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

							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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)		),
							    ]		)
							SCREAMING_SNAKE_CASE__		: Any     =     transform(_snake_case		).unsqueeze(0		).to(_snake_case		)
							return image


def lowercase_						(     _snake_case		):
							if "visual_encoder" in key:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     re.sub("""visual_encoder*"""     ,"""vision_model.encoder"""     ,_snake_case		)
							if "blocks" in key:
														SCREAMING_SNAKE_CASE__		: List[str]     =     re.sub(R"""blocks"""     ,"""layers"""     ,_snake_case		)
							if "attn" in key:
														SCREAMING_SNAKE_CASE__		: Dict     =     re.sub(R"""attn"""     ,"""self_attn"""     ,_snake_case		)
							if "norm1" in key:
														SCREAMING_SNAKE_CASE__		: List[str]     =     re.sub(R"""norm1"""     ,"""layer_norm1"""     ,_snake_case		)
							if "norm2" in key:
														SCREAMING_SNAKE_CASE__		: Tuple     =     re.sub(R"""norm2"""     ,"""layer_norm2"""     ,_snake_case		)
							if "encoder.norm" in key:
														SCREAMING_SNAKE_CASE__		: List[str]     =     re.sub(R"""encoder.norm"""     ,"""post_layernorm"""     ,_snake_case		)
							if "encoder.patch_embed.proj" in key:
														SCREAMING_SNAKE_CASE__		: int     =     re.sub(R"""encoder.patch_embed.proj"""     ,"""embeddings.patch_embedding"""     ,_snake_case		)

							if "encoder.pos_embed" in key:
														SCREAMING_SNAKE_CASE__		: List[str]     =     re.sub(R"""encoder.pos_embed"""     ,"""embeddings.position_embedding"""     ,_snake_case		)
							if "encoder.cls_token" in key:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     re.sub(R"""encoder.cls_token"""     ,"""embeddings.class_embedding"""     ,_snake_case		)

							if "self_attn" in key:
														SCREAMING_SNAKE_CASE__		: str     =     re.sub(R"""self_attn.proj"""     ,"""self_attn.projection"""     ,_snake_case		)

							return key


@torch.no_grad()
def lowercase_						(     _snake_case     ,_snake_case=None		):
							if config_path is not None:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     BlipConfig.from_pretrained(_snake_case		)
							else:
														SCREAMING_SNAKE_CASE__		: List[str]     =     BlipConfig(projection_dim=512     ,text_config={}     ,vision_config={}		)

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     BlipForConditionalGeneration(_snake_case		).eval()

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

							SCREAMING_SNAKE_CASE__		: Any     =     blip_decoder(pretrained=_snake_case     ,image_size=384     ,vit="""base"""		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     pt_model.eval()

							SCREAMING_SNAKE_CASE__		: List[Any]     =     pt_model.state_dict()
							for key in modified_state_dict.copy():
														SCREAMING_SNAKE_CASE__		: str     =     modified_state_dict.pop(_snake_case		)
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     rename_key(_snake_case		)
														SCREAMING_SNAKE_CASE__		: Dict     =     value

							hf_model.load_state_dict(_snake_case		)

							SCREAMING_SNAKE_CASE__		: List[str]     =     384
							SCREAMING_SNAKE_CASE__		: int     =     load_demo_image(image_size=_snake_case     ,device="""cpu"""		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     BertTokenizer.from_pretrained("""bert-base-uncased"""		)
							SCREAMING_SNAKE_CASE__		: str     =     tokenizer(["""a picture of"""]		).input_ids

							SCREAMING_SNAKE_CASE__		: Any     =     hf_model.generate(_snake_case     ,_snake_case		)

							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]

							SCREAMING_SNAKE_CASE__		: Tuple     =     hf_model.generate(_snake_case		)

							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(_snake_case		)

							# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     (
							    """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
							)

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     blip_vqa(pretrained=_snake_case     ,image_size=_snake_case     ,vit="""base"""		)
							vqa_model.eval()

							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     vqa_model.state_dict()
							for key in modified_state_dict.copy():
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     modified_state_dict.pop(_snake_case		)
														SCREAMING_SNAKE_CASE__		: Tuple     =     rename_key(_snake_case		)
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     value

							SCREAMING_SNAKE_CASE__		: List[Any]     =     BlipForQuestionAnswering(_snake_case		)

							hf_vqa_model.load_state_dict(_snake_case		)

							SCREAMING_SNAKE_CASE__		: Tuple     =     ["""How many dogs are in this image?"""]
							SCREAMING_SNAKE_CASE__		: str     =     tokenizer(_snake_case     ,return_tensors="""pt"""		).input_ids

							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     hf_vqa_model.generate(_snake_case     ,_snake_case		)
							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"""		)

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

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     blip_itm(pretrained=_snake_case     ,image_size=_snake_case     ,vit="""base"""		)
							itm_model.eval()

