


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__a 					=   '''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

__a 					=   '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

__a 					=   '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''







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


      def 							__lowerCamelCase      (				self		):
            return datasets.MetricInfo(
                description=_DESCRIPTION				,    citation=_CITATION				,    inputs_description=_KWARGS_DESCRIPTION				,    features=datasets.Features(
                    {
                        '''predictions''': datasets.Sequence(datasets.Value('''string'''				,    id='''token'''		)				,    id='''sequence'''		),
                        '''references''': datasets.Sequence(
                            datasets.Sequence(datasets.Value('''string'''				,    id='''token'''		)				,    id='''sequence'''		)				,    id='''references'''		),
                    }		)				,    )






      def 							__lowerCamelCase      (				self				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__ = 1				,    SCREAMING_SNAKE_CASE__ = 4				,    ):
            return {
                "google_bleu": gleu_score.corpus_gleu(
                    list_of_references=A_				,    hypotheses=A_				,    min_len=A_				,    max_len=A_		)
            }





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		(		)					-> int:


     """simple docstring"""


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

     return image






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			[]
     # 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		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			dct.pop(_lowercase )
     __UpperCamelCase =			val






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


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

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






def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


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

     __UpperCamelCase =			BlipaConfig(vision_config=_lowercase					,  text_config=_lowercase )

     return config, image_size






@torch.no_grad()
def 		_A		(		_lowercase					,  _lowercase=None					,  _lowercase=False )					-> Union[str, Any]:


     """simple docstring"""


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

     __UpperCamelCase =			BlipaForConditionalGeneration(_lowercase ).eval()

     __UpperCamelCase =			{
         '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'),
     }

     __UpperCamelCase,    __UpperCamelCase =			model_name_to_original[model_name]

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

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

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

     # read in qv biases
     read_in_q_v_bias(_lowercase					,  _lowercase )

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

     __UpperCamelCase =			load_demo_image()
     __UpperCamelCase =			vis_processors['eval'](_lowercase ).unsqueeze(0 ).to(_lowercase )
     __UpperCamelCase =			tokenizer(['\n']					,  return_tensors='pt' ).input_ids.to(_lowercase )

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

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

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

     print('Generating a caption...' )
     __UpperCamelCase =			''
     __UpperCamelCase =			tokenizer(_lowercase					,  return_tensors='pt' ).input_ids.to(_lowercase )

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

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

     if push_to_hub:
          processor.push_to_hub(f'''nielsr/{model_name}''' )
          hf_model.push_to_hub(f'''nielsr/{model_name}''' )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    __snake_case   							=						[
        '''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''',
    )

    __snake_case   							=						parser.parse_args()

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


