


import os

import jsonlines
import numpy as np
from tqdm import tqdm


_lowercase							:   int						=						2048
_lowercase							:   int						=						4096
_lowercase							:   int						=						42
_lowercase							:   int						=						os.environ.pop("PROCESS_TRAIN", "false")
_lowercase							:   Tuple						=						{"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}





def 	_lowerCAmelCase					( UpperCamelCase__:  Any    )			-> List[str]:





	"""simple docstring"""







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

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

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

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

	return answer





def 	_lowerCAmelCase					( UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[Any]=False    )			-> Dict:





	"""simple docstring"""







	A             =					_get_single_answer(UpperCamelCase__    )
	# 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
		A             =					example["""document"""]["""tokens"""]
		A             =					[]
		for i in range(len(doc["""token"""]    )    ):
			if not doc["is_html"][i]:
				context.append(doc["""token"""][i]    )
		return {
		    "context": " ".join(UpperCamelCase__    ),
		    "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

	A             =					["""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

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

	A             =					[]
	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
	A             =					""" """.join(context[start_token:end_token]    )

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

	return {
	    "context": " ".join(UpperCamelCase__    ),
	    "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 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  str				,       UpperCamelCase__:  Tuple=20_48				,       UpperCamelCase__:  int=40_96				,       UpperCamelCase__:  str=True    )			-> Union[str, Any]:





	"""simple docstring"""







	A             =					get_context_and_ans(UpperCamelCase__				,       assertion=UpperCamelCase__    )
	A             =					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"],
		    },
		}

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

	# return yes/no
	if answer["category"][0] in ["yes", "no"]:  # category is list with one element
		A             =					[]
		A             =					[]
		A             =					input_ids[:q_len]
		A             =					range(UpperCamelCase__				,       len(UpperCamelCase__    )				,       max_length - doc_stride    )
		for i in doc_start_indices:
			A             =					i + max_length - q_len
			A             =					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(UpperCamelCase__    ),
		        "end_token": [-1_00] * len(UpperCamelCase__    ),
		        "category": category,
		    },
		}

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

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

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

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

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

	if len(UpperCamelCase__    ) <= 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"],
		    },
		}

	A             =					input_ids[:q_len]
	A             =					range(UpperCamelCase__				,       len(UpperCamelCase__    )				,       max_length - doc_stride    )

	A             =					[]
	A             =					[]
	A             =					[]
	A             =					[]  # null, yes, no, long, short
	for i in doc_start_indices:
		A             =					i + max_length - q_len
		A             =					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:
			A             =					start_token - i + q_len
			A             =					end_token - i + q_len
			answers_category.append(answer["""category"""][0]    )  # ["short"] -> "short"
		else:
			A             =					-1_00
			A             =					-1_00
			answers_category.append("""null"""    )
		A             =					inputs[-1][start_token : end_token + 1]

		answers_start_token.append(UpperCamelCase__    )
		answers_end_token.append(UpperCamelCase__    )
		if assertion:
			if new != old and new != [tokenizer.cls_token_id]:
				print("""ISSUE in strided for ID:"""				,       example["""id"""]    )
				print("""New:"""				,       tokenizer.decode(UpperCamelCase__    )    )
				print("""Old:"""				,       tokenizer.decode(UpperCamelCase__    )				,       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 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  str				,       UpperCamelCase__:  int=20_48				,       UpperCamelCase__:  Dict=40_96				,       UpperCamelCase__:  Optional[int]=False    )			-> Any:





	"""simple docstring"""







	A             =					get_strided_contexts_and_ans(
	    UpperCamelCase__				,       UpperCamelCase__				,       doc_stride=UpperCamelCase__				,       max_length=UpperCamelCase__				,       assertion=UpperCamelCase__				,       )

	return example





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	with jsonlines.open(UpperCamelCase__				,       """a"""    ) as writer:
		for example in tqdm(UpperCamelCase__				,       total=len(UpperCamelCase__    )				,       desc="""Saving samples ... """    ):
			A             =					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

				_lowercase							:   Optional[int]						=						load_dataset("natural_questions")
				_lowercase							:   str						=						BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

				_lowercase							:   Union[str, Any]						=						data["train" if PROCESS_TRAIN == "true" else "validation"]

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

				np.random.seed(SEED)
				_lowercase							:   Union[str, Any]						=						"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 	_lowerCAmelCase					( )			-> Tuple:





	"""simple docstring"""







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

	return image





def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Dict:





	"""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 	_lowerCAmelCase					( UpperCamelCase__:  Tuple				,       UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Union[str, Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					dct.pop(UpperCamelCase__    )
	A             =					val





def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Union[str, Any]    )			-> Tuple:





	"""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(UpperCamelCase__				,       requires_grad=UpperCamelCase__    ), v_bias)    )
		A             =					qkv_bias





def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  Tuple    )			-> Dict:





	"""simple docstring"""







	A             =					3_64 if """coco""" in model_name else 2_24
	A             =					BlipaVisionConfig(image_size=UpperCamelCase__    ).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=UpperCamelCase__    ).to_dict()
	elif "opt-6.7b" in model_name:
		A             =					OPTConfig.from_pretrained("""facebook/opt-6.7b"""				,       eos_token_id=UpperCamelCase__    ).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=UpperCamelCase__				,       text_config=UpperCamelCase__    )

	return config, image_size





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  Optional[Any]=None				,       UpperCamelCase__:  Tuple=False    )			-> List[str]:





	"""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=UpperCamelCase__    ).input_ids[0]
	A       ,      A             =					get_blipa_config(UpperCamelCase__				,       eos_token_id=UpperCamelCase__    )

	A             =					BlipaForConditionalGeneration(UpperCamelCase__    ).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=UpperCamelCase__				,       model_type=UpperCamelCase__				,       is_eval=UpperCamelCase__				,       device=UpperCamelCase__    )
	original_model.eval()
	print("""Done!"""    )

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

	# some keys can be renamed efficiently
	for key, val in state_dict.copy().items():
		A             =					state_dict.pop(UpperCamelCase__    )
		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(UpperCamelCase__				,       UpperCamelCase__    )

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

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

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

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

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

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

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

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

	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							:   List[Any]						=						argparse.ArgumentParser()
				_lowercase							:   List[str]						=						[
				    "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							:   Tuple						=						parser.parse_args()

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



