






'''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

SCREAMING_SNAKE_CASE							=						collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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


def 				lowercase_					(     __A			:							str      )							->   Any:



 """simple docstring"""




 lowercase       : Dict						=numpy.dtype(numpy.uintaa      ).newbyteorder('''>'''      )
 return numpy.frombuffer(bytestream.read(4      )       ,   dtype=__A      )[0]


@deprecated(__A       ,   '''Please use tf.data to implement this functionality.'''      )
def 				lowercase_					(     __A			:							Union[str, Any]      )							->   List[str]:



 """simple docstring"""




 print('''Extracting'''       ,   f.name      )
 with gzip.GzipFile(fileobj=__A      ) as bytestream:
  lowercase       : int						=_readaa(__A      )
  if magic != 2_0_5_1:
   raise ValueError(
       '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)      )
  lowercase       : Optional[Any]						=_readaa(__A      )
  lowercase       : int						=_readaa(__A      )
  lowercase       : Dict						=_readaa(__A      )
  lowercase       : Union[str, Any]						=bytestream.read(rows * cols * num_images      )
  lowercase       : Union[str, Any]						=numpy.frombuffer(__A       ,   dtype=numpy.uinta      )
  lowercase       : Tuple						=data.reshape(__A       ,   __A       ,   __A       ,   1      )
  return data


@deprecated(__A       ,   '''Please use tf.one_hot on tensors.'''      )
def 				lowercase_					(     __A			:							Dict       ,   __A			:							Any      )							->   List[str]:



 """simple docstring"""




 lowercase       : str						=labels_dense.shape[0]
 lowercase       : int						=numpy.arange(__A      ) * num_classes
 lowercase       : List[str]						=numpy.zeros((num_labels, num_classes)      )
 lowercase       : Tuple						=1
 return labels_one_hot


@deprecated(__A       ,   '''Please use tf.data to implement this functionality.'''      )
def 				lowercase_					(     __A			:							List[str]       ,   __A			:							Union[str, Any]=False       ,   __A			:							Tuple=1_0      )							->   Optional[int]:



 """simple docstring"""




 print('''Extracting'''       ,   f.name      )
 with gzip.GzipFile(fileobj=__A      ) as bytestream:
  lowercase       : List[Any]						=_readaa(__A      )
  if magic != 2_0_4_9:
   raise ValueError(
       '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)      )
  lowercase       : str						=_readaa(__A      )
  lowercase       : Optional[int]						=bytestream.read(__A      )
  lowercase       : List[str]						=numpy.frombuffer(__A       ,   dtype=numpy.uinta      )
  if one_hot:
   return _dense_to_one_hot(__A       ,   __A      )
  return labels
class UpperCAmelCase_       :




      """simple docstring"""




      @deprecated(
          UpperCAmelCase  ,   '''Please use alternatives such as official/mnist/_DataSet.py'''
          ''' from tensorflow/models.'''  ,   )
      def __init__(			self      :		Any  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		Dict=False  ,   UpperCAmelCase      :		List[Any]=False  ,   UpperCAmelCase      :		Optional[Any]=dtypes.floataa  ,   UpperCAmelCase      :		Tuple=True  ,   UpperCAmelCase      :		Tuple=None  ,   )	->	List[str]:


       '''simple docstring'''

       lowercase	,		lowercase       : Union[str, Any]						=random_seed.get_seed(UpperCAmelCase		)
       # If op level seed is not set, use whatever graph level seed is returned
       numpy.random.seed(seeda if seed is None else seeda		)
       lowercase       : Optional[int]						=dtypes.as_dtype(UpperCAmelCase		).base_dtype
       if dtype not in (dtypes.uinta, dtypes.floataa):
        raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype		)
       if fake_data:
        lowercase       : Dict						=1_0000
        lowercase       : Union[str, Any]						=one_hot
       else:
        assert (
            images.shape[0] == labels.shape[0]
        ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
        lowercase       : List[str]						=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
         lowercase       : int						=images.reshape(
             images.shape[0]  ,   images.shape[1] * images.shape[2]		)
        if dtype == dtypes.floataa:
         # Convert from [0, 255] -> [0.0, 1.0].
         lowercase       : Optional[Any]						=images.astype(numpy.floataa		)
         lowercase       : Tuple						=numpy.multiply(UpperCAmelCase  ,   1.0 / 2_5_5.0		)
       lowercase       : Tuple						=images
       lowercase       : Optional[int]						=labels
       lowercase       : Union[str, Any]						=0
       lowercase       : Any						=0






