





"""simple docstring"""





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
  import tensorflow as tf


lowercase__				:Optional[int]				   =       logging.get_logger(__name__)




@dataclass
class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       Tuple         =       [
          'no_inference',
          'no_cuda',
          'no_tpu',
          'no_speed',
          'no_memory',
          'no_env_print',
          'no_multi_process',
      ]

      def __init__(							self       :      Dict						,				**__lowercase       :      Dict ):


        '''simple docstring'''




        for deprecated_arg in self.deprecated_args:
          if deprecated_arg in kwargs:
            __UpperCAmelCase    :						Optional[Any]       =						deprecated_arg[3:]
            __UpperCAmelCase    :						List[str]       =						not kwargs.pop(__lowercase )
            logger.warning(
                f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                f''' {positive_arg}={kwargs[positive_arg]}''' )
        __UpperCAmelCase    :						int       =						kwargs.pop('''tpu_name'''						,				self.tpu_name )
        __UpperCAmelCase    :						Any       =						kwargs.pop('''device_idx'''						,				self.device_idx )
        __UpperCAmelCase    :						Tuple       =						kwargs.pop('''eager_mode'''						,				self.eager_mode )
        __UpperCAmelCase    :						Optional[int]       =						kwargs.pop('''use_xla'''						,				self.use_xla )
        super().__init__(**__lowercase )

      _A     :       str         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Name of TPU'}      ,  )
      _A     :       int         =       field(
          default=0      ,  metadata={'help': 'CPU / GPU device index. Defaults to 0.'}      ,  )
      _A     :       bool         =       field(default=__UpperCAmelCase      ,  metadata={'help': 'Benchmark models in eager model.'} )
      _A     :       bool         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
          }      ,  )

      @cached_property
      def       A_					(							self       :      int ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        __UpperCAmelCase    :						Any       =						None
        if self.tpu:
          try:
            if self.tpu_name:
              __UpperCAmelCase    :						List[str]       =						tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
            else:
              __UpperCAmelCase    :						Any       =						tf.distribute.cluster_resolver.TPUClusterResolver()
          except ValueError:
            __UpperCAmelCase    :						int       =						None
        return tpu

      @cached_property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        if self.is_tpu:
          tf.config.experimental_connect_to_cluster(self._setup_tpu )
          tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )

          __UpperCAmelCase    :						Any       =						tf.distribute.TPUStrategy(self._setup_tpu )
        else:
          # currently no multi gpu is allowed
          if self.is_gpu:
            # TODO: Currently only single GPU is supported
            tf.config.set_visible_devices(self.gpu_list[self.device_idx]						,				'''GPU''' )
            __UpperCAmelCase    :						Any       =						tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}''' )
          else:
            tf.config.set_visible_devices([]						,				'''GPU''' )  # disable GPU
            __UpperCAmelCase    :						str       =						tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}''' )

        return strategy

      @property
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        return self._setup_tpu is not None

      @property
      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        return self._setup_strategy

      @property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        return tf.config.list_physical_devices('''GPU''' )

      @property
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        if self.cuda:
          return len(self.gpu_list )
        return 0







      @property
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        return self.n_gpu > 0






"""simple docstring"""





import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
  __UpperCAmelCase    :						Tuple       =						Image.open(requests.get(UpperCAmelCase_  ,			stream=UpperCAmelCase_  ).raw  ).convert('''RGB'''  )

  __UpperCAmelCase    :						Dict       =						transforms.Compose(
      [
          transforms.Resize((image_size, image_size)  ,			interpolation=InterpolationMode.BICUBIC  ),
          transforms.ToTensor(),
          transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)  ,			(0.26_862_954, 0.26_130_258, 0.27_577_711)  ),
      ]  )
  __UpperCAmelCase    :						Optional[Any]       =						transform(UpperCAmelCase_  ).unsqueeze(0  ).to(UpperCAmelCase_  )
  return image


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  if "visual_encoder" in key:
    __UpperCAmelCase    :						Tuple       =						re.sub('''visual_encoder*'''  ,			'''vision_model.encoder'''  ,			UpperCAmelCase_  )
  if "blocks" in key:
    __UpperCAmelCase    :						List[str]       =						re.sub(R'''blocks'''  ,			'''layers'''  ,			UpperCAmelCase_  )
  if "attn" in key:
    __UpperCAmelCase    :						Union[str, Any]       =						re.sub(R'''attn'''  ,			'''self_attn'''  ,			UpperCAmelCase_  )
  if "norm1" in key:
    __UpperCAmelCase    :						Optional[Any]       =						re.sub(R'''norm1'''  ,			'''layer_norm1'''  ,			UpperCAmelCase_  )
  if "norm2" in key:
    __UpperCAmelCase    :						Optional[int]       =						re.sub(R'''norm2'''  ,			'''layer_norm2'''  ,			UpperCAmelCase_  )
  if "encoder.norm" in key:
    __UpperCAmelCase    :						Union[str, Any]       =						re.sub(R'''encoder.norm'''  ,			'''post_layernorm'''  ,			UpperCAmelCase_  )
  if "encoder.patch_embed.proj" in key:
    __UpperCAmelCase    :						Optional[int]       =						re.sub(R'''encoder.patch_embed.proj'''  ,			'''embeddings.patch_embedding'''  ,			UpperCAmelCase_  )

