





'''simple docstring'''






import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

lowerCAmelCase									=   collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCAmelCase									=   "https://storage.googleapis.com/cvdf-datasets/mnist/"






def   __A    (				a_    :	int						):
		lowerCAmelCase  :     Optional[Any]       						= numpy.dtype(numpy.uintaa						).newbyteorder(">"						)
		return numpy.frombuffer(bytestream.read(4						)   ,dtype=UpperCamelCase__						)[0]






@deprecated(UpperCamelCase__   ,"Please use tf.data to implement this functionality."						)
def   __A    (				a_    :	Union[str, Any]						):
		print("Extracting"   ,f.name						)
		with gzip.GzipFile(fileobj=UpperCamelCase__						) as bytestream:
				lowerCAmelCase  :     List[Any]       						= _readaa(UpperCamelCase__						)
				if magic != 2_0_5_1:
						raise ValueError(
						    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)						)
				lowerCAmelCase  :     Union[str, Any]       						= _readaa(UpperCamelCase__						)
				lowerCAmelCase  :     str       						= _readaa(UpperCamelCase__						)
				lowerCAmelCase  :     Tuple       						= _readaa(UpperCamelCase__						)
				lowerCAmelCase  :     Tuple       						= bytestream.read(rows * cols * num_images						)
				lowerCAmelCase  :     Any       						= numpy.frombuffer(UpperCamelCase__   ,dtype=numpy.uinta						)
				lowerCAmelCase  :     List[Any]       						= data.reshape(UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__   ,1						)
				return data






@deprecated(UpperCamelCase__   ,"Please use tf.one_hot on tensors."						)
def   __A    (				a_    :	Any   ,a_    :	List[str]						):
		lowerCAmelCase  :     List[Any]       						= labels_dense.shape[0]
		lowerCAmelCase  :     Any       						= numpy.arange(UpperCamelCase__						) * num_classes
		lowerCAmelCase  :     str       						= numpy.zeros((num_labels, num_classes)						)
		lowerCAmelCase  :     List[str]       						= 1
		return labels_one_hot






@deprecated(UpperCamelCase__   ,"Please use tf.data to implement this functionality."						)
def   __A    (				a_    :	Optional[int]   ,a_    :	Tuple=False   ,a_    :	Dict=1_0						):
		print("Extracting"   ,f.name						)
		with gzip.GzipFile(fileobj=UpperCamelCase__						) as bytestream:
				lowerCAmelCase  :     Dict       						= _readaa(UpperCamelCase__						)
				if magic != 2_0_4_9:
						raise ValueError(
						    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)						)
				lowerCAmelCase  :     Optional[Any]       						= _readaa(UpperCamelCase__						)
				lowerCAmelCase  :     Dict       						= bytestream.read(UpperCamelCase__						)
				lowerCAmelCase  :     str       						= numpy.frombuffer(UpperCamelCase__   ,dtype=numpy.uinta						)
				if one_hot:
						return _dense_to_one_hot(UpperCamelCase__   ,UpperCamelCase__						)
				return labels
class 		lowerCamelCase	:



	@deprecated(
	    a__		,   "Please use alternatives such as official/mnist/_DataSet.py"
	    " from tensorflow/models."		,   )
	def __init__( self		,   a_		,   a_		,   a_=False		,   a_=False		,   a_=dtypes.floataa		,   a_=True		,   a_=None		,   ):
			lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= random_seed.get_seed(a__						)
			# If op level seed is not set, use whatever graph level seed is returned
			numpy.random.seed(seeda if seed is None else seeda						)
			lowerCAmelCase  :     Tuple       						= dtypes.as_dtype(a__						).base_dtype
			if dtype not in (dtypes.uinta, dtypes.floataa):
					raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype						)
			if fake_data:
					lowerCAmelCase  :     Dict       						= 10_000
					lowerCAmelCase  :     Optional[int]       						= one_hot
			else:
					assert (
					    images.shape[0] == labels.shape[0]
					), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
					lowerCAmelCase  :     List[Any]       						= images.shape[0]