      @property
      def 					A__   (			self      :		List[str]		)	->	int:


       '''simple docstring'''

       return self._images






      @property
      def 					A__   (			self      :		str		)	->	Dict:


       '''simple docstring'''

       return self._labels






      @property
      def 					A__   (			self      :		Optional[Any]		)	->	List[Any]:


       '''simple docstring'''

       return self._num_examples






      @property
      def 					A__   (			self      :		Tuple		)	->	List[Any]:


       '''simple docstring'''

       return self._epochs_completed






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		str=True		)	->	int:


       '''simple docstring'''

       if fake_data:
        lowercase       : Optional[Any]						=[1] * 784
        lowercase       : Any						=[1] + [0] * 9 if self.one_hot else 0
        return (
            [fake_image for _ in range(UpperCAmelCase		)],
            [fake_label for _ in range(UpperCAmelCase		)],
        )
       lowercase       : List[str]						=self._index_in_epoch
       # Shuffle for the first epoch
       if self._epochs_completed == 0 and start == 0 and shuffle:
        lowercase       : int						=numpy.arange(self._num_examples		)
        numpy.random.shuffle(UpperCAmelCase		)
        lowercase       : Union[str, Any]						=self.images[perma]
        lowercase       : List[str]						=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
        lowercase       : Optional[int]						=self._num_examples - start
        lowercase       : Tuple						=self._images[start : self._num_examples]
        lowercase       : Optional[Any]						=self._labels[start : self._num_examples]
        # Shuffle the data
        if shuffle:
         lowercase       : List[str]						=numpy.arange(self._num_examples		)
         numpy.random.shuffle(UpperCAmelCase		)
         lowercase       : Optional[int]						=self.images[perm]
         lowercase       : Union[str, Any]						=self.labels[perm]
        # Start next epoch
        lowercase       : str						=0
        lowercase       : Optional[int]						=batch_size - rest_num_examples
        lowercase       : Optional[int]						=self._index_in_epoch
        lowercase       : str						=self._images[start:end]
        lowercase       : Dict						=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
        lowercase       : Union[str, Any]						=self._index_in_epoch
        return self._images[start:end], self._labels[start:end]





@deprecated(__A       ,   '''Please write your own downloading logic.'''      )
def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							Union[str, Any]       ,   __A			:							Optional[int]      )							->   Dict:



 """simple docstring"""




 if not gfile.Exists(__A      ):
  gfile.MakeDirs(__A      )
 lowercase       : Optional[int]						=os.path.join(__A       ,   __A      )
 if not gfile.Exists(__A      ):
  urllib.request.urlretrieve(__A       ,   __A      )  # noqa: S310
  with gfile.GFile(__A      ) as f:
   lowercase       : Dict						=f.size()
  print('''Successfully downloaded'''       ,   __A       ,   __A       ,   '''bytes.'''      )
 return filepath





@deprecated(
    __A       ,   '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''      )
def 				lowercase_					(     __A			:							Any       ,   __A			:							Union[str, Any]=False       ,   __A			:							Tuple=False       ,   __A			:							Optional[Any]=dtypes.floataa       ,   __A			:							List[Any]=True       ,   __A			:							str=5_0_0_0       ,   __A			:							Any=None       ,   __A			:							Any=DEFAULT_SOURCE_URL       ,   )							->   Dict:



