a							=	key.replace("Qformer.bert"     , "qformer"					)
    if "attention.self" in key:
      _a							=	key.replace("self"     , "attention"					)
    if "opt_proj" in key:
      _a							=	key.replace("opt_proj"     , "language_projection"					)
    if "t5_proj" in key:
      _a							=	key.replace("t5_proj"     , "language_projection"					)
    if key.startswith("opt"					):
      _a							=	key.replace("opt"     , "language"					)
    if key.startswith("t5"					):
      _a							=	key.replace("t5"     , "language"					)
    _a							=	val

  # read in qv biases
  read_in_q_v_bias(UpperCamelCase_     , UpperCamelCase_					)

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

  _a							=	load_demo_image()
  _a							=	vis_processors["eval"](UpperCamelCase_					).unsqueeze(0					).to(UpperCamelCase_					)
  _a							=	tokenizer(["\n"]     , return_tensors="pt"					).input_ids.to(UpperCamelCase_					)

  # create processor
  _a							=	BlipImageProcessor(
      size={"height": image_size, "width": image_size}     , image_mean=UpperCamelCase_     , image_std=UpperCamelCase_					)
  _a							=	BlipaProcessor(image_processor=UpperCamelCase_     , tokenizer=UpperCamelCase_					)
  _a							=	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							=	original_model({"image": original_pixel_values, "text_input": [""]}					).logits
      _a							=	hf_model(UpperCamelCase_     , UpperCamelCase_					).logits
    else:
      _a							=	original_model(
          {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}					).logits
      _a							=	input_ids.masked_fill(input_ids == tokenizer.pad_token_id     , -100					)
      _a							=	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							=	torch.tensor(
        [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]     , device=UpperCamelCase_					)
    assert torch.allclose(logits[0, :3, :3]     , UpperCamelCase_     , atol=1E-4					)
  elif model_name == "blip2-flan-t5-xl-coco":
    _a							=	torch.tensor(
        [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]     , device=UpperCamelCase_					)
  else:
    # cast to same type
    _a							=	logits.dtype
    assert torch.allclose(original_logits.to(UpperCamelCase_					)     , UpperCamelCase_     , atol=1E-2					)
  print("Looks ok!"					)

  print("Generating a caption..."					)
  _a							=	""
  _a							=	tokenizer(UpperCamelCase_     , return_tensors="pt"					).input_ids.to(UpperCamelCase_					)

  _a							=	original_model.generate({"image": original_pixel_values}					)
  _a							=	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							=	input_ids.shape[1]
  _a							=	processor.batch_decode(outputs[:, prompt_length:]     , skip_special_tokens=UpperCamelCase_					)
  _a							=	[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__":
     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)










"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Tuple       			=				(KDPMaDiscreteScheduler,)
      A__	:		Tuple       			=				10






      def 			lowerCamelCase__    (    self     ,     **a_	)	->List[str]:







        '''simple docstring'''


        _a							=	{
            "num_train_timesteps": 1_1_0_0,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**a_	)
        return config






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=a_	)






      def 			lowerCamelCase__    (    self	)	->List[Any]:







        '''simple docstring'''


        for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,     [0.0_002, 0.002, 0.02]	):
          self.check_over_configs(beta_start=a_     ,     beta_end=a_	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=a_	)






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=a_	)






      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''simple docstring'''


        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(prediction_type="v_prediction"	)
        _a							=	scheduler_class(**a_	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter * scheduler.init_noise_sigma
        _a							=	sample.to(a_	)

        for i, t in enumerate(scheduler.timesteps	):
          _a							=	scheduler.scale_model_input(a_     ,     a_	)

          _a							=	model(a_     ,     a_	)

          _a							=	scheduler.step(a_     ,     a_     ,     a_	)
          _a							=	output.prev_sample

        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6_934E-07	) < 1E-2
          assert abs(result_mean.item() - 6.1_112E-10	) < 1E-3
        else:
          # CUDA
          assert abs(result_sum.item() - 4.693_428_650_170_972E-07	) < 1E-2
          assert abs(result_mean.item() - 0.0_002	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        if torch_device == "mps":
          return
        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config()
        _a							=	scheduler_class(**a_	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter * scheduler.init_noise_sigma
        _a							=	sample.to(a_	)

        for i, t in enumerate(scheduler.timesteps	):
          _a							=	scheduler.scale_model_input(a_     ,     a_	)

          _a							=	model(a_     ,     a_	)

          _a							=	scheduler.step(a_     ,     a_     ,     a_	)
          _a							=	output.prev_sample

        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 20.4_125	) < 1E-2
          assert abs(result_mean.item() - 0.0_266	) < 1E-3
        else:
          # CUDA
          assert abs(result_sum.item() - 20.4_125	) < 1E-2
          assert abs(result_mean.item() - 0.0_266	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        if torch_device == "mps":
          return
        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config()
        _a							=	scheduler_class(**a_	)

        scheduler.set_timesteps(self.num_inference_steps     ,     device=a_	)

        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter.to(a_	) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
          _a							=	scheduler.scale_model_input(a_     ,     a_	)

          _a							=	model(a_     ,     a_	)

          _a							=	scheduler.step(a_     ,     a_     ,     a_	)
          _a							=	output.prev_sample

        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        if str(a_	).startswith("cpu"	):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 20.4_125	) < 1E-2
          assert abs(result_mean.item() - 0.0_266	) < 1E-3
        else:
          # CUDA
          assert abs(result_sum.item() - 20.4_125	) < 1E-2
          assert abs(result_mean.item() - 0.0_266	) < 1E-3


