

'''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__       (  ):




     """simple docstring"""


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

     return image







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      []
     # 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__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      dct.pop(a__						)
     __SCREAMING_SNAKE_CASE     =      val







def    a__       (  a__      ,    a__						):




     """simple docstring"""


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

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







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      3_64 if """coco""" in model_name else 2_24
     __SCREAMING_SNAKE_CASE     =      BlipaVisionConfig(image_size=a__						).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:
          __SCREAMING_SNAKE_CASE     =      OPTConfig.from_pretrained("""facebook/opt-2.7b"""      ,    eos_token_id=a__						).to_dict()
     elif "opt-6.7b" in model_name:
          __SCREAMING_SNAKE_CASE     =      OPTConfig.from_pretrained("""facebook/opt-6.7b"""      ,    eos_token_id=a__						).to_dict()
     elif "t5-xl" in model_name:
          __SCREAMING_SNAKE_CASE     =      TaConfig.from_pretrained("""google/flan-t5-xl"""      ,    dense_act_fn="""gelu"""      ,    bos_token_id=1						).to_dict()
     elif "t5-xxl" in model_name:
          __SCREAMING_SNAKE_CASE     =      TaConfig.from_pretrained("""google/flan-t5-xxl"""      ,    dense_act_fn="""gelu"""      ,    bos_token_id=1						).to_dict()

     __SCREAMING_SNAKE_CASE     =      BlipaConfig(vision_config=a__      ,    text_config=a__						)

     return config, image_size







@torch.no_grad()
def    a__       (  a__      ,    a__=None      ,    a__=False						):




     """simple docstring"""


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

     __SCREAMING_SNAKE_CASE     =      BlipaForConditionalGeneration(a__						).eval()

     __SCREAMING_SNAKE_CASE     =      {
         """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"""),
     }

     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      model_name_to_original[model_name]

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

     # update state dict keys
     __SCREAMING_SNAKE_CASE     =      original_model.state_dict()
     __SCREAMING_SNAKE_CASE     =      create_rename_keys(a__						)
     for src, dest in rename_keys:
          rename_key(a__      ,    a__      ,    a__						)

     # some keys can be renamed efficiently
     for key, val in state_dict.copy().items():
          __SCREAMING_SNAKE_CASE     =      state_dict.pop(a__						)
          if key.startswith("""Qformer.bert"""						):
               __SCREAMING_SNAKE_CASE     =      key.replace("""Qformer.bert"""      ,    """qformer"""						)
          if "attention.self" in key:
               __SCREAMING_SNAKE_CASE     =      key.replace("""self"""      ,    """attention"""						)
          if "opt_proj" in key:
               __SCREAMING_SNAKE_CASE     =      key.replace("""opt_proj"""      ,    """language_projection"""						)
          if "t5_proj" in key:
               __SCREAMING_SNAKE_CASE     =      key.replace("""t5_proj"""      ,    """language_projection"""						)
          if key.startswith("""opt"""						):
               __SCREAMING_SNAKE_CASE     =      key.replace("""opt"""      ,    """language"""						)
          if key.startswith("""t5"""						):
               __SCREAMING_SNAKE_CASE     =      key.replace("""t5"""      ,    """language"""						)
          __SCREAMING_SNAKE_CASE     =      val

     # read in qv biases
     read_in_q_v_bias(a__      ,    a__						)

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

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

     # create processor
     __SCREAMING_SNAKE_CASE     =      BlipImageProcessor(
         size={"""height""": image_size, """width""": image_size}      ,    image_mean=a__      ,    image_std=a__						)
     __SCREAMING_SNAKE_CASE     =      BlipaProcessor(image_processor=a__      ,    tokenizer=a__						)
     __SCREAMING_SNAKE_CASE     =      processor(images=a__      ,    return_tensors="""pt"""						).pixel_values.to(a__						)

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

     original_model.to(a__						)
     hf_model.to(a__						)
     with torch.no_grad():
          if "opt" in model_name:
               __SCREAMING_SNAKE_CASE     =      original_model({"""image""": original_pixel_values, """text_input""": [""""""]}						).logits
               __SCREAMING_SNAKE_CASE     =      hf_model(a__      ,    a__						).logits
          else:
               __SCREAMING_SNAKE_CASE     =      original_model(
                   {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}						).logits
               __SCREAMING_SNAKE_CASE     =      input_ids.masked_fill(input_ids == tokenizer.pad_token_id      ,    -1_00						)
               __SCREAMING_SNAKE_CASE     =      hf_model(a__      ,    a__      ,    labels=a__						).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":
          __SCREAMING_SNAKE_CASE     =      torch.tensor(
              [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]      ,    device=a__						)
          assert torch.allclose(logits[0, :3, :3]      ,    a__      ,    atol=1E-4						)
     elif model_name == "blip2-flan-t5-xl-coco":
          __SCREAMING_SNAKE_CASE     =      torch.tensor(
              [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]      ,    device=a__						)
     else:
          # cast to same type
          __SCREAMING_SNAKE_CASE     =      logits.dtype
          assert torch.allclose(original_logits.to(a__						)      ,    a__      ,    atol=1E-2						)
     print("""Looks ok!"""						)

     print("""Generating a caption..."""						)
     __SCREAMING_SNAKE_CASE     =      """"""
     __SCREAMING_SNAKE_CASE     =      tokenizer(a__      ,    return_tensors="""pt"""						).input_ids.to(a__						)

     __SCREAMING_SNAKE_CASE     =      original_model.generate({"""image""": original_pixel_values}						)
     __SCREAMING_SNAKE_CASE     =      hf_model.generate(
         a__      ,    a__      ,    do_sample=a__      ,    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:"""      ,    a__						)
     __SCREAMING_SNAKE_CASE     =      input_ids.shape[1]
     __SCREAMING_SNAKE_CASE     =      processor.batch_decode(outputs[:, prompt_length:]      ,    skip_special_tokens=a__						)
     __SCREAMING_SNAKE_CASE     =      [text.strip() for text in output_text]
     print("""HF generation:"""      ,    a__						)

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

     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 :       str			   =     argparse.ArgumentParser()
  UpperCAmelCase :       str			   =     [
      '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 :       Optional[int]			   =     parser.parse_args()

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









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :       Union[str, Any]			   =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase :       Union[str, Any]			   =     [
      'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SEWForCTC',
      'SEWForSequenceClassification',
      'SEWModel',
      'SEWPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase :       int			   =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






