
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(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     List[Any]       	=      """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
       lowercase							:     Dict       	=      Image.open(requests.get(SCREAMING_SNAKE_CASE__					,	stream=SCREAMING_SNAKE_CASE__				).raw				).convert("""RGB"""				)

       lowercase							:     int       	=      transforms.Compose(
           [
               transforms.Resize((image_size, image_size)					,	interpolation=InterpolationMode.BICUBIC				),
               transforms.ToTensor(),
               transforms.Normalize((0.48145466, 0.4578275, 0.40821073)					,	(0.26862954, 0.26130258, 0.27577711)				),
           ]				)
       lowercase							:     Optional[Any]       	=      transform(SCREAMING_SNAKE_CASE__				).unsqueeze(0				).to(SCREAMING_SNAKE_CASE__				)
       return image







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

       if "encoder.pos_embed" in key:
              lowercase							:     Tuple       	=      re.sub(R"""encoder.pos_embed"""					,	"""embeddings.position_embedding"""					,	SCREAMING_SNAKE_CASE__				)
       if "encoder.cls_token" in key:
              lowercase							:     Dict       	=      re.sub(R"""encoder.cls_token"""					,	"""embeddings.class_embedding"""					,	SCREAMING_SNAKE_CASE__				)

       if "self_attn" in key:
              lowercase							:     Tuple       	=      re.sub(R"""self_attn.proj"""					,	"""self_attn.projection"""					,	SCREAMING_SNAKE_CASE__				)

       return key







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None				) ->    int:
       if config_path is not None:
              lowercase							:     Dict       	=      BlipConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
       else:
              lowercase							:     Tuple       	=      BlipConfig(projection_dim=512					,	text_config={}					,	vision_config={}				)

       lowercase							:     List[str]       	=      BlipForConditionalGeneration(SCREAMING_SNAKE_CASE__				).eval()

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

       lowercase							:     Tuple       	=      blip_decoder(pretrained=SCREAMING_SNAKE_CASE__					,	image_size=384					,	vit="""base"""				)
       lowercase							:     Optional[int]       	=      pt_model.eval()

       lowercase							:     Union[str, Any]       	=      pt_model.state_dict()
       for key in modified_state_dict.copy():
              lowercase							:     List[Any]       	=      modified_state_dict.pop(SCREAMING_SNAKE_CASE__				)
              lowercase							:     List[str]       	=      rename_key(SCREAMING_SNAKE_CASE__				)
              lowercase							:     List[str]       	=      value

       hf_model.load_state_dict(SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      384
       lowercase							:     Union[str, Any]       	=      load_demo_image(image_size=SCREAMING_SNAKE_CASE__					,	device="""cpu"""				)
       lowercase							:     Union[str, Any]       	=      BertTokenizer.from_pretrained("""bert-base-uncased"""				)
       lowercase							:     str       	=      tokenizer(["""a picture of"""]				).input_ids

       lowercase							:     Any       	=      hf_model.generate(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       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]

       lowercase							:     List[str]       	=      hf_model.generate(SCREAMING_SNAKE_CASE__				)

       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(SCREAMING_SNAKE_CASE__				)

       # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
       lowercase							:     Dict       	=      (
           """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
       )

       lowercase							:     Dict       	=      blip_vqa(pretrained=SCREAMING_SNAKE_CASE__					,	image_size=SCREAMING_SNAKE_CASE__					,	vit="""base"""				)
       vqa_model.eval()

       lowercase							:     str       	=      vqa_model.state_dict()
       for key in modified_state_dict.copy():
              lowercase							:     Any       	=      modified_state_dict.pop(SCREAMING_SNAKE_CASE__				)
              lowercase							:     int       	=      rename_key(SCREAMING_SNAKE_CASE__				)
              lowercase							:     Tuple       	=      value

       lowercase							:     Dict       	=      BlipForQuestionAnswering(SCREAMING_SNAKE_CASE__				)

       hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Union[str, Any]       	=      ["""How many dogs are in this image?"""]
       lowercase							:     Any       	=      tokenizer(SCREAMING_SNAKE_CASE__					,	return_tensors="""pt"""				).input_ids

       lowercase							:     int       	=      hf_vqa_model.generate(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       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"""				)

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

       lowercase							:     Dict       	=      blip_itm(pretrained=SCREAMING_SNAKE_CASE__					,	image_size=SCREAMING_SNAKE_CASE__					,	vit="""base"""				)
       itm_model.eval()

       lowercase							:     int       	=      itm_model.state_dict()
       for key in modified_state_dict.copy():
              lowercase							:     List[str]       	=      modified_state_dict.pop(SCREAMING_SNAKE_CASE__				)
              lowercase							:     Tuple       	=      rename_key(SCREAMING_SNAKE_CASE__				)
              lowercase							:     int       	=      value

       lowercase							:     Optional[int]       	=      BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Dict       	=      ["""A picture of a woman with a dog sitting in a beach"""]
       lowercase							:     Tuple       	=      tokenizer(
           SCREAMING_SNAKE_CASE__					,	return_tensors="""pt"""					,	padding="""max_length"""					,	truncation=SCREAMING_SNAKE_CASE__					,	max_length=35					,	).input_ids

       hf_itm_model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       hf_itm_model.eval()

       lowercase							:     Union[str, Any]       	=      hf_itm_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	use_itm_head=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Any       	=      hf_itm_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	use_itm_head=SCREAMING_SNAKE_CASE__				)

       assert out[0].item() == 0.2110687494277954
       assert torch.nn.functional.softmax(out_itm[0]					,	dim=1				)[:, 1].item() == 0.45698845386505127

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


if __name__ == "__main__":
  lowercase  :					Optional[int]						    =							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)




from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands



def 	lowerCAmelCase       (						)->       int:
				lowerCAmelCase_       :					int			 = ArgumentParser('''Transformers CLI tool'''       ,      usage='''transformers-cli <command> [<args>]'''       )
				lowerCAmelCase_       :					Dict			 = parser.add_subparsers(help='''transformers-cli command helpers'''       )

				# Register commands
				ConvertCommand.register_subcommand(lowerCAmelCase_       )
				DownloadCommand.register_subcommand(lowerCAmelCase_       )
				EnvironmentCommand.register_subcommand(lowerCAmelCase_       )
				RunCommand.register_subcommand(lowerCAmelCase_       )
				ServeCommand.register_subcommand(lowerCAmelCase_       )
				UserCommands.register_subcommand(lowerCAmelCase_       )
				AddNewModelCommand.register_subcommand(lowerCAmelCase_       )
				AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_       )
				LfsCommands.register_subcommand(lowerCAmelCase_       )
				PTtoTFCommand.register_subcommand(lowerCAmelCase_       )

				# Let's go
				lowerCAmelCase_       :					Union[str, Any]			 = parser.parse_args()

				if not hasattr(lowerCAmelCase_       ,      '''func'''       ):
								parser.print_help()
								exit(1       )

				# Run
				lowerCAmelCase_       :					List[Any]			 = args.func(lowerCAmelCase_       )
				service.run()


if __name__ == "__main__":
						main()