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

    snake_case__						=				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)							),
        ]							)
    snake_case__						=				transform(__lowerCAmelCase							).unsqueeze(0							).to(__lowerCAmelCase							)
    return image





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

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

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

    return key





@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=None							):
    if config_path is not None:
        snake_case__						=				BlipConfig.from_pretrained(__lowerCAmelCase							)
    else:
        snake_case__						=				BlipConfig(projection_dim=512    ,		text_config={}    ,		vision_config={}							)

    snake_case__						=				BlipForConditionalGeneration(__lowerCAmelCase							).eval()

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

    snake_case__						=				blip_decoder(pretrained=__lowerCAmelCase    ,		image_size=384    ,		vit="""base"""							)
    snake_case__						=				pt_model.eval()

    snake_case__						=				pt_model.state_dict()
    for key in modified_state_dict.copy():
        snake_case__						=				modified_state_dict.pop(__lowerCAmelCase							)
        snake_case__						=				rename_key(__lowerCAmelCase							)
        snake_case__						=				value

    hf_model.load_state_dict(__lowerCAmelCase							)

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

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

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

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

    snake_case__						=				BlipForQuestionAnswering(__lowerCAmelCase							)

    hf_vqa_model.load_state_dict(__lowerCAmelCase							)

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

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

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

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

    snake_case__						=				BlipForImageTextRetrieval(__lowerCAmelCase							)

    snake_case__						=				["""A picture of a woman with a dog sitting in a beach"""]
    snake_case__						=				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__						=				hf_itm_model(__lowerCAmelCase    ,		__lowerCAmelCase    ,		use_itm_head=__lowerCAmelCase							)
    snake_case__						=				hf_itm_model(__lowerCAmelCase    ,		__lowerCAmelCase    ,		use_itm_head=__lowerCAmelCase							)

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

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





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )
     parser.add_argument(
         '''--original_config_file''',
         type=str,
         required=True,
         help='''The YAML config file corresponding to the original architecture.''',
     )
     parser.add_argument(
         '''--num_in_channels''',
         default=None,
         type=int,
         help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
     )
     parser.add_argument(
         '''--image_size''',
         default=512,
         type=int,
         help=(
             '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
             ''' Base. Use 768 for Stable Diffusion v2.'''
         ),
     )
     parser.add_argument(
         '''--extract_ema''',
         action='''store_true''',
         help=(
             '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
             ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
             ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
         ),
     )
     parser.add_argument(
         '''--upcast_attention''',
         action='''store_true''',
         help=(
             '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
             ''' diffusion 2.1.'''
         ),
     )
     parser.add_argument(
         '''--from_safetensors''',
         action='''store_true''',
         help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
     )
     parser.add_argument(
         '''--to_safetensors''',
         action='''store_true''',
         help='''Whether to store pipeline in safetensors format or not.''',
     )
     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')





     def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
         if string == "True":
             return True
         elif string == "False":
             return False
         else:
             raise ValueError(F"""could not parse string as bool {string}"""							)

     parser.add_argument(
         '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
     )

     parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

     __magic_name__            =  parser.parse_args()

     __magic_name__            =  download_controlnet_from_original_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         extract_ema=args.extract_ema,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         use_linear_projection=args.use_linear_projection,
         cross_attention_dim=args.cross_attention_dim,
     )





     controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



