

'''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 					__UpperCamelCase				(						lowercase__    :	Optional[Any],       lowercase__    :	List[str]			):






     '''simple docstring'''





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

     __lowercase      		=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      		=transform(lowercase__			).unsqueeze(0			).to(lowercase__			)
     return image

def 					__UpperCamelCase				(						lowercase__    :	List[str]			):






     '''simple docstring'''





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

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

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

     return key

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	List[str],       lowercase__    :	Dict=None			):






     '''simple docstring'''





     if config_path is not None:
          __lowercase      		=BlipConfig.from_pretrained(lowercase__			)
     else:
          __lowercase      		=BlipConfig(projection_dim=5_12,       text_config={},       vision_config={}			)

     __lowercase      		=BlipForConditionalGeneration(lowercase__			).eval()

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

     __lowercase      		=blip_decoder(pretrained=lowercase__,       image_size=3_84,       vit='base'			)
     __lowercase      		=pt_model.eval()

     __lowercase      		=pt_model.state_dict()
     for key in modified_state_dict.copy():
          __lowercase      		=modified_state_dict.pop(lowercase__			)
          __lowercase      		=rename_key(lowercase__			)
          __lowercase      		=value

     hf_model.load_state_dict(lowercase__			)

     __lowercase      		=3_84
     __lowercase      		=load_demo_image(image_size=lowercase__,       device='cpu'			)
     __lowercase      		=BertTokenizer.from_pretrained('bert-base-uncased'			)
     __lowercase      		=tokenizer(['a picture of']			).input_ids

     __lowercase      		=hf_model.generate(lowercase__,       lowercase__			)

     assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

     __lowercase      		=hf_model.generate(lowercase__			)

     assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

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

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

     __lowercase      		=blip_vqa(pretrained=lowercase__,       image_size=lowercase__,       vit='base'			)
     vqa_model.eval()

     __lowercase      		=vqa_model.state_dict()
     for key in modified_state_dict.copy():
          __lowercase      		=modified_state_dict.pop(lowercase__			)
          __lowercase      		=rename_key(lowercase__			)
          __lowercase      		=value

     __lowercase      		=BlipForQuestionAnswering(lowercase__			)

     hf_vqa_model.load_state_dict(lowercase__			)

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

     __lowercase      		=hf_vqa_model.generate(lowercase__,       lowercase__			)
     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      		='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

     __lowercase      		=blip_itm(pretrained=lowercase__,       image_size=lowercase__,       vit='base'			)
     itm_model.eval()

     __lowercase      		=itm_model.state_dict()
     for key in modified_state_dict.copy():
          __lowercase      		=modified_state_dict.pop(lowercase__			)
          __lowercase      		=rename_key(lowercase__			)
          __lowercase      		=value

     __lowercase      		=BlipForImageTextRetrieval(lowercase__			)

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

     hf_itm_model.load_state_dict(lowercase__			)
     hf_itm_model.eval()

     __lowercase      		=hf_itm_model(lowercase__,       lowercase__,       use_itm_head=lowercase__			)
     __lowercase      		=hf_itm_model(lowercase__,       lowercase__,       use_itm_head=lowercase__			)

     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__":
       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'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}


class 	lowerCAmelCase  (   A ,      A ):
       lowerCAmelCase_												=			"dinat"

       lowerCAmelCase_												=			{
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self      :			int    ,   __lowercase      :			str=4    ,   __lowercase      :			str=3    ,   __lowercase      :			int=64    ,   __lowercase      :			int=[3, 4, 6, 5]    ,   __lowercase      :			Union[str, Any]=[2, 4, 8, 16]    ,   __lowercase      :			Optional[int]=7    ,   __lowercase      :			str=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]    ,   __lowercase      :			Any=3.0    ,   __lowercase      :			Optional[Any]=True    ,   __lowercase      :			List[Any]=0.0    ,   __lowercase      :			Optional[Any]=0.0    ,   __lowercase      :			int=0.1    ,   __lowercase      :			Union[str, Any]="gelu"    ,   __lowercase      :			Any=0.0_2    ,   __lowercase      :			int=1E-5    ,   __lowercase      :			Optional[int]=0.0    ,   __lowercase      :			Optional[int]=None    ,   __lowercase      :			str=None    ,   **__lowercase      :			Any    ,   ):







            """simple docstring"""







            super().__init__(**__lowercase   )

            __lowercase      		=patch_size
            __lowercase      		=num_channels
            __lowercase      		=embed_dim
            __lowercase      		=depths
            __lowercase      		=len(__lowercase   )
            __lowercase      		=num_heads
            __lowercase      		=kernel_size
            __lowercase      		=dilations
            __lowercase      		=mlp_ratio
            __lowercase      		=qkv_bias
            __lowercase      		=hidden_dropout_prob
            __lowercase      		=attention_probs_dropout_prob
            __lowercase      		=drop_path_rate
            __lowercase      		=hidden_act
            __lowercase      		=layer_norm_eps
            __lowercase      		=initializer_range
            # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
            # this indicates the channel dimension after the last stage of the model
            __lowercase      		=int(embed_dim * 2 ** (len(__lowercase   ) - 1)   )
            __lowercase      		=layer_scale_init_value
            __lowercase      		=['stem'] + [f'''stage{idx}''' for idx in range(1    ,   len(__lowercase   ) + 1   )]
            __lowercase		,  __lowercase      		=get_aligned_output_features_output_indices(
                out_features=__lowercase    ,   out_indices=__lowercase    ,   stage_names=self.stage_names   )


