


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

							lowerCAmelCase__			  =      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)			),
							    ]			)
							lowerCAmelCase__			  =      transform(A			).unsqueeze(0			).to(A			)
							return image


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

							if "encoder.pos_embed" in key:
														lowerCAmelCase__			  =      re.sub(R'''encoder.pos_embed'''					,       '''embeddings.position_embedding'''					,       A			)
							if "encoder.cls_token" in key:
														lowerCAmelCase__			  =      re.sub(R'''encoder.cls_token'''					,       '''embeddings.class_embedding'''					,       A			)

							if "self_attn" in key:
														lowerCAmelCase__			  =      re.sub(R'''self_attn.proj'''					,       '''self_attn.projection'''					,       A			)

							return key


@torch.no_grad()
def    _snake_case  (	A					,       A=None			)		->       str:
							if config_path is not None:
														lowerCAmelCase__			  =      BlipConfig.from_pretrained(A			)
							else:
														lowerCAmelCase__			  =      BlipConfig(projection_dim=512					,       text_config={}					,       vision_config={}			)

							lowerCAmelCase__			  =      BlipForConditionalGeneration(A			).eval()

							lowerCAmelCase__			  =      '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

							lowerCAmelCase__			  =      blip_decoder(pretrained=A					,       image_size=384					,       vit='''base'''			)
							lowerCAmelCase__			  =      pt_model.eval()

							lowerCAmelCase__			  =      pt_model.state_dict()
							for key in modified_state_dict.copy():
														lowerCAmelCase__			  =      modified_state_dict.pop(A			)
														lowerCAmelCase__			  =      rename_key(A			)
														lowerCAmelCase__			  =      value

							hf_model.load_state_dict(A			)

							lowerCAmelCase__			  =      384
							lowerCAmelCase__			  =      load_demo_image(image_size=A					,       device='''cpu'''			)
							lowerCAmelCase__			  =      BertTokenizer.from_pretrained('''bert-base-uncased'''			)
							lowerCAmelCase__			  =      tokenizer(['''a picture of''']			).input_ids

							lowerCAmelCase__			  =      hf_model.generate(A					,       A			)

							assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

							lowerCAmelCase__			  =      hf_model.generate(A			)

							assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

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

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

							lowerCAmelCase__			  =      blip_vqa(pretrained=A					,       image_size=A					,       vit='''base'''			)
							vqa_model.eval()

							lowerCAmelCase__			  =      vqa_model.state_dict()
							for key in modified_state_dict.copy():
														lowerCAmelCase__			  =      modified_state_dict.pop(A			)
														lowerCAmelCase__			  =      rename_key(A			)
														lowerCAmelCase__			  =      value

							lowerCAmelCase__			  =      BlipForQuestionAnswering(A			)

							hf_vqa_model.load_state_dict(A			)

							lowerCAmelCase__			  =      ['''How many dogs are in this image?''']
							lowerCAmelCase__			  =      tokenizer(A					,       return_tensors='''pt'''			).input_ids

							lowerCAmelCase__			  =      hf_vqa_model.generate(A					,       A			)
							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'''			)

							lowerCAmelCase__			  =      '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

							lowerCAmelCase__			  =      blip_itm(pretrained=A					,       image_size=A					,       vit='''base'''			)
							itm_model.eval()

							lowerCAmelCase__			  =      itm_model.state_dict()
							for key in modified_state_dict.copy():
														lowerCAmelCase__			  =      modified_state_dict.pop(A			)
														lowerCAmelCase__			  =      rename_key(A			)
														lowerCAmelCase__			  =      value

							lowerCAmelCase__			  =      BlipForImageTextRetrieval(A			)

							lowerCAmelCase__			  =      ['''A picture of a woman with a dog sitting in a beach''']
							lowerCAmelCase__			  =      tokenizer(
							    A					,       return_tensors='''pt'''					,       padding='''max_length'''					,       truncation=A					,       max_length=35					,       ).input_ids

							hf_itm_model.load_state_dict(A			)
							hf_itm_model.eval()

							lowerCAmelCase__			  =      hf_itm_model(A					,       A					,       use_itm_head=A			)
							lowerCAmelCase__			  =      hf_itm_model(A					,       A					,       use_itm_head=A			)

							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__":
		__UpperCAmelCase       							=				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''')
		__UpperCAmelCase       							=				parser.parse_args()

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





'''simple docstring'''



import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def       _UpperCamelCase			(*_lowerCamelCase				:  str ,						_lowerCamelCase				:  Optional[Union[Dict, Any]] = None ,						_lowerCamelCase				:  List[Any]=True ,						_lowerCamelCase				:  str=2						)-> str:




     '''simple docstring'''





     from .. import __version__

     __snake_case            =	take_from
     __snake_case            =	()
     if not isinstance(args[0] ,						_lowerCamelCase						):
          __snake_case            =	(args,)

     for attribute, version_name, message in args:
          if version.parse(version.parse(_lowerCamelCase						).base_version						) >= version.parse(_lowerCamelCase						):
               raise ValueError(
                   f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
                   f''' version {__version__} is >= {version_name}'''						)

          __snake_case            =	None
          if isinstance(_lowerCamelCase ,						_lowerCamelCase						) and attribute in deprecated_kwargs:
               values += (deprecated_kwargs.pop(_lowerCamelCase						),)
               __snake_case            =	f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
          elif hasattr(_lowerCamelCase ,						_lowerCamelCase						):
               values += (getattr(_lowerCamelCase ,						_lowerCamelCase						),)
               __snake_case            =	f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
          elif deprecated_kwargs is None:
               __snake_case            =	f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

          if warning is not None:
               __snake_case            =	warning + ''' ''' if standard_warn else ''''''
               warnings.warn(warning + message ,						_lowerCamelCase ,						stacklevel=_lowerCamelCase						)

     if isinstance(_lowerCamelCase ,						_lowerCamelCase						) and len(_lowerCamelCase						) > 0:
          __snake_case            =	inspect.getouterframes(inspect.currentframe()						)[1]
          __snake_case            =	call_frame.filename
          __snake_case            =	call_frame.lineno
          __snake_case            =	call_frame.function
          __snake_case       ,     __snake_case            =	next(iter(deprecated_kwargs.items()						)						)
          raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''						)

     if len(_lowerCamelCase						) == 0:
          return
     elif len(_lowerCamelCase						) == 1:
          return values[0]
     return values






