




"""simple docstring"""


from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_      			=       logging.get_logger(__name__)






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["pixel_values"]


 def __init__(       self:	str,   a_:	bool = True,   a_:	Dict[str, int] = None,   a_:	PILImageResampling = PILImageResampling.BICUBIC,   a_:	bool = True,   a_:	Dict[str, int] = None,   a_:	bool = True,   a_:	Union[int, float] = 1 / 255,   a_:	bool = True,   a_:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN,   a_:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD,   **a_:	str,   ):




   '''simple docstring'''



   super().__init__(**a_	)
   _snake_case							:	Union[str, Any]			=						size if size is not None else {"""shortest_edge""": 224}
   _snake_case							:	str			=						get_size_dict(a_,   default_to_square=a_	)
   _snake_case							:	Optional[int]			=						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
   _snake_case							:	List[str]			=						get_size_dict(a_,   param_name="""crop_size"""	)

   _snake_case							:	int			=						do_resize
   _snake_case							:	str			=						size
   _snake_case							:	List[Any]			=						resample
   _snake_case							:	List[Any]			=						do_center_crop
   _snake_case							:	Any			=						crop_size
   _snake_case							:	Union[str, Any]			=						do_rescale
   _snake_case							:	str			=						rescale_factor
   _snake_case							:	Union[str, Any]			=						do_normalize
   _snake_case							:	Union[str, Any]			=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
   _snake_case							:	int			=						image_std if image_std is not None else IMAGENET_DEFAULT_STD


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	np.ndarray,   a_:	Dict[str, int],   a_:	PILImageResampling = PILImageResampling.BICUBIC,   a_:	Optional[Union[str, ChannelDimension]] = None,   **a_:	List[Any],   ):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						get_size_dict(a_,   default_to_square=a_	)
   # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
   if "shortest_edge" in size:
     _snake_case							:	List[str]			=						int((256 / 224) * size["""shortest_edge"""]	)
     _snake_case							:	Tuple			=						get_resize_output_image_size(a_,   size=a_,   default_to_square=a_	)
     _snake_case							:	Dict			=						{"""height""": output_size[0], """width""": output_size[1]}
   if "height" not in size_dict or "width" not in size_dict:
     raise ValueError(
         f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"	)
   return resize(
       a_,   size=(size_dict["""height"""], size_dict["""width"""]),   resample=a_,   data_format=a_,   **a_	)


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	np.ndarray,   a_:	Dict[str, int],   a_:	Optional[Union[str, ChannelDimension]] = None,   **a_:	Any,   ):




   '''simple docstring'''



   _snake_case							:	Dict			=						get_size_dict(a_	)
   if "height" not in size or "width" not in size:
     raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"	)
   return center_crop(a_,   size=(size["""height"""], size["""width"""]),   data_format=a_,   **a_	)


 def 				UpperCamelCase_     (       self:	Dict,   a_:	np.ndarray,   a_:	Union[int, float],   a_:	Optional[Union[str, ChannelDimension]] = None,   **a_:	str,   ):




   '''simple docstring'''



   return rescale(a_,   scale=a_,   data_format=a_,   **a_	)


 def 				UpperCamelCase_     (       self:	Optional[int],   a_:	np.ndarray,   a_:	Union[float, List[float]],   a_:	Union[float, List[float]],   a_:	Optional[Union[str, ChannelDimension]] = None,   **a_:	Tuple,   ):




   '''simple docstring'''



   return normalize(a_,   mean=a_,   std=a_,   data_format=a_,   **a_	)


 def 				UpperCamelCase_     (       self:	Dict,   a_:	ImageInput,   a_:	Optional[bool] = None,   a_:	Optional[Dict[str, int]] = None,   a_:	PILImageResampling = None,   a_:	Optional[bool] = None,   a_:	Optional[Dict[str, int]] = None,   a_:	Optional[bool] = None,   a_:	Optional[float] = None,   a_:	Optional[bool] = None,   a_:	Optional[Union[float, Iterable[float]]] = None,   a_:	Optional[Union[float, Iterable[float]]] = None,   a_:	Optional[TensorType] = None,   a_:	ChannelDimension = ChannelDimension.FIRST,   **a_:	List[str],   ):




   '''simple docstring'''



   _snake_case							:	Any			=						do_resize if do_resize is not None else self.do_resize
   _snake_case							:	Union[str, Any]			=						resample if resample is not None else self.resample
   _snake_case							:	Tuple			=						do_center_crop if do_center_crop is not None else self.do_center_crop
   _snake_case							:	Dict			=						do_rescale if do_rescale is not None else self.do_rescale
   _snake_case							:	Tuple			=						rescale_factor if rescale_factor is not None else self.rescale_factor
   _snake_case							:	List[Any]			=						do_normalize if do_normalize is not None else self.do_normalize
   _snake_case							:	Tuple			=						image_mean if image_mean is not None else self.image_mean
   _snake_case							:	int			=						image_std if image_std is not None else self.image_std

   _snake_case							:	Union[str, Any]			=						size if size is not None else self.size
   _snake_case							:	Tuple			=						get_size_dict(a_,   default_to_square=a_	)
   _snake_case							:	str			=						crop_size if crop_size is not None else self.crop_size
   _snake_case							:	int			=						get_size_dict(a_,   param_name="""crop_size"""	)

   _snake_case							:	Optional[Any]			=						make_list_of_images(a_	)

   if not valid_images(a_	):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray."""	)

   if do_resize and size is None:
     raise ValueError("""Size must be specified if do_resize is True."""	)

   if do_center_crop and crop_size is None:
     raise ValueError("""Crop size must be specified if do_center_crop is True."""	)

