



import os

import jsonlines
import numpy as np
from tqdm import tqdm


A__	      =       20_48
A__	      =       40_96
A__	      =       42
A__	      =       os.environ.pop("""PROCESS_TRAIN""", """false""")
A__	      =       {"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					def choose_first(snake_case	,						snake_case=False			):
										assert isinstance(snake_case	,						snake_case			)
										if len(snake_case			) == 1:
															_lowerCAmelCase						=    answer[0]
															return {k: [answer[k]] for k in answer} if is_long_answer else answer
										for a in answer:
															if is_long_answer:
																				_lowerCAmelCase						=    {k: [a[k]] for k in a}
															if len(a["""start_token"""]			) > 0:
																				break
										return a

					_lowerCAmelCase						=    {"""id""": example["""id"""]}
					_lowerCAmelCase						=    example["""annotations"""]
					_lowerCAmelCase						=    annotation["""yes_no_answer"""]
					if 0 in yes_no_answer or 1 in yes_no_answer:
										_lowerCAmelCase						=    ["""yes"""] if 1 in yes_no_answer else ["""no"""]
										_lowerCAmelCase						=    _lowerCAmelCase						=    []
										_lowerCAmelCase						=    _lowerCAmelCase						=    []
										_lowerCAmelCase						=    ["""<cls>"""]
					else:
										_lowerCAmelCase						=    ["""short"""]
										_lowerCAmelCase						=    choose_first(annotation["""short_answers"""]			)
										if len(out["""start_token"""]			) == 0:
															# answer will be long if short is not available
															_lowerCAmelCase						=    ["""long"""]
															_lowerCAmelCase						=    choose_first(annotation["""long_answer"""]	,						is_long_answer=snake_case			)
															_lowerCAmelCase						=    []
										answer.update(snake_case			)

					# disregard some samples
					if len(answer["""start_token"""]			) > 1 or answer["start_token"] == answer["end_token"]:
										_lowerCAmelCase						=    True
					else:
										_lowerCAmelCase						=    False

					_lowerCAmelCase						=    ["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
					if not all(isinstance(answer[k]	,						snake_case			) for k in cols			):
										raise ValueError("""Issue in ID"""	,						example["""id"""]			)

					return answer


def 		_UpperCAmelCase				(      snake_case	,						snake_case=False			):





					"""simple docstring"""

					_lowerCAmelCase						=    _get_single_answer(snake_case			)
					# bytes are of no use
					del answer["start_byte"]
					del answer["end_byte"]

					# handle yes_no answers explicitly
					if answer["category"][0] in ["yes", "no"]:  # category is list with one element
										_lowerCAmelCase						=    example["""document"""]["""tokens"""]
										_lowerCAmelCase						=    []
										for i in range(len(doc["""token"""]			)			):
															if not doc["is_html"][i]:
																				context.append(doc["""token"""][i]			)
										return {
										    "context": " ".join(snake_case			),
										    "answer": {
										        "start_token": -1_00,  # ignore index in cross-entropy
										        "end_token": -1_00,  # ignore index in cross-entropy
										        "category": answer["category"],
										        "span": answer["category"],  # extra
										    },
										}

					# later, help in removing all no answers
					if answer["start_token"] == [-1]:
										return {
										    "context": "None",
										    "answer": {
										        "start_token": -1,
										        "end_token": -1,
										        "category": "null",
										        "span": "None",  # extra
										    },
										}

					# handling normal samples

					_lowerCAmelCase						=    ["""start_token""", """end_token"""]
					answer.update({k: answer[k][0] if len(answer[k]			) > 0 else answer[k] for k in cols}			)  # e.g. [10] == 10

					_lowerCAmelCase						=    example["""document"""]["""tokens"""]
					_lowerCAmelCase						=    answer["""start_token"""]
					_lowerCAmelCase						=    answer["""end_token"""]

					_lowerCAmelCase						=    []
					for i in range(len(doc["""token"""]			)			):
										if not doc["is_html"][i]:
															context.append(doc["""token"""][i]			)
										else:
															if answer["start_token"] > i:
																				start_token -= 1
															if answer["end_token"] > i:
																				end_token -= 1
					_lowerCAmelCase						=    """ """.join(context[start_token:end_token]			)

