


"""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       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Any:







		"""simple docstring"""





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

		A					       = transforms.Compose(
		    [
		        transforms.Resize((image_size, image_size) ,			interpolation=InterpolationMode.BICUBIC	),
		        transforms.ToTensor(),
		        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3) ,			(0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)	),
		    ]	)
		A					       = transform(UpperCamelCase__	).unsqueeze(0	).to(UpperCamelCase__	)
		return image







def  __snake_case       (   UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





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

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

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

		return key







@torch.no_grad()
def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__=None	)      ->		int:







		"""simple docstring"""





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

		A					       = BlipForConditionalGeneration(UpperCamelCase__	).eval()

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

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

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

		hf_model.load_state_dict(UpperCamelCase__	)

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

		A					       = hf_model.generate(UpperCamelCase__ ,			UpperCamelCase__	)

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

		A					       = hf_model.generate(UpperCamelCase__	)

		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(UpperCamelCase__	)

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

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

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

		A					       = BlipForQuestionAnswering(UpperCamelCase__	)

		hf_vqa_model.load_state_dict(UpperCamelCase__	)

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

		A					       = 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'	)

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

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

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

		A					       = BlipForImageTextRetrieval(UpperCamelCase__	)

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

		A					       = hf_itm_model(UpperCamelCase__ ,			UpperCamelCase__ ,			use_itm_head=UpperCamelCase__	)
		A					       = hf_itm_model(UpperCamelCase__ ,			UpperCamelCase__ ,			use_itm_head=UpperCamelCase__	)

		assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
		assert torch.nn.functional.softmax(out_itm[0] ,			dim=1	)[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

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


if __name__ == "__main__":
	UpperCamelCase						:				List[Any]             =      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						:				Tuple             =      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						:				int             =      logging.get_logger(__name__)

UpperCamelCase						:				Optional[int]             =      {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}



class 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_			):
						lowerCAmelCase       =     """bit"""
						lowerCAmelCase       =     ["""preactivation""", """bottleneck"""]
						lowerCAmelCase       =     ["""SAME""", """VALID"""]







						def __init__(							self   :    List[str]			,			_lowercase   :    int=3			,			_lowercase   :    List[str]=64			,			_lowercase   :    Optional[int]=[256, 512, 1_024, 2_048]			,			_lowercase   :    Optional[int]=[3, 4, 6, 3]			,			_lowercase   :    int="preactivation"			,			_lowercase   :    Union[str, Any]="relu"			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Tuple=32			,			_lowercase   :    Optional[int]=0.0			,			_lowercase   :    Union[str, Any]=False			,			_lowercase   :    Dict=32			,			_lowercase   :    Union[str, Any]=1			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Any=None			,			**_lowercase   :    str			,			):
								super().__init__(**_lowercase		)
								if layer_type not in self.layer_types:
										raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
								if global_padding is not None:
										if global_padding.upper() in self.supported_padding:
												A					       = global_padding.upper()
										else:
												raise ValueError(f'Padding strategy {global_padding} not supported'		)
								A					       = num_channels
								A					       = embedding_size
								A					       = hidden_sizes
								A					       = depths
								A					       = layer_type
								A					       = hidden_act
								A					       = global_padding
								A					       = num_groups
								A					       = drop_path_rate
								A					       = embedding_dynamic_padding
								A					       = output_stride
								A					       = width_factor

								A					       = ['stem'] + [f'stage{idx}' for idx in range(1			,			len(_lowercase		) + 1		)]
								A       , A					       = get_aligned_output_features_output_indices(
								    out_features=_lowercase			,			out_indices=_lowercase			,			stage_names=self.stage_names		)