 """simple docstring"""




 if fake_data:

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

  lowercase       : Tuple						=fake()
  lowercase       : Union[str, Any]						=fake()
  lowercase       : int						=fake()
  return _Datasets(train=__A       ,   validation=__A       ,   test=__A      )

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

 lowercase       : List[Any]						='''train-images-idx3-ubyte.gz'''
 lowercase       : List[str]						='''train-labels-idx1-ubyte.gz'''
 lowercase       : List[Any]						='''t10k-images-idx3-ubyte.gz'''
 lowercase       : List[str]						='''t10k-labels-idx1-ubyte.gz'''

 lowercase       : Dict						=_maybe_download(
     __A       ,   __A       ,   source_url + train_images_file      )
 with gfile.Open(__A       ,   '''rb'''      ) as f:
  lowercase       : Union[str, Any]						=_extract_images(__A      )

 lowercase       : Dict						=_maybe_download(
     __A       ,   __A       ,   source_url + train_labels_file      )
 with gfile.Open(__A       ,   '''rb'''      ) as f:
  lowercase       : Tuple						=_extract_labels(__A       ,   one_hot=__A      )

 lowercase       : Any						=_maybe_download(
     __A       ,   __A       ,   source_url + test_images_file      )
 with gfile.Open(__A       ,   '''rb'''      ) as f:
  lowercase       : Dict						=_extract_images(__A      )

 lowercase       : int						=_maybe_download(
     __A       ,   __A       ,   source_url + test_labels_file      )
 with gfile.Open(__A       ,   '''rb'''      ) as f:
  lowercase       : Any						=_extract_labels(__A       ,   one_hot=__A      )

 if not 0 <= validation_size <= len(__A      ):
  lowercase       : str						=(
      '''Validation size should be between 0 and '''
      F'{len(__A      )}. Received: {validation_size}.'
  )
  raise ValueError(__A      )

 lowercase       : int						=train_images[:validation_size]
 lowercase       : Any						=train_labels[:validation_size]
 lowercase       : List[Any]						=train_images[validation_size:]
 lowercase       : List[Any]						=train_labels[validation_size:]

 lowercase       : int						={'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

 lowercase       : Any						=_DataSet(__A       ,   __A       ,   **__A      )
 lowercase       : List[str]						=_DataSet(__A       ,   __A       ,   **__A      )
 lowercase       : Tuple						=_DataSet(__A       ,   __A       ,   **__A      )

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





import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def   lowerCamelCase__							(							):




			"""simple docstring"""


			lowerCAmelCase_         =      "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
			lowerCAmelCase_         =      Image.open(requests.get(__lowerCAmelCase       ,				stream=__lowerCAmelCase							).raw							).convert("RGB"							)

			return image






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]							):




			"""simple docstring"""


			lowerCAmelCase_         =      []
			# 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.embeddings.layernorm.weight")							)
			rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias")							)

			# fmt: on
			return rename_keys






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Union[str, Any]							):




			"""simple docstring"""


			lowerCAmelCase_         =      dct.pop(__lowerCAmelCase							)
			lowerCAmelCase_         =      val






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any       ,				__lowerCAmelCase    :    Union[str, Any]							):




			"""simple docstring"""


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

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]							):




			"""simple docstring"""


			lowerCAmelCase_         =      364 if "coco" in model_name else 224
			lowerCAmelCase_         =      InstructBlipVisionConfig(image_size=__lowerCAmelCase							).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 "t5-xl" in model_name:
						lowerCAmelCase_         =      TaConfig.from_pretrained("google/flan-t5-xl"       ,				dense_act_fn="gelu"       ,				bos_token_id=1							).to_dict()
			elif "t5-xxl" in model_name:
						lowerCAmelCase_         =      TaConfig.from_pretrained("google/flan-t5-xxl"       ,				dense_act_fn="gelu"       ,				bos_token_id=1							).to_dict()
			elif "vicuna-7b" in model_name:
						lowerCAmelCase_         =      LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"       ,				vocab_size=32001							).to_dict()
			elif "vicuna-13b" in model_name:
						lowerCAmelCase_         =      LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"       ,				vocab_size=32001							).to_dict()
			else:
						raise ValueError("Model name not supported"							)

			# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
			lowerCAmelCase_         =      InstructBlipQFormerConfig(vocab_size=30523							).to_dict()
			lowerCAmelCase_         =      InstructBlipConfig(vision_config=__lowerCAmelCase       ,				text_config=__lowerCAmelCase       ,				qformer_config=__lowerCAmelCase							)

			return config, image_size






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    int=None       ,				__lowerCAmelCase    :    Dict=False							):




			"""simple docstring"""


			lowerCAmelCase_         =      AutoTokenizer.from_pretrained("bert-base-uncased"       ,				truncation_side="left"							)
			qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}							)

			if "t5" in model_name:
						lowerCAmelCase_         =      TaTokenizerFast.from_pretrained("google/flan-t5-xl"       ,				truncation_side="left"							)
			elif "vicuna" in model_name:
						# the following was used in the original implementation:
						# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
						# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
						# tokenizer.add_special_tokens({"bos_token": "</s>"})
						# tokenizer.add_special_tokens({"eos_token": "</s>"})
						# tokenizer.add_special_tokens({"unk_token": "</s>"})
						lowerCAmelCase_         =      LlamaTokenizerFast.from_pretrained(
						    "huggyllama/llama-7b"       ,				truncation_side="left"       ,				bos_token="</s>"       ,				unk_token="</s>"							)
						tokenizer.add_special_tokens({"pad_token": "[PAD]"}							)

			lowerCAmelCase_      ,    lowerCAmelCase_         =      get_blipa_config(__lowerCAmelCase							)
			lowerCAmelCase_         =      InstructBlipForConditionalGeneration(__lowerCAmelCase							).eval()

			lowerCAmelCase_         =      {
			    "instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
			    "instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
			    "instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
			    "instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
			}

			lowerCAmelCase_      ,    lowerCAmelCase_         =      model_name_to_original[model_name]

			# load original model
			print("Loading original model..."							)
			lowerCAmelCase_         =      "cuda:1" if torch.cuda.is_available() else "cpu"
			lowerCAmelCase_         =      "cuda:2" if torch.cuda.is_available() else "cpu"
			lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      load_model_and_preprocess(
			    name=__lowerCAmelCase       ,				model_type=__lowerCAmelCase       ,				is_eval=__lowerCAmelCase       ,				device=__lowerCAmelCase							)
			original_model.eval()
			print("Done!"							)

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

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						lowerCAmelCase_         =      state_dict.pop(__lowerCAmelCase							)
						if key.startswith("Qformer.bert"							):
									lowerCAmelCase_         =      key.replace("Qformer.bert"       ,				"qformer"							)
						if "attention.self" in key:
									lowerCAmelCase_         =      key.replace("self"       ,				"attention"							)
						if "llm_proj" in key:
									lowerCAmelCase_         =      key.replace("llm_proj"       ,				"language_projection"							)
						if "t5_proj" in key:
									lowerCAmelCase_         =      key.replace("t5_proj"       ,				"language_projection"							)
						if key.startswith("llm_model"							):
									lowerCAmelCase_         =      key.replace("llm_model"       ,				"language_model"							)
						if key.startswith("t5"							):
									lowerCAmelCase_         =      key.replace("t5"       ,				"language"							)
						lowerCAmelCase_         =      val

			# read in qv biases
			read_in_q_v_bias(__lowerCAmelCase       ,				__lowerCAmelCase							)

			# note: weights get loaded in torch.float32 by default
			hf_model.load_state_dict(__lowerCAmelCase       ,				strict=__lowerCAmelCase							)

			lowerCAmelCase_         =      load_demo_image()
			lowerCAmelCase_         =      "What is unusual about this image?"