					# checking above code
					if assertion:
										_lowerCAmelCase						=    doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
										_lowerCAmelCase						=    doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
										_lowerCAmelCase						=    """ """.join([old[i] for i in range(len(snake_case			)			) if not is_html[i]]			)
										if new != old:
															print("""ID:"""	,						example["""id"""]			)
															print("""New:"""	,						snake_case	,						end="""\n"""			)
															print("""Old:"""	,						snake_case	,						end="""\n\n"""			)

					return {
					    "context": " ".join(snake_case			),
					    "answer": {
					        "start_token": start_token,
					        "end_token": end_token - 1,  # this makes it inclusive
					        "category": answer["category"],  # either long or short
					        "span": new,  # extra
					    },
					}


def 		_UpperCAmelCase				(      snake_case	,						snake_case	,						snake_case=20_48	,						snake_case=40_96	,						snake_case=True			):





					"""simple docstring"""

					_lowerCAmelCase						=    get_context_and_ans(snake_case	,						assertion=snake_case			)
					_lowerCAmelCase						=    out["""answer"""]

					# later, removing these samples
					if answer["start_token"] == -1:
										return {
										    "example_id": example["id"],
										    "input_ids": [[-1]],
										    "labels": {
										        "start_token": [-1],
										        "end_token": [-1],
										        "category": ["null"],
										    },
										}

					_lowerCAmelCase						=    tokenizer(example["""question"""]["""text"""]	,						out["""context"""]			).input_ids
					_lowerCAmelCase						=    input_ids.index(tokenizer.sep_token_id			) + 1

					# return yes/no
					if answer["category"][0] in ["yes", "no"]:  # category is list with one element
										_lowerCAmelCase						=    []
										_lowerCAmelCase						=    []
										_lowerCAmelCase						=    input_ids[:q_len]
										_lowerCAmelCase						=    range(snake_case	,						len(snake_case			)	,						max_length - doc_stride			)
										for i in doc_start_indices:
															_lowerCAmelCase						=    i + max_length - q_len
															_lowerCAmelCase						=    input_ids[i:end_index]
															inputs.append(q_indices + slice			)
															category.append(answer["""category"""][0]			)
															if slice[-1] == tokenizer.sep_token_id:
																				break

										return {
										    "example_id": example["id"],
										    "input_ids": inputs,
										    "labels": {
										        "start_token": [-1_00] * len(snake_case			),
										        "end_token": [-1_00] * len(snake_case			),
										        "category": category,
										    },
										}

					_lowerCAmelCase						=    out["""context"""].split()
					_lowerCAmelCase						=    splitted_context[answer["""end_token"""]]
					_lowerCAmelCase						=    len(
					    tokenizer(
					        """ """.join(splitted_context[: answer["""start_token"""]]			)	,						add_special_tokens=snake_case	,						).input_ids			)
					_lowerCAmelCase						=    len(
					    tokenizer(""" """.join(splitted_context[: answer["""end_token"""]]			)	,						add_special_tokens=snake_case			).input_ids			)

					answer["start_token"] += q_len
					answer["end_token"] += q_len

					# fixing end token
					_lowerCAmelCase						=    len(tokenizer(snake_case	,						add_special_tokens=snake_case			).input_ids			)
					if num_sub_tokens > 1:
										answer["end_token"] += num_sub_tokens - 1

					_lowerCAmelCase						=    input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
					_lowerCAmelCase						=    answer["""start_token"""]
					_lowerCAmelCase						=    answer["""end_token"""]

					if assertion:
										_lowerCAmelCase						=    tokenizer.decode(snake_case			)
										if answer["span"] != new:
															print("""ISSUE IN TOKENIZATION"""			)
															print("""OLD:"""	,						answer["""span"""]			)
															print("""NEW:"""	,						snake_case	,						end="""\n\n"""			)

					if len(snake_case			) <= max_length:
										return {
										    "example_id": example["id"],
										    "input_ids": [input_ids],
										    "labels": {
										        "start_token": [answer["start_token"]],
										        "end_token": [answer["end_token"]],
										        "category": answer["category"],
										    },
										}