  if "encoder.pos_embed" in key:
    __UpperCAmelCase    :						List[Any]       =						re.sub(R'''encoder.pos_embed'''  ,			'''embeddings.position_embedding'''  ,			UpperCAmelCase_  )
  if "encoder.cls_token" in key:
    __UpperCAmelCase    :						List[str]       =						re.sub(R'''encoder.cls_token'''  ,			'''embeddings.class_embedding'''  ,			UpperCAmelCase_  )

  if "self_attn" in key:
    __UpperCAmelCase    :						Any       =						re.sub(R'''self_attn.proj'''  ,			'''self_attn.projection'''  ,			UpperCAmelCase_  )

  return key


@torch.no_grad()
def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=None  )						->Dict:





  """simple docstring"""


  if config_path is not None:
    __UpperCAmelCase    :						Optional[int]       =						BlipConfig.from_pretrained(UpperCAmelCase_  )
  else:
    __UpperCAmelCase    :						Optional[int]       =						BlipConfig(projection_dim=5_12  ,			text_config={}  ,			vision_config={}  )

  __UpperCAmelCase    :						Optional[Any]       =						BlipForConditionalGeneration(UpperCAmelCase_  ).eval()

  __UpperCAmelCase    :						List[str]       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

  __UpperCAmelCase    :						List[Any]       =						blip_decoder(pretrained=UpperCAmelCase_  ,			image_size=3_84  ,			vit='''base'''  )
  __UpperCAmelCase    :						str       =						pt_model.eval()

  __UpperCAmelCase    :						List[Any]       =						pt_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[str]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						Optional[int]       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						List[Any]       =						value

  hf_model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						str       =						3_84
  __UpperCAmelCase    :						Tuple       =						load_demo_image(image_size=UpperCAmelCase_  ,			device='''cpu'''  )
  __UpperCAmelCase    :						Optional[Any]       =						BertTokenizer.from_pretrained('''bert-base-uncased'''  )
  __UpperCAmelCase    :						Optional[int]       =						tokenizer(['''a picture of''']  ).input_ids

  __UpperCAmelCase    :						Union[str, Any]       =						hf_model.generate(UpperCAmelCase_  ,			UpperCAmelCase_  )

  assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

  __UpperCAmelCase    :						Optional[int]       =						hf_model.generate(UpperCAmelCase_  )

  assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

  if pytorch_dump_folder_path is not None:
    hf_model.save_pretrained(UpperCAmelCase_  )

  # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
  __UpperCAmelCase    :						Optional[Any]       =						(
      '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
  )

  __UpperCAmelCase    :						List[str]       =						blip_vqa(pretrained=UpperCAmelCase_  ,			image_size=UpperCAmelCase_  ,			vit='''base'''  )
  vqa_model.eval()

  __UpperCAmelCase    :						Dict       =						vqa_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[Any]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						int       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						Union[str, Any]       =						value

  __UpperCAmelCase    :						List[str]       =						BlipForQuestionAnswering(UpperCAmelCase_  )

  hf_vqa_model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						str       =						['''How many dogs are in this image?''']
  __UpperCAmelCase    :						Dict       =						tokenizer(UpperCAmelCase_  ,			return_tensors='''pt'''  ).input_ids

  __UpperCAmelCase    :						Any       =						hf_vqa_model.generate(UpperCAmelCase_  ,			UpperCAmelCase_  )
  print(tokenizer.decode(answer[0]  )  )

  assert tokenizer.decode(answer[0]  ) == "[UNK] 1 [SEP]"
  if pytorch_dump_folder_path is not None:
    hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''  )

  __UpperCAmelCase    :						List[Any]       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

  __UpperCAmelCase    :						List[str]       =						blip_itm(pretrained=UpperCAmelCase_  ,			image_size=UpperCAmelCase_  ,			vit='''base'''  )
  itm_model.eval()

  __UpperCAmelCase    :						Any       =						itm_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[str]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						Dict       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						int       =						value

  __UpperCAmelCase    :						Optional[int]       =						BlipForImageTextRetrieval(UpperCAmelCase_  )

  __UpperCAmelCase    :						List[str]       =						['''A picture of a woman with a dog sitting in a beach''']
  __UpperCAmelCase    :						Optional[int]       =						tokenizer(
      UpperCAmelCase_  ,			return_tensors='''pt'''  ,			padding='''max_length'''  ,			truncation=UpperCAmelCase_  ,			max_length=35  ,			).input_ids

  hf_itm_model.load_state_dict(UpperCAmelCase_  )
  hf_itm_model.eval()

  __UpperCAmelCase    :						List[Any]       =						hf_itm_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			use_itm_head=UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						hf_itm_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			use_itm_head=UpperCAmelCase_  )

  assert out[0].item() == 0.2_110_687_494_277_954
  assert torch.nn.functional.softmax(out_itm[0]  ,			dim=1  )[:, 1].item() == 0.45_698_845_386_505_127

  if pytorch_dump_folder_path is not None:
    hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''  )


if __name__ == "__main__":
  lowercase__				:str				   =       argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  lowercase__				:int				   =       parser.parse_args()

  convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)