					# Convert shape from [num examples, rows, columns, depth]
					# to [num examples, rows*columns] (assuming depth == 1)
					if reshape:
							assert images.shape[3] == 1
							lowerCAmelCase  :     Union[str, Any]       						= images.reshape(
							    images.shape[0]		,   images.shape[1] * images.shape[2]						)
					if dtype == dtypes.floataa:
							# Convert from [0, 255] -> [0.0, 1.0].
							lowerCAmelCase  :     str       						= images.astype(numpy.floataa						)
							lowerCAmelCase  :     Union[str, Any]       						= numpy.multiply(a__		,   1.0 / 255.0						)
			lowerCAmelCase  :     Union[str, Any]       						= images
			lowerCAmelCase  :     Optional[int]       						= labels
			lowerCAmelCase  :     List[str]       						= 0
			lowerCAmelCase  :     Optional[Any]       						= 0



	@property
	def 	_lowerCamelCase     ( self						):
			return self._images



	@property
	def 	_lowerCamelCase     ( self						):
			return self._labels



	@property
	def 	_lowerCamelCase     ( self						):
			return self._num_examples



	@property
	def 	_lowerCamelCase     ( self						):
			return self._epochs_completed





	def 	_lowerCamelCase     ( self		,   a_		,   a_=False		,   a_=True						):
			if fake_data:
					lowerCAmelCase  :     Dict       						= [1] * 784
					lowerCAmelCase  :     Union[str, Any]       						= [1] + [0] * 9 if self.one_hot else 0
					return (
					    [fake_image for _ in range(a__						)],
					    [fake_label for _ in range(a__						)],
					)
			lowerCAmelCase  :     Any       						= self._index_in_epoch
			# Shuffle for the first epoch
			if self._epochs_completed == 0 and start == 0 and shuffle:
					lowerCAmelCase  :     List[Any]       						= numpy.arange(self._num_examples						)
					numpy.random.shuffle(a__						)
					lowerCAmelCase  :     List[str]       						= self.images[perma]
					lowerCAmelCase  :     int       						= self.labels[perma]
			# Go to the next epoch
			if start + batch_size > self._num_examples:
					# Finished epoch
					self._epochs_completed += 1
					# Get the rest examples in this epoch
					lowerCAmelCase  :     List[Any]       						= self._num_examples - start
					lowerCAmelCase  :     List[Any]       						= self._images[start : self._num_examples]
					lowerCAmelCase  :     List[Any]       						= self._labels[start : self._num_examples]
					# Shuffle the data
					if shuffle:
							lowerCAmelCase  :     Optional[int]       						= numpy.arange(self._num_examples						)
							numpy.random.shuffle(a__						)
							lowerCAmelCase  :     str       						= self.images[perm]
							lowerCAmelCase  :     int       						= self.labels[perm]
					# Start next epoch
					lowerCAmelCase  :     List[Any]       						= 0
					lowerCAmelCase  :     int       						= batch_size - rest_num_examples
					lowerCAmelCase  :     Tuple       						= self._index_in_epoch
					lowerCAmelCase  :     Any       						= self._images[start:end]
					lowerCAmelCase  :     str       						= self._labels[start:end]
					return (
					    numpy.concatenate((images_rest_part, images_new_part)		,   axis=0						),
					    numpy.concatenate((labels_rest_part, labels_new_part)		,   axis=0						),
					)
			else:
					self._index_in_epoch += batch_size
					lowerCAmelCase  :     List[str]       						= self._index_in_epoch
					return self._images[start:end], self._labels[start:end]






@deprecated(UpperCamelCase__   ,"Please write your own downloading logic."						)
def   __A    (				a_    :	Tuple   ,a_    :	Tuple   ,a_    :	List[Any]						):







		if not gfile.Exists(UpperCamelCase__						):
				gfile.MakeDirs(UpperCamelCase__						)
		lowerCAmelCase  :     List[Any]       						= os.path.join(UpperCamelCase__   ,UpperCamelCase__						)
		if not gfile.Exists(UpperCamelCase__						):
				urllib.request.urlretrieve(UpperCamelCase__   ,UpperCamelCase__						)  # noqa: S310
				with gfile.GFile(UpperCamelCase__						) as f:
						lowerCAmelCase  :     List[Any]       						= f.size()
				print("Successfully downloaded"   ,UpperCamelCase__   ,UpperCamelCase__   ,"bytes."						)
		return filepath