			# create processor
			lowerCAmelCase_         =      BlipImageProcessor(
			    size={"height": image_size, "width": image_size}       ,				image_mean=__lowerCAmelCase       ,				image_std=__lowerCAmelCase							)
			lowerCAmelCase_         =      InstructBlipProcessor(
			    image_processor=__lowerCAmelCase       ,				tokenizer=__lowerCAmelCase       ,				qformer_tokenizer=__lowerCAmelCase       ,				)
			lowerCAmelCase_         =      processor(images=__lowerCAmelCase       ,				text=__lowerCAmelCase       ,				return_tensors="pt"							).to(__lowerCAmelCase							)

			# make sure processor creates exact same pixel values
			lowerCAmelCase_         =      vis_processors["eval"](__lowerCAmelCase							).unsqueeze(0							).to(__lowerCAmelCase							)
			lowerCAmelCase_         =      inputs.pixel_values
			assert torch.allclose(original_pixel_values.to(pixel_values.device							)       ,				__lowerCAmelCase							)

			original_model.to(__lowerCAmelCase							)
			hf_model.to(__lowerCAmelCase							)
			with torch.no_grad():
						if "vicuna" in model_name:
									lowerCAmelCase_         =      original_model({"image": original_pixel_values, "text_input": [prompt]}							).logits
									lowerCAmelCase_         =      hf_model(**__lowerCAmelCase							).logits
						else:
									lowerCAmelCase_         =      original_model(
									    {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}							).logits
									lowerCAmelCase_         =      tokenizer("\n"       ,				return_tensors="pt"							).input_ids.to(__lowerCAmelCase							)
									lowerCAmelCase_         =      label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id       ,				-100							)
									lowerCAmelCase_         =      hf_model(**__lowerCAmelCase       ,				labels=__lowerCAmelCase							).logits

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

			# assert values
			assert original_logits.shape == logits.shape
			lowerCAmelCase_         =      1e-4 if "vicuna" in model_name else 1e-5
			assert torch.allclose(original_logits.to(logits.device							)       ,				__lowerCAmelCase       ,				atol=__lowerCAmelCase							)
			print("Looks ok!"							)

			print("Generating with original model..."							)
			lowerCAmelCase_         =      original_model.generate({"image": original_pixel_values, "prompt": prompt}       ,				num_beams=5							)

			# important: we need to cast the weights of the HF model to the appropriate type
			print("Generating with HF model..."							)
			lowerCAmelCase_         =      hf_model.generate(
			    **__lowerCAmelCase       ,				do_sample=__lowerCAmelCase       ,				num_beams=5       ,				max_length=256       ,				min_length=1       ,				top_p=0.9       ,				repetition_penalty=1.5       ,				length_penalty=1.0       ,				temperature=1       ,				)
			if "vicuna" in model_name:
						# convert output id 0 to 2 (eos_token_id)
						# TODO add this in the generate method?
						lowerCAmelCase_         =      2
			print("Original generation:"       ,				__lowerCAmelCase							)
			lowerCAmelCase_         =      processor.batch_decode(__lowerCAmelCase       ,				skip_special_tokens=__lowerCAmelCase							)
			lowerCAmelCase_         =      [text.strip() for text in output_text]
			print("HF generation:"       ,				__lowerCAmelCase							)

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

			if push_to_hub:
						processor.push_to_hub(F"""Salesforce/{model_name}"""							)
						hf_model.push_to_hub(F"""Salesforce/{model_name}"""							)


if __name__ == "__main__":
			_A  =	argparse.ArgumentParser()
			_A  =	[
			    "instructblip-vicuna-7b",
			    "instructblip-vicuna-13b",
			    "instructblip-flan-t5-xl",
			    "instructblip-flan-t5-xxl",
			]
			parser.add_argument(
			    "--model_name",
			    default="instructblip-flan-t5-xl",
			    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",
			)

			_A  =	parser.parse_args()

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



