





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase__				:Tuple				   =       {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__				:Optional[Any]				   =       [
      'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLMForMultipleChoice',
      'XLMForQuestionAnswering',
      'XLMForQuestionAnsweringSimple',
      'XLMForSequenceClassification',
      'XLMForTokenClassification',
      'XLMModel',
      'XLMPreTrainedModel',
      'XLMWithLMHeadModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__				:List[Any]				   =       [
      'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLMForMultipleChoice',
      'TFXLMForQuestionAnsweringSimple',
      'TFXLMForSequenceClassification',
      'TFXLMForTokenClassification',
      'TFXLMMainLayer',
      'TFXLMModel',
      'TFXLMPreTrainedModel',
      'TFXLMWithLMHeadModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  lowercase__				:List[Any]				   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






"""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)