@deprecated(
    UpperCamelCase__   ,"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"						)
def   __A    (				a_    :	Tuple   ,a_    :	Any=False   ,a_    :	str=False   ,a_    :	Any=dtypes.floataa   ,a_    :	Optional[Any]=True   ,a_    :	List[Any]=5_0_0_0   ,a_    :	Optional[int]=None   ,a_    :	Optional[Any]=DEFAULT_SOURCE_URL   ,):







		if fake_data:

				def fake():
						return _DataSet(
						    []   ,[]   ,fake_data=UpperCamelCase__   ,one_hot=UpperCamelCase__   ,dtype=UpperCamelCase__   ,seed=UpperCamelCase__						)

				lowerCAmelCase  :     Any       						= fake()
				lowerCAmelCase  :     Dict       						= fake()
				lowerCAmelCase  :     int       						= fake()
				return _Datasets(train=UpperCamelCase__   ,validation=UpperCamelCase__   ,test=UpperCamelCase__						)

		if not source_url:  # empty string check
				lowerCAmelCase  :     List[Any]       						= DEFAULT_SOURCE_URL

		lowerCAmelCase  :     Tuple       						= "train-images-idx3-ubyte.gz"
		lowerCAmelCase  :     List[Any]       						= "train-labels-idx1-ubyte.gz"
		lowerCAmelCase  :     List[str]       						= "t10k-images-idx3-ubyte.gz"
		lowerCAmelCase  :     List[str]       						= "t10k-labels-idx1-ubyte.gz"

		lowerCAmelCase  :     int       						= _maybe_download(
		    UpperCamelCase__   ,UpperCamelCase__   ,source_url + train_images_file						)
		with gfile.Open(UpperCamelCase__   ,"rb"						) as f:
				lowerCAmelCase  :     List[Any]       						= _extract_images(UpperCamelCase__						)

		lowerCAmelCase  :     Optional[int]       						= _maybe_download(
		    UpperCamelCase__   ,UpperCamelCase__   ,source_url + train_labels_file						)
		with gfile.Open(UpperCamelCase__   ,"rb"						) as f:
				lowerCAmelCase  :     Dict       						= _extract_labels(UpperCamelCase__   ,one_hot=UpperCamelCase__						)

		lowerCAmelCase  :     List[str]       						= _maybe_download(
		    UpperCamelCase__   ,UpperCamelCase__   ,source_url + test_images_file						)
		with gfile.Open(UpperCamelCase__   ,"rb"						) as f:
				lowerCAmelCase  :     Dict       						= _extract_images(UpperCamelCase__						)

		lowerCAmelCase  :     Optional[int]       						= _maybe_download(
		    UpperCamelCase__   ,UpperCamelCase__   ,source_url + test_labels_file						)
		with gfile.Open(UpperCamelCase__   ,"rb"						) as f:
				lowerCAmelCase  :     Tuple       						= _extract_labels(UpperCamelCase__   ,one_hot=UpperCamelCase__						)

		if not 0 <= validation_size <= len(UpperCamelCase__						):
				lowerCAmelCase  :     Tuple       						= (
				    "Validation size should be between 0 and "
				    f'''{len(UpperCamelCase__						)}. Received: {validation_size}.'''
				)
				raise ValueError(UpperCamelCase__						)

		lowerCAmelCase  :     Tuple       						= train_images[:validation_size]
		lowerCAmelCase  :     Any       						= train_labels[:validation_size]
		lowerCAmelCase  :     Optional[int]       						= train_images[validation_size:]
		lowerCAmelCase  :     str       						= train_labels[validation_size:]

		lowerCAmelCase  :     Union[str, Any]       						= {"dtype": dtype, "reshape": reshape, "seed": seed}

		lowerCAmelCase  :     List[Any]       						= _DataSet(UpperCamelCase__   ,UpperCamelCase__   ,**UpperCamelCase__						)
		lowerCAmelCase  :     Dict       						= _DataSet(UpperCamelCase__   ,UpperCamelCase__   ,**UpperCamelCase__						)
		lowerCAmelCase  :     Dict       						= _DataSet(UpperCamelCase__   ,UpperCamelCase__   ,**UpperCamelCase__						)

		return _Datasets(train=UpperCamelCase__   ,validation=UpperCamelCase__   ,test=UpperCamelCase__						)







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)



