


"""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      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			"https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
			__lowerCAmelCase				    =			Image.open(requests.get(_UpperCamelCase    , stream=_UpperCamelCase      ).raw      ).convert("RGB"      )

			__lowerCAmelCase				    =			transforms.Compose(
			    [
			        transforms.Resize((image_size, image_size)    , interpolation=InterpolationMode.BICUBIC      ),
			        transforms.ToTensor(),
			        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)    , (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)      ),
			    ]      )
			__lowerCAmelCase				    =			transform(_UpperCamelCase      ).unsqueeze(0      ).to(_UpperCamelCase      )
			return image


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			if "visual_encoder" in key:
						__lowerCAmelCase				    =			re.sub("visual_encoder*"    , "vision_model.encoder"    , _UpperCamelCase      )
			if "blocks" in key:
						__lowerCAmelCase				    =			re.sub(R"blocks"    , "layers"    , _UpperCamelCase      )
			if "attn" in key:
						__lowerCAmelCase				    =			re.sub(R"attn"    , "self_attn"    , _UpperCamelCase      )
			if "norm1" in key:
						__lowerCAmelCase				    =			re.sub(R"norm1"    , "layer_norm1"    , _UpperCamelCase      )
			if "norm2" in key:
						__lowerCAmelCase				    =			re.sub(R"norm2"    , "layer_norm2"    , _UpperCamelCase      )
			if "encoder.norm" in key:
						__lowerCAmelCase				    =			re.sub(R"encoder.norm"    , "post_layernorm"    , _UpperCamelCase      )
			if "encoder.patch_embed.proj" in key:
						__lowerCAmelCase				    =			re.sub(R"encoder.patch_embed.proj"    , "embeddings.patch_embedding"    , _UpperCamelCase      )

			if "encoder.pos_embed" in key:
						__lowerCAmelCase				    =			re.sub(R"encoder.pos_embed"    , "embeddings.position_embedding"    , _UpperCamelCase      )
			if "encoder.cls_token" in key:
						__lowerCAmelCase				    =			re.sub(R"encoder.cls_token"    , "embeddings.class_embedding"    , _UpperCamelCase      )

			if "self_attn" in key:
						__lowerCAmelCase				    =			re.sub(R"self_attn.proj"    , "self_attn.projection"    , _UpperCamelCase      )

			return key


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=None      ):

			'''simple docstring'''




			if config_path is not None:
						__lowerCAmelCase				    =			BlipConfig.from_pretrained(_UpperCamelCase      )
			else:
						__lowerCAmelCase				    =			BlipConfig(projection_dim=512    , text_config={}    , vision_config={}      )

			__lowerCAmelCase				    =			BlipForConditionalGeneration(_UpperCamelCase      ).eval()

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

			__lowerCAmelCase				    =			blip_decoder(pretrained=_UpperCamelCase    , 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(_UpperCamelCase      )
						__lowerCAmelCase				    =			rename_key(_UpperCamelCase      )
						__lowerCAmelCase				    =			value

			hf_model.load_state_dict(_UpperCamelCase      )

			__lowerCAmelCase				    =			384
			__lowerCAmelCase				    =			load_demo_image(image_size=_UpperCamelCase    , device="cpu"      )
			__lowerCAmelCase				    =			BertTokenizer.from_pretrained("bert-base-uncased"      )
			__lowerCAmelCase				    =			tokenizer(["a picture of"]      ).input_ids

			__lowerCAmelCase				    =			hf_model.generate(_UpperCamelCase    , _UpperCamelCase      )

			assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

			__lowerCAmelCase				    =			hf_model.generate(_UpperCamelCase      )

			assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

			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'
			__lowerCAmelCase				    =			(
			    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
			)

			__lowerCAmelCase				    =			blip_vqa(pretrained=_UpperCamelCase    , image_size=_UpperCamelCase    , vit="base"      )
			vqa_model.eval()

			__lowerCAmelCase				    =			vqa_model.state_dict()
			for key in modified_state_dict.copy():
						__lowerCAmelCase				    =			modified_state_dict.pop(_UpperCamelCase      )
						__lowerCAmelCase				    =			rename_key(_UpperCamelCase      )
						__lowerCAmelCase				    =			value

			__lowerCAmelCase				    =			BlipForQuestionAnswering(_UpperCamelCase      )

			hf_vqa_model.load_state_dict(_UpperCamelCase      )

			__lowerCAmelCase				    =			["How many dogs are in this image?"]
			__lowerCAmelCase				    =			tokenizer(_UpperCamelCase    , return_tensors="pt"      ).input_ids

			__lowerCAmelCase				    =			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"      )

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

			__lowerCAmelCase				    =			blip_itm(pretrained=_UpperCamelCase    , image_size=_UpperCamelCase    , vit="base"      )
			itm_model.eval()

			__lowerCAmelCase				    =			itm_model.state_dict()
			for key in modified_state_dict.copy():
						__lowerCAmelCase				    =			modified_state_dict.pop(_UpperCamelCase      )
						__lowerCAmelCase				    =			rename_key(_UpperCamelCase      )
						__lowerCAmelCase				    =			value