					_lowerCAmelCase						=    input_ids[:q_len]
					_lowerCAmelCase						=    range(snake_case	,						len(snake_case			)	,						max_length - doc_stride			)

					_lowerCAmelCase						=    []
					_lowerCAmelCase						=    []
					_lowerCAmelCase						=    []
					_lowerCAmelCase						=    []  # null, yes, no, long, short
					for i in doc_start_indices:
										_lowerCAmelCase						=    i + max_length - q_len
										_lowerCAmelCase						=    input_ids[i:end_index]
										inputs.append(q_indices + slice			)
										assert len(inputs[-1]			) <= max_length, "Issue in truncating length"

										if start_token >= i and end_token <= end_index - 1:
															_lowerCAmelCase						=    start_token - i + q_len
															_lowerCAmelCase						=    end_token - i + q_len
															answers_category.append(answer["""category"""][0]			)  # ["short"] -> "short"
										else:
															_lowerCAmelCase						=    -1_00
															_lowerCAmelCase						=    -1_00
															answers_category.append("""null"""			)
										_lowerCAmelCase						=    inputs[-1][start_token : end_token + 1]

										answers_start_token.append(snake_case			)
										answers_end_token.append(snake_case			)
										if assertion:
															if new != old and new != [tokenizer.cls_token_id]:
																				print("""ISSUE in strided for ID:"""	,						example["""id"""]			)
																				print("""New:"""	,						tokenizer.decode(snake_case			)			)
																				print("""Old:"""	,						tokenizer.decode(snake_case			)	,						end="""\n\n"""			)
										if slice[-1] == tokenizer.sep_token_id:
															break

					return {
					    "example_id": example["id"],
					    "input_ids": inputs,
					    "labels": {
					        "start_token": answers_start_token,
					        "end_token": answers_end_token,
					        "category": answers_category,
					    },
					}


def 		_UpperCAmelCase				(      snake_case	,						snake_case	,						snake_case=20_48	,						snake_case=40_96	,						snake_case=False			):





					"""simple docstring"""

					_lowerCAmelCase						=    get_strided_contexts_and_ans(
					    snake_case	,						snake_case	,						doc_stride=snake_case	,						max_length=snake_case	,						assertion=snake_case	,						)

					return example


def 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

					with jsonlines.open(snake_case	,						"""a"""			) as writer:
										for example in tqdm(snake_case	,						total=len(snake_case			)	,						desc="""Saving samples ... """			):
															_lowerCAmelCase						=    example["""labels"""]
															for ids, start, end, cat in zip(
															    example["""input_ids"""]	,						labels["""start_token"""]	,						labels["""end_token"""]	,						labels["""category"""]	,						):
																				if start == -1 and end == -1:
																									continue  # leave waste samples with no answer
																				if cat == "null" and np.random.rand() < 0.6:
																									continue  # removing 50 % samples
																				writer.write(
																				    {
																				        """input_ids""": ids,
																				        """start_token""": start,
																				        """end_token""": end,
																				        """category""": CATEGORY_MAPPING[cat],
																				    }			)


if __name__ == "__main__":
	from datasets import load_dataset

	from transformers import BigBirdTokenizer

	A__	      =       load_dataset("""natural_questions""")
	A__	      =       BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

	A__	      =       data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

	A__	      =       {
	    """tokenizer""": tokenizer,
	    """doc_stride""": DOC_STRIDE,
	    """max_length""": MAX_LENGTH,
	    """assertion""": False,
	}
	A__	      =       data.map(prepare_inputs, fn_kwargs=fn_kwargs)
	A__	      =       data.remove_columns(["""annotations""", """document""", """id""", """question"""])
	print(data)

	np.random.seed(SEED)
	A__	      =       """nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
	save_to_disk(data, file_name=cache_file_name)








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(      )		->       Optional[int]:
					"""simple docstring"""





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

					return image






def     a(      A       :    Tuple       )		->       List[str]:
					"""simple docstring"""





					a					     =   []
					# fmt: off

					# vision encoder
					rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")       )
					rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")       )
					rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")       )
					rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")       )
					rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")       )
					rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")       )

					for i in range(config.vision_config.num_hidden_layers       ):
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')       )