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""	)

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("""Image mean and std must be specified if do_normalize is True."""	)

   # All transformations expect numpy arrays.
   _snake_case							:	Any			=						[to_numpy_array(a_	) for image in images]

   if do_resize:
     _snake_case							:	List[Any]			=						[self.resize(a_,   a_,   a_	) for image in images]

   if do_center_crop:
     _snake_case							:	str			=						[self.center_crop(a_,   a_	) for image in images]

   if do_rescale:
     _snake_case							:	Optional[Any]			=						[self.rescale(a_,   a_	) for image in images]

   if do_normalize:
     _snake_case							:	Optional[int]			=						[self.normalize(a_,   a_,   a_	) for image in images]

   _snake_case							:	Tuple			=						[to_channel_dimension_format(a_,   a_	) for image in images]

   _snake_case							:	Tuple			=						{"""pixel_values""": images}
   return BatchFeature(data=a_,   tensor_type=a_	)








"""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       UpperCAmelCase__ ():
  """simple docstring"""

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

  return image


def       UpperCAmelCase__ (snake_case__							:      Any					):
  """simple docstring"""

  _snake_case							:	str			=						[]
  # 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       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      List[Any]	,     snake_case__							:      Tuple					):
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						dct.pop(snake_case__					)
  _snake_case							:	Optional[int]			=						val


def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      str					):
  """simple docstring"""

  for i in range(config.vision_config.num_hidden_layers					):
    # read in original q and v biases
    _snake_case							:	Optional[int]			=						state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias"					)
    _snake_case							:	Tuple			=						state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias"					)

    # next, set bias in the state dict
    _snake_case							:	List[str]			=						torch.cat((q_bias, torch.zeros_like(snake_case__	,     requires_grad=snake_case__					), v_bias)					)
    _snake_case							:	Dict			=						qkv_bias


def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      Union[str, Any]					):
  """simple docstring"""

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

  _snake_case							:	int			=						BlipaConfig(vision_config=snake_case__	,     text_config=snake_case__					)

  return config, image_size


@torch.no_grad()
def       UpperCAmelCase__ (snake_case__							:      int	,     snake_case__							:      int=None	,     snake_case__							:      str=False					):
  """simple docstring"""

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

  _snake_case							:	str			=						BlipaForConditionalGeneration(snake_case__					).eval()

  _snake_case							:	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"""),
  }

  _snake_case	,      _snake_case							:	List[Any]			=						model_name_to_original[model_name]

  # load original model
  print("""Loading original model..."""					)
  _snake_case							:	int			=						"""cuda""" if torch.cuda.is_available() else """cpu"""
  _snake_case	,      _snake_case	,      _snake_case							:	Any			=						load_model_and_preprocess(
      name=snake_case__	,     model_type=snake_case__	,     is_eval=snake_case__	,     device=snake_case__					)
  original_model.eval()
  print("""Done!"""					)

  # update state dict keys
  _snake_case							:	Any			=						original_model.state_dict()
  _snake_case							:	Dict			=						create_rename_keys(snake_case__					)
  for src, dest in rename_keys:
    rename_key(snake_case__	,     snake_case__	,     snake_case__					)

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

  # read in qv biases
  read_in_q_v_bias(snake_case__	,     snake_case__					)

  _snake_case	,      _snake_case							:	List[str]			=						hf_model.load_state_dict(snake_case__	,     strict=snake_case__					)
  assert len(snake_case__					) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  _snake_case							:	Any			=						load_demo_image()
  _snake_case							:	str			=						vis_processors["""eval"""](snake_case__					).unsqueeze(0					).to(snake_case__					)
  _snake_case							:	List[Any]			=						tokenizer(["""\n"""]	,     return_tensors="""pt"""					).input_ids.to(snake_case__					)

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

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

  original_model.to(snake_case__					)
  hf_model.to(snake_case__					)
  with torch.no_grad():
    if "opt" in model_name:
      _snake_case							:	str			=						original_model({"""image""": original_pixel_values, """text_input""": [""""""]}					).logits
      _snake_case							:	int			=						hf_model(snake_case__	,     snake_case__					).logits
    else:
      _snake_case							:	str			=						original_model(
          {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}					).logits
      _snake_case							:	Optional[int]			=						input_ids.masked_fill(input_ids == tokenizer.pad_token_id	,     -1_00					)
      _snake_case							:	Union[str, Any]			=						hf_model(snake_case__	,     snake_case__	,     labels=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":
    _snake_case							:	List[str]			=						torch.tensor(
        [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]	,     device=snake_case__					)
    assert torch.allclose(logits[0, :3, :3]	,     snake_case__	,     atol=1e-4					)
  elif model_name == "blip2-flan-t5-xl-coco":
    _snake_case							:	Union[str, Any]			=						torch.tensor(
        [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]	,     device=snake_case__					)
  else:
    # cast to same type
    _snake_case							:	int			=						logits.dtype
    assert torch.allclose(original_logits.to(snake_case__					)	,     snake_case__	,     atol=1e-2					)
  print("""Looks ok!"""					)

  print("""Generating a caption..."""					)
  _snake_case							:	Any			=						""""""
  _snake_case							:	str			=						tokenizer(snake_case__	,     return_tensors="""pt"""					).input_ids.to(snake_case__					)

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

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(snake_case__					)
    hf_model.save_pretrained(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__":
     A_      			=       argparse.ArgumentParser()
     A_      			=       [
         '''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''',
     )

     A_      			=       parser.parse_args()

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


