




"""simple docstring"""


from typing import Dict, Optional

import numpy as np

import datasets


A_      			=       '''\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'''

A_      			=       '''\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n'''

A_      			=       '''\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'''


def       UpperCAmelCase__ (snake_case__							:      Union[str, Any]	,     snake_case__							:      str	,     snake_case__							:      Dict	,     snake_case__							:      Any	,     snake_case__							:      Union[str, Any] = None	,     snake_case__							:      Tuple = False	,     ):






  if label_map is not None:
    for old_id, new_id in label_map.items():
      _snake_case							:	int			=						new_id

    # turn into Numpy arrays
  _snake_case							:	int			=						np.array(__SCREAMING_SNAKE_CASE					)
  _snake_case							:	Any			=						np.array(__SCREAMING_SNAKE_CASE					)

  if reduce_labels:
    _snake_case							:	str			=						2_55
    _snake_case							:	str			=						label - 1
    _snake_case							:	List[Any]			=						2_55

  _snake_case							:	Union[str, Any]			=						label != ignore_index
  _snake_case							:	Dict			=						np.not_equal(__SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE					)
  _snake_case							:	Tuple			=						pred_label[mask]
  _snake_case							:	Any			=						np.array(__SCREAMING_SNAKE_CASE					)[mask]

  _snake_case							:	List[str]			=						pred_label[pred_label == label]

  _snake_case							:	int			=						np.histogram(__SCREAMING_SNAKE_CASE	,     bins=__SCREAMING_SNAKE_CASE	,     range=(0, num_labels - 1)					)[0]
  _snake_case							:	Tuple			=						np.histogram(__SCREAMING_SNAKE_CASE	,     bins=__SCREAMING_SNAKE_CASE	,     range=(0, num_labels - 1)					)[0]
  _snake_case							:	List[Any]			=						np.histogram(__SCREAMING_SNAKE_CASE	,     bins=__SCREAMING_SNAKE_CASE	,     range=(0, num_labels - 1)					)[0]

  _snake_case							:	List[Any]			=						area_pred_label + area_label - area_intersect

  return area_intersect, area_union, area_pred_label, area_label


def       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      str	,     snake_case__							:      Optional[int]	,     snake_case__							:      Optional[Any]	,     snake_case__							:      int = None	,     snake_case__							:      Any = False	,     ):
  _snake_case							:	List[Any]			=						np.zeros((num_labels,)	,     dtype=np.floataa					)
  _snake_case							:	List[str]			=						np.zeros((num_labels,)	,     dtype=np.floataa					)
  _snake_case							:	int			=						np.zeros((num_labels,)	,     dtype=np.floataa					)
  _snake_case							:	int			=						np.zeros((num_labels,)	,     dtype=np.floataa					)
  for result, gt_seg_map in zip(__SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE					):
    _snake_case							:	int			=						intersect_and_union(
        __SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE					)
    total_area_intersect += area_intersect
    total_area_union += area_union
    total_area_pred_label += area_pred_label
    total_area_label += area_label
  return total_area_intersect, total_area_union, total_area_pred_label, total_area_label


def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      Dict	,     snake_case__							:      str	,     snake_case__							:      Tuple	,     snake_case__							:      int = None	,     snake_case__							:      int = None	,     snake_case__							:      Union[str, Any] = False	,     ):
  _snake_case							:	Tuple			=						total_intersect_and_union(
      __SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE					)

  # compute metrics
  _snake_case							:	Union[str, Any]			=						{}

  _snake_case							:	int			=						total_area_intersect.sum() / total_area_label.sum()
  _snake_case							:	str			=						total_area_intersect / total_area_union
  _snake_case							:	Union[str, Any]			=						total_area_intersect / total_area_label

  _snake_case							:	List[str]			=						np.nanmean(__SCREAMING_SNAKE_CASE					)
  _snake_case							:	str			=						np.nanmean(__SCREAMING_SNAKE_CASE					)
  _snake_case							:	Any			=						all_acc
  _snake_case							:	Union[str, Any]			=						iou
  _snake_case							:	Tuple			=						acc

  if nan_to_num is not None:
    _snake_case							:	str			=						{metric: np.nan_to_num(__SCREAMING_SNAKE_CASE	,     nan=__SCREAMING_SNAKE_CASE					) for metric, metric_value in metrics.items()}

  return metrics






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,    _KWARGS_DESCRIPTION				)
class       lowercase(  datasets.Metric				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   return datasets.MetricInfo(
       description=_DESCRIPTION,   citation=_CITATION,   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
           # 1st Seq - height dim, 2nd - width dim
           {
               """predictions""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16"""	)	)	),
               """references""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16"""	)	)	),
           }	),   reference_urls=[
           """https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"""
       ],   )


 def 				UpperCamelCase_     (       self:	Optional[int],   a_:	str,   a_:	Any,   a_:	List[Any],   a_:	str,   a_:	Optional[int] = None,   a_:	int = None,   a_:	Optional[int] = False,   ):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						mean_iou(
       results=lowercase_,   gt_seg_maps=lowercase_,   num_labels=lowercase_,   ignore_index=lowercase_,   nan_to_num=lowercase_,   label_map=lowercase_,   reduce_labels=lowercase_,   )
   return iou_result








"""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)


