



'''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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					str:
      snake_case__    :       Dict       		= """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
      snake_case__    :       Tuple       		= Image.open(requests.get(_lowerCAmelCase	,  stream=_lowerCAmelCase						).raw						).convert("""RGB"""						)

      snake_case__    :       int       		= transforms.Compose(
          [
              transforms.Resize((image_size, image_size)	,  interpolation=InterpolationMode.BICUBIC						),
              transforms.ToTensor(),
              transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)	,  (0.2686_2954, 0.2613_0258, 0.2757_7711)						),
          ]						)
      snake_case__    :       int       		= transform(_lowerCAmelCase						).unsqueeze(0						).to(_lowerCAmelCase						)
      return image





def 		__snake_case(							_lowerCAmelCase						)						->					str:
      if "visual_encoder" in key:
            snake_case__    :       Optional[int]       		= re.sub("""visual_encoder*"""	,  """vision_model.encoder"""	,  _lowerCAmelCase						)
      if "blocks" in key:
            snake_case__    :       Dict       		= re.sub(r"""blocks"""	,  """layers"""	,  _lowerCAmelCase						)
      if "attn" in key:
            snake_case__    :       Any       		= re.sub(r"""attn"""	,  """self_attn"""	,  _lowerCAmelCase						)
      if "norm1" in key:
            snake_case__    :       Tuple       		= re.sub(r"""norm1"""	,  """layer_norm1"""	,  _lowerCAmelCase						)
      if "norm2" in key:
            snake_case__    :       int       		= re.sub(r"""norm2"""	,  """layer_norm2"""	,  _lowerCAmelCase						)
      if "encoder.norm" in key:
            snake_case__    :       Optional[Any]       		= re.sub(r"""encoder.norm"""	,  """post_layernorm"""	,  _lowerCAmelCase						)
      if "encoder.patch_embed.proj" in key:
            snake_case__    :       Any       		= re.sub(r"""encoder.patch_embed.proj"""	,  """embeddings.patch_embedding"""	,  _lowerCAmelCase						)

      if "encoder.pos_embed" in key:
            snake_case__    :       Optional[int]       		= re.sub(r"""encoder.pos_embed"""	,  """embeddings.position_embedding"""	,  _lowerCAmelCase						)
      if "encoder.cls_token" in key:
            snake_case__    :       Optional[Any]       		= re.sub(r"""encoder.cls_token"""	,  """embeddings.class_embedding"""	,  _lowerCAmelCase						)

      if "self_attn" in key:
            snake_case__    :       Optional[int]       		= re.sub(r"""self_attn.proj"""	,  """self_attn.projection"""	,  _lowerCAmelCase						)

      return key





@torch.no_grad()
def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase=None						)						->					int:
      if config_path is not None:
            snake_case__    :       Optional[Any]       		= BlipConfig.from_pretrained(_lowerCAmelCase						)
      else:
            snake_case__    :       Optional[Any]       		= BlipConfig(projection_dim=512	,  text_config={}	,  vision_config={}						)

      snake_case__    :       Optional[int]       		= BlipForConditionalGeneration(_lowerCAmelCase						).eval()

      snake_case__    :       Optional[int]       		= """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""

      snake_case__    :       List[str]       		= blip_decoder(pretrained=_lowerCAmelCase	,  image_size=384	,  vit="""base"""						)
      snake_case__    :       int       		= pt_model.eval()

      snake_case__    :       str       		= pt_model.state_dict()
      for key in modified_state_dict.copy():
            snake_case__    :       Dict       		= modified_state_dict.pop(_lowerCAmelCase						)
            snake_case__    :       List[str]       		= rename_key(_lowerCAmelCase						)
            snake_case__    :       Dict       		= value

      hf_model.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       Dict       		= 384
      snake_case__    :       Tuple       		= load_demo_image(image_size=_lowerCAmelCase	,  device="""cpu"""						)
      snake_case__    :       List[Any]       		= BertTokenizer.from_pretrained("""bert-base-uncased"""						)
      snake_case__    :       Tuple       		= tokenizer(["""a picture of"""]						).input_ids

      snake_case__    :       Optional[int]       		= hf_model.generate(_lowerCAmelCase	,  _lowerCAmelCase						)

      assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

      snake_case__    :       List[str]       		= hf_model.generate(_lowerCAmelCase						)

      assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

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

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

      snake_case__    :       str       		= blip_vqa(pretrained=_lowerCAmelCase	,  image_size=_lowerCAmelCase	,  vit="""base"""						)
      vqa_model.eval()

      snake_case__    :       Any       		= vqa_model.state_dict()
      for key in modified_state_dict.copy():
            snake_case__    :       Optional[int]       		= modified_state_dict.pop(_lowerCAmelCase						)
            snake_case__    :       Optional[Any]       		= rename_key(_lowerCAmelCase						)
            snake_case__    :       Dict       		= value

      snake_case__    :       List[Any]       		= BlipForQuestionAnswering(_lowerCAmelCase						)

      hf_vqa_model.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       str       		= ["""How many dogs are in this image?"""]
      snake_case__    :       Optional[Any]       		= tokenizer(_lowerCAmelCase	,  return_tensors="""pt"""						).input_ids

      snake_case__    :       Tuple       		= hf_vqa_model.generate(_lowerCAmelCase	,  _lowerCAmelCase						)
      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"""						)

      snake_case__    :       str       		= """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

      snake_case__    :       Tuple       		= blip_itm(pretrained=_lowerCAmelCase	,  image_size=_lowerCAmelCase	,  vit="""base"""						)
      itm_model.eval()

      snake_case__    :       Union[str, Any]       		= itm_model.state_dict()
      for key in modified_state_dict.copy():
            snake_case__    :       int       		= modified_state_dict.pop(_lowerCAmelCase						)
            snake_case__    :       Optional[Any]       		= rename_key(_lowerCAmelCase						)
            snake_case__    :       Union[str, Any]       		= value

      snake_case__    :       str       		= BlipForImageTextRetrieval(_lowerCAmelCase						)

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

      hf_itm_model.load_state_dict(_lowerCAmelCase						)
      hf_itm_model.eval()

      snake_case__    :       str       		= hf_itm_model(_lowerCAmelCase	,  _lowerCAmelCase	,  use_itm_head=_lowerCAmelCase						)
      snake_case__    :       Dict       		= hf_itm_model(_lowerCAmelCase	,  _lowerCAmelCase	,  use_itm_head=_lowerCAmelCase						)

      assert out[0].item() == 0.2110_6874_9427_7954
      assert torch.nn.functional.softmax(out_itm[0]	,  dim=1						)[:, 1].item() == 0.4_5698_8453_8650_5127

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


if __name__ == "__main__":
       __a  			=						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")
       __a  			=						parser.parse_args()

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





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a  			=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTBigCodeForSequenceClassification",
           "GPTBigCodeForTokenClassification",
           "GPTBigCodeForCausalLM",
           "GPTBigCodeModel",
           "GPTBigCodePreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_bigcode import (
                  GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTBigCodeForCausalLM,
                  GPTBigCodeForSequenceClassification,
                  GPTBigCodeForTokenClassification,
                  GPTBigCodeModel,
                  GPTBigCodePreTrainedModel,
              )


else:
       import sys

       __a  			=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
