


import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCAmelCase	: str  				='\\n@inproceedings{popovic-2015-chrf,\n    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n    month = sep,\n    year = "2015",\n    address = "Lisbon, Portugal",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W15-3049",\n    doi = "10.18653/v1/W15-3049",\n    pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n    title = "chr{F}++: words helping character n-grams",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Second Conference on Machine Translation",\n    month = sep,\n    year = "2017",\n    address = "Copenhagen, Denmark",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W17-4770",\n    doi = "10.18653/v1/W17-4770",\n    pages = "612--618",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

__lowerCAmelCase	: Optional[Any]  				='\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

__lowerCAmelCase	: str  				='\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION       )
class        _lowercase      (			datasets.Metric       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Union[str, Any]	)		->			List[Any]:
										if version.parse(scb.__version__	) < version.parse('''1.4.12'''	):
																raise ImportWarning(
																    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
																    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''	)
										return datasets.MetricInfo(
										    description=_DESCRIPTION			,			citation=_CITATION			,			homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf'''			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
										        {
										            '''predictions''': datasets.Value('''string'''			,			id='''sequence'''	),
										            '''references''': datasets.Sequence(datasets.Value('''string'''			,			id='''sequence'''	)			,			id='''references'''	),
										        }	)			,			codebase_urls=['''https://github.com/mjpost/sacreBLEU#chrf--chrf''']			,			reference_urls=[
										        '''https://github.com/m-popovic/chrF''',
										    ]			,			)



				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:Optional[int]			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:int = CHRF.CHAR_ORDER			,			lowerCAmelCase__			:int = CHRF.WORD_ORDER			,			lowerCAmelCase__			:int = CHRF.BETA			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = False			,			)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     str					=		len(references[0]	)
										if any(len(lowerCAmelCase__	) != references_per_prediction for refs in references	):
																raise ValueError('''Sacrebleu requires the same number of references for each prediction'''	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[[refs[i] for refs in references] for i in range(lowerCAmelCase__	)]

										__SCREAMING_SNAKE_CASE						:     Dict					=		CHRF(lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		sb_chrf.corpus_score(lowerCAmelCase__			,			lowerCAmelCase__	)

										return {
										    "score": output.score,
										    "char_order": output.char_order,
										    "word_order": output.word_order,
										    "beta": output.beta,
										}




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							(      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		'''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
						__SCREAMING_SNAKE_CASE						:     List[str]					=		Image.open(requests.get(lowercase__    ,       stream=lowercase__      ).raw      ).convert('''RGB'''      )

						return image


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		[]
						# 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							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		dct.pop(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		val


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						for i in range(config.vision_config.num_hidden_layers      ):
												# read in original q and v biases
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''      )
												__SCREAMING_SNAKE_CASE						:     int					=		state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''      )

												# next, set bias in the state dict
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.cat((q_bias, torch.zeros_like(lowercase__    ,       requires_grad=lowercase__      ), v_bias)      )
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		qkv_bias


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		364 if '''coco''' in model_name else 224
						__SCREAMING_SNAKE_CASE						:     List[str]					=		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:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		OPTConfig.from_pretrained('''facebook/opt-2.7b'''    ,       eos_token_id=lowercase__      ).to_dict()
						elif "opt-6.7b" in model_name:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		OPTConfig.from_pretrained('''facebook/opt-6.7b'''    ,       eos_token_id=lowercase__      ).to_dict()
						elif "t5-xl" in model_name:
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		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						:     Union[str, Any]					=		TaConfig.from_pretrained('''google/flan-t5-xxl'''    ,       dense_act_fn='''gelu'''    ,       bos_token_id=1      ).to_dict()

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		BlipaConfig(vision_config=lowercase__    ,       text_config=lowercase__      )

						return config, image_size


@torch.no_grad()
def        _UpperCamelCase							(      lowercase__    ,       lowercase__=None    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		(
						    AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''      )
						    if '''opt''' in model_name
						    else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''      )
						)
						__SCREAMING_SNAKE_CASE						:     str					=		tokenizer('''\n'''    ,       add_special_tokens=lowercase__      ).input_ids[0]
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		get_blipa_config(lowercase__    ,       eos_token_id=lowercase__      )

						__SCREAMING_SNAKE_CASE						:     int					=		BlipaForConditionalGeneration(lowercase__      ).eval()

						__SCREAMING_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'''),
						}

						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		model_name_to_original[model_name]

						# load original model
						print('''Loading original model...'''      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		'''cuda''' if torch.cuda.is_available() else '''cpu'''
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		load_model_and_preprocess(
						    name=lowercase__    ,       model_type=lowercase__    ,       is_eval=lowercase__    ,       device=lowercase__      )
						original_model.eval()
						print('''Done!'''      )

						# update state dict keys
						__SCREAMING_SNAKE_CASE						:     List[str]					=		original_model.state_dict()
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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():
												__SCREAMING_SNAKE_CASE						:     Tuple					=		state_dict.pop(lowercase__      )
												if key.startswith('''Qformer.bert'''      ):
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		key.replace('''Qformer.bert'''    ,       '''qformer'''      )
												if "attention.self" in key:
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		key.replace('''self'''    ,       '''attention'''      )
												if "opt_proj" in key:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		key.replace('''opt_proj'''    ,       '''language_projection'''      )
												if "t5_proj" in key:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		key.replace('''t5_proj'''    ,       '''language_projection'''      )
												if key.startswith('''opt'''      ):
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		key.replace('''opt'''    ,       '''language'''      )
												if key.startswith('''t5'''      ):
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		key.replace('''t5'''    ,       '''language'''      )
												__SCREAMING_SNAKE_CASE						:     Tuple					=		val

						# read in qv biases
						read_in_q_v_bias(lowercase__    ,       lowercase__      )

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

						__SCREAMING_SNAKE_CASE						:     List[str]					=		load_demo_image()
						__SCREAMING_SNAKE_CASE						:     Any					=		vis_processors['''eval'''](lowercase__      ).unsqueeze(0      ).to(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		tokenizer(['''\n''']    ,       return_tensors='''pt'''      ).input_ids.to(lowercase__      )

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

						print('''Generating a caption...'''      )
						__SCREAMING_SNAKE_CASE						:     Any					=		''''''
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tokenizer(lowercase__    ,       return_tensors='''pt'''      ).input_ids.to(lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		original_model.generate({'''image''': original_pixel_values}      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		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__      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		input_ids.shape[1]
						__SCREAMING_SNAKE_CASE						:     Any					=		processor.batch_decode(outputs[:, prompt_length:]    ,       skip_special_tokens=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[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__":
						__lowerCAmelCase	: List[str]  				=argparse.ArgumentParser()
						__lowerCAmelCase	: Tuple  				=[
						    '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',
						)

						__lowerCAmelCase	: List[Any]  				=parser.parse_args()

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