					# QFormer
					rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight")       )
					rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")       )

					# fmt: on
					return rename_keys






def     a(      A       :    str ,     A       :    Tuple ,     A       :    Optional[int]       )		->       int:
					"""simple docstring"""





					a					     =   dct.pop(A       )
					a					     =   val






def     a(      A       :    Union[str, Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





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

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






def     a(      A       :    Union[str, Any] ,     A       :    Tuple       )		->       str:
					"""simple docstring"""





					a					     =   364 if "coco" in model_name else 224
					a					     =   BlipaVisionConfig(image_size=A       ).to_dict()

					# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
					# seems like flan-T5 models don't have bos_token_id properly set?
					if "opt-2.7b" in model_name:
										a					     =   OPTConfig.from_pretrained("facebook/opt-2.7b" ,     eos_token_id=A       ).to_dict()
					elif "opt-6.7b" in model_name:
										a					     =   OPTConfig.from_pretrained("facebook/opt-6.7b" ,     eos_token_id=A       ).to_dict()
					elif "t5-xl" in model_name:
										a					     =   TaConfig.from_pretrained("google/flan-t5-xl" ,     dense_act_fn="gelu" ,     bos_token_id=1       ).to_dict()
					elif "t5-xxl" in model_name:
										a					     =   TaConfig.from_pretrained("google/flan-t5-xxl" ,     dense_act_fn="gelu" ,     bos_token_id=1       ).to_dict()

					a					     =   BlipaConfig(vision_config=A ,     text_config=A       )

					return config, image_size






@torch.no_grad()
def     a(      A       :    Optional[int] ,     A       :    List[str]=None ,     A       :    Optional[int]=False       )		->       Any:
					"""simple docstring"""





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

					a					     =   BlipaForConditionalGeneration(A       ).eval()

					a					     =   {
					    "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"),
					}

					a     ,				a					     =   model_name_to_original[model_name]

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

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

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

					# read in qv biases
					read_in_q_v_bias(A ,     A       )

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

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

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

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

					original_model.to(A       )
					hf_model.to(A       )
					with torch.no_grad():
										if "opt" in model_name:
															a					     =   original_model({"image": original_pixel_values, "text_input": [""]}       ).logits
															a					     =   hf_model(A ,     A       ).logits
										else:
															a					     =   original_model(
															    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}       ).logits
															a					     =   input_ids.masked_fill(input_ids == tokenizer.pad_token_id ,     -100       )
															a					     =   hf_model(A ,     A ,     labels=A       ).logits

					assert original_logits.shape == logits.shape
					print("First values of original logits:" ,     original_logits[0, :3, :3]       )
					print("First values of HF logits:" ,     logits[0, :3, :3]       )

					# assert values
					if model_name == "blip2-flan-t5-xl":
										a					     =   torch.tensor(
										    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] ,     device=A       )
										assert torch.allclose(logits[0, :3, :3] ,     A ,     atol=1e-4       )
					elif model_name == "blip2-flan-t5-xl-coco":
										a					     =   torch.tensor(
										    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] ,     device=A       )
					else:
										# cast to same type
										a					     =   logits.dtype
										assert torch.allclose(original_logits.to(A       ) ,     A ,     atol=1e-2       )
					print("Looks ok!"       )

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

					a					     =   original_model.generate({"image": original_pixel_values}       )
					a					     =   hf_model.generate(
					    A ,     A ,     do_sample=A ,     num_beams=5 ,     max_length=30 ,     min_length=1 ,     top_p=0.9 ,     repetition_penalty=1.0 ,     length_penalty=1.0 ,     temperature=1 ,     )
					print("Original generation:" ,     A       )
					a					     =   input_ids.shape[1]
					a					     =   processor.batch_decode(outputs[:, prompt_length:] ,     skip_special_tokens=A       )
					a					     =   [text.strip() for text in output_text]
					print("HF generation:" ,     A       )

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

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


if __name__ == "__main__":
				_lowercase: Dict      			=		argparse.ArgumentParser()
				_lowercase: 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",
				)

				_lowercase: Any      			=		parser.parse_args()

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