





'''simple docstring'''
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 				lowercase__   (   ) ->      Dict:



       """simple docstring"""






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

       return image
def 				lowercase__   (   __lowercase						:  List[Any]     ) ->      str:



       """simple docstring"""






       __UpperCamelCase       =  []
       # 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 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  List[str]				,  __lowercase						:  Optional[Any]     ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  dct.pop(__lowercase     )
       __UpperCamelCase       =  val
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






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

              # next, set bias in the state dict
              __UpperCamelCase       =  torch.cat((q_bias, torch.zeros_like(__lowercase				,  requires_grad=__lowercase     ), v_bias)     )
              __UpperCamelCase       =  qkv_bias
def 				lowercase__   (   __lowercase						:  List[Any]				,  __lowercase						:  int     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  364 if 'coco' in model_name else 224
       __UpperCamelCase       =  BlipaVisionConfig(image_size=__lowercase     ).to_dict()

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

       __UpperCamelCase       =  BlipaConfig(vision_config=__lowercase				,  text_config=__lowercase     )

       return config, image_size
@torch.no_grad()
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Any=None				,  __lowercase						:  Union[str, Any]=False     ) ->      str:



       """simple docstring"""






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

       __UpperCamelCase       =  BlipaForConditionalGeneration(__lowercase     ).eval()

       __UpperCamelCase       =  {
           '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'),
       }

       __UpperCamelCase       ,						__UpperCamelCase       =  model_name_to_original[model_name]

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

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

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

       # read in qv biases
       read_in_q_v_bias(__lowercase				,  __lowercase     )

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

       __UpperCamelCase       =  load_demo_image()
       __UpperCamelCase       =  vis_processors['eval'](__lowercase     ).unsqueeze(0     ).to(__lowercase     )
       __UpperCamelCase       =  tokenizer(['\n']				,  return_tensors='pt'     ).input_ids.to(__lowercase     )

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

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

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

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

       # assert values
       if model_name == "blip2-flan-t5-xl":
              __UpperCamelCase       =  torch.tensor(
                  [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]]				,  device=__lowercase     )
              assert torch.allclose(logits[0, :3, :3]				,  __lowercase				,  atol=1e-4     )
       elif model_name == "blip2-flan-t5-xl-coco":
              __UpperCamelCase       =  torch.tensor(
                  [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]]				,  device=__lowercase     )
       else:
              # cast to same type
              __UpperCamelCase       =  logits.dtype
              assert torch.allclose(original_logits.to(__lowercase     )				,  __lowercase				,  atol=1e-2     )
       print('Looks ok!'     )

       print('Generating a caption...'     )
       __UpperCamelCase       =  ''
       __UpperCamelCase       =  tokenizer(__lowercase				,  return_tensors='pt'     ).input_ids.to(__lowercase     )

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

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

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


if __name__ == "__main__":
    a__							:							Any =argparse.ArgumentParser()
    a__							:							Optional[int] =[
        '''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''',
    )

    a__							:							Tuple =parser.parse_args()

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







'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def 				lowercase__   (   __lowercase						:  Features     ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  np.inf

       def set_batch_size(__lowercase						:  FeatureType     ) -> None:
              nonlocal batch_size
              if isinstance(__lowercase				,  __lowercase     ):
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
              elif isinstance(__lowercase				,  __lowercase     ):
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
              elif isinstance(__lowercase				,  __lowercase     ) and feature.dtype == "binary":
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

       _visit(__lowercase				,  __lowercase     )

       return None if batch_size is np.inf else batch_size

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							NestedDataStructureLike[PathLike]							,	__A       :							Optional[NamedSplit] = None							,	__A       :							Optional[Features] = None							,	__A       :							str = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        super().__init__(
            __A							,	split=__A							,	features=__A							,	cache_dir=__A							,	keep_in_memory=__A							,	streaming=__A							,	num_proc=__A							,	**__A							,	)
        __UpperCamelCase       =  path_or_paths if isinstance(__A							,	__A	) else {self.split: path_or_paths}
        __UpperCamelCase       =  _PACKAGED_DATASETS_MODULES['parquet'][1]
        __UpperCamelCase       =  Parquet(
            cache_dir=__A							,	data_files=__A							,	features=__A							,	hash=__A							,	**__A							,	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        # Build iterable dataset
        if self.streaming:
               __UpperCamelCase       =  self.builder.as_streaming_dataset(split=self.split	)
        # Build regular (map-style) dataset
        else:
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None

               self.builder.download_and_prepare(
                   download_config=__A							,	download_mode=__A							,	verification_mode=__A							,	base_path=__A							,	num_proc=self.num_proc							,	)
               __UpperCamelCase       =  self.builder.as_dataset(
                   split=self.split							,	verification_mode=__A							,	in_memory=self.keep_in_memory	)
        return dataset






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							Dataset							,	__A       :							Union[PathLike, BinaryIO]							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  path_or_buf
        __UpperCamelCase       =  batch_size or get_writer_batch_size(dataset.features	)
        __UpperCamelCase       =  parquet_writer_kwargs






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

        if isinstance(self.path_or_buf							,	(str, bytes, os.PathLike)	):
               with open(self.path_or_buf							,	'wb+'	) as buffer:
                      __UpperCamelCase       =  self._write(file_obj=__A							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        else:
               __UpperCamelCase       =  self._write(file_obj=self.path_or_buf							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        return written






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							BinaryIO							,	__A       :							int							,	**__A       :							List[str]	):
        __UpperCamelCase       =  0
        __UpperCamelCase       =  parquet_writer_kwargs.pop('path_or_buf'							,	__A	)
        __UpperCamelCase       =  self.dataset.features.arrow_schema

        __UpperCamelCase       =  pq.ParquetWriter(__A							,	schema=__A							,	**__A	)

        for offset in logging.tqdm(
            range(0							,	len(self.dataset	)							,	__A	)							,	unit='ba'							,	disable=not logging.is_progress_bar_enabled()							,	desc='Creating parquet from Arrow format'							,	):
               __UpperCamelCase       =  query_table(
                   table=self.dataset._data							,	key=slice(__A							,	offset + batch_size	)							,	indices=self.dataset._indices if self.dataset._indices is not None else None							,	)
               writer.write_table(__A	)
               written += batch.nbytes
        writer.close()
        return written