			__lowerCAmelCase				    =			BlipForImageTextRetrieval(_UpperCamelCase      )

			__lowerCAmelCase				    =			["A picture of a woman with a dog sitting in a beach"]
			__lowerCAmelCase				    =			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()

			__lowerCAmelCase				    =			hf_itm_model(_UpperCamelCase    , _UpperCamelCase    , use_itm_head=_UpperCamelCase      )
			__lowerCAmelCase				    =			hf_itm_model(_UpperCamelCase    , _UpperCamelCase    , use_itm_head=_UpperCamelCase      )

			assert out[0].item() == 0.21_10_68_74_94_27_79_54
			assert torch.nn.functional.softmax(out_itm[0]    , dim=1      )[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

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


if __name__ == "__main__":
	A     :   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")
	A     :   Optional[int]			    =			parser.parse_args()

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










import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase				:		Union[str, Any]     =   "src/diffusers"

# Matches is_xxx_available()
lowerCamelCase				:		Dict     =   re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
lowerCamelCase				:		Union[str, Any]     =   re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


lowerCamelCase				:		Any     =   "\n{0} = None\n"

lowerCamelCase				:		List[str]     =   "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


lowerCamelCase				:		str     =   "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    _re_backend.findall(lowercase     )
      if len(lowercase     ) == 0:
            return None

      return "_and_".join(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      with open(os.path.join(lowercase	,							'__init__.py'     )	,							'r'	,							encoding='utf-8'	,							newline='\n'     ) as f:
            lowerCamelCase_		     =    f.readlines()

      # Get to the point we do the actual imports for type checking
      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    {}
      # Go through the end of the file
      while line_index < len(lowercase     ):
            # If the line contains is_backend_available, we grab all objects associated with the `else` block
            lowerCamelCase_		     =    find_backend(lines[line_index]     )
            if backend is not None:
                  while not lines[line_index].startswith('else:'     ):
                        line_index += 1
                  line_index += 1
                  lowerCamelCase_		     =    []
                  # Until we unindent, add backend objects to the list
                  while line_index < len(lowercase     ) and len(lines[line_index]     ) > 1:
                        lowerCamelCase_		     =    lines[line_index]
                        lowerCamelCase_		     =    _re_single_line_import.search(lowercase     )
                        if single_line_import_search is not None:
                              objects.extend(single_line_import_search.groups()[0].split(', '     )     )
                        elif line.startswith(' ' * 8     ):
                              objects.append(line[8:-2]     )
                        line_index += 1

                  if len(lowercase     ) > 0:
                        lowerCamelCase_		     =    objects
            else:
                  line_index += 1

      return backend_specific_objects

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]	,							lowercase	:       str     ):


      '''simple docstring'''



      if name.isupper():
            return DUMMY_CONSTANT.format(lowercase     )
      elif name.islower():
            return DUMMY_FUNCTION.format(lowercase	,							lowercase     )
      else:
            return DUMMY_CLASS.format(lowercase	,							lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]=None     ):


      '''simple docstring'''



      if backend_specific_objects is None:
            lowerCamelCase_		     =    read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      lowerCamelCase_		     =    {}

      for backend, objects in backend_specific_objects.items():
            lowerCamelCase_		     =    '[' + ', '.join(f"""\"{b}\"""" for b in backend.split('_and_'     )     ) + ']'
            lowerCamelCase_		     =    '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
            dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
            dummy_file += "\n".join([create_dummy_object(lowercase	,							lowercase     ) for o in objects]     )
            lowerCamelCase_		     =    dummy_file

      return dummy_files

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]=False     ):


      '''simple docstring'''



      lowerCamelCase_		     =    create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      lowerCamelCase_		     =    {'torch': 'pt'}

      # Locate actual dummy modules and read their content.
      lowerCamelCase_		     =    os.path.join(lowercase	,							'utils'     )
      lowerCamelCase_		     =    {
          backend: os.path.join(lowercase	,							f"""dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py"""     )
          for backend in dummy_files.keys()
      }

      lowerCamelCase_		     =    {}
      for backend, file_path in dummy_file_paths.items():
            if os.path.isfile(lowercase     ):
                  with open(lowercase	,							'r'	,							encoding='utf-8'	,							newline='\n'     ) as f:
                        lowerCamelCase_		     =    f.read()
            else:
                  lowerCamelCase_		     =    ''

      for backend in dummy_files.keys():
            if dummy_files[backend] != actual_dummies[backend]:
                  if overwrite:
                        print(
                            f"""Updating diffusers.utils.dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py as the main """
                            '__init__ has new objects.'     )
                        with open(dummy_file_paths[backend]	,							'w'	,							encoding='utf-8'	,							newline='\n'     ) as f:
                              f.write(dummy_files[backend]     )
                  else:
                        raise ValueError(
                            'The main __init__ has objects that are not present in '
                            f"""diffusers.utils.dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py. Run `make fix-copies` """
                            'to fix this.'     )


if __name__ == "__main__":
 lowerCamelCase				:		List[Any]     =   argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 lowerCamelCase				:		Tuple     =   parser.parse_args()

 check_dummies(args.fix_and_overwrite)