							SCREAMING_SNAKE_CASE__		: List[Any]     =     itm_model.state_dict()
							for key in modified_state_dict.copy():
														SCREAMING_SNAKE_CASE__		: List[Any]     =     modified_state_dict.pop(_snake_case		)
														SCREAMING_SNAKE_CASE__		: Dict     =     rename_key(_snake_case		)
														SCREAMING_SNAKE_CASE__		: Dict     =     value

							SCREAMING_SNAKE_CASE__		: str     =     BlipForImageTextRetrieval(_snake_case		)

							SCREAMING_SNAKE_CASE__		: Dict     =     ["""A picture of a woman with a dog sitting in a beach"""]
							SCREAMING_SNAKE_CASE__		: int     =     tokenizer(
							    _snake_case     ,return_tensors="""pt"""     ,padding="""max_length"""     ,truncation=_snake_case     ,max_length=35     ,).input_ids

							hf_itm_model.load_state_dict(_snake_case		)
							hf_itm_model.eval()

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     hf_itm_model(_snake_case     ,_snake_case     ,use_itm_head=_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     hf_itm_model(_snake_case     ,_snake_case     ,use_itm_head=_snake_case		)

							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__   :					Tuple												=				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__   :					str												=				parser.parse_args()

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






"""simple docstring"""




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase__   :					Optional[Any]												=				logging.get_logger(__name__)

UpperCAmelCase__   :					str												=				{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     '''detr'''
				__UpperCamelCase   :		List[Any]       =     ['''past_key_values''']
				__UpperCamelCase   :		List[Any]       =     {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}



				def __init__(self     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=1_00     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__="relu"     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=1.0     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__="sine"     , SCREAMING_SNAKE_CASE__="resnet50"     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.1     , **SCREAMING_SNAKE_CASE__     , )      ->	Optional[Any]:



											"""simple docstring"""




											if backbone_config is not None and use_timm_backbone:
																		raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""					)

											if not use_timm_backbone:
																		if backbone_config is None:
																									logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
																									SCREAMING_SNAKE_CASE__		: Any     =     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]					)
																		elif isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     backbone_config.get("""model_type"""					)
																									SCREAMING_SNAKE_CASE__		: int     =     CONFIG_MAPPING[backbone_model_type]
																									SCREAMING_SNAKE_CASE__		: str     =     config_class.from_dict(SCREAMING_SNAKE_CASE__					)
																		# set timm attributes to None
																		SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[str]     =     None, None, None

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     use_timm_backbone
											SCREAMING_SNAKE_CASE__		: Tuple     =     backbone_config
											SCREAMING_SNAKE_CASE__		: List[Any]     =     num_channels
											SCREAMING_SNAKE_CASE__		: Tuple     =     num_queries
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     d_model
											SCREAMING_SNAKE_CASE__		: str     =     encoder_ffn_dim
											SCREAMING_SNAKE_CASE__		: str     =     encoder_layers
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     encoder_attention_heads
											SCREAMING_SNAKE_CASE__		: Any     =     decoder_ffn_dim
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     decoder_layers
											SCREAMING_SNAKE_CASE__		: Dict     =     decoder_attention_heads
											SCREAMING_SNAKE_CASE__		: List[str]     =     dropout
											SCREAMING_SNAKE_CASE__		: Tuple     =     attention_dropout
											SCREAMING_SNAKE_CASE__		: Tuple     =     activation_dropout
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     activation_function
											SCREAMING_SNAKE_CASE__		: Any     =     init_std
											SCREAMING_SNAKE_CASE__		: Dict     =     init_xavier_std
											SCREAMING_SNAKE_CASE__		: Any     =     encoder_layerdrop
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     decoder_layerdrop
											SCREAMING_SNAKE_CASE__		: Dict     =     encoder_layers
											SCREAMING_SNAKE_CASE__		: List[str]     =     auxiliary_loss
											SCREAMING_SNAKE_CASE__		: List[Any]     =     position_embedding_type
											SCREAMING_SNAKE_CASE__		: List[str]     =     backbone
											SCREAMING_SNAKE_CASE__		: Dict     =     use_pretrained_backbone
											SCREAMING_SNAKE_CASE__		: Any     =     dilation
											# Hungarian matcher
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     class_cost
											SCREAMING_SNAKE_CASE__		: Tuple     =     bbox_cost
											SCREAMING_SNAKE_CASE__		: List[Any]     =     giou_cost
											# Loss coefficients
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     mask_loss_coefficient
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dice_loss_coefficient
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     bbox_loss_coefficient
											SCREAMING_SNAKE_CASE__		: Any     =     giou_loss_coefficient
											SCREAMING_SNAKE_CASE__		: List[str]     =     eos_coefficient
											super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)



				@property
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return self.encoder_attention_heads



				@property
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return self.d_model



				@classmethod
				def 			__magic_name__					(cls     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	Tuple:



											"""simple docstring"""




											return cls(backbone_config=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Dict[str, any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     copy.deepcopy(self.__dict__					)
											if output["backbone_config"] is not None:
																		SCREAMING_SNAKE_CASE__		: Any     =     self.backbone_config.to_dict()
											SCREAMING_SNAKE_CASE__		: Dict     =     self.__class__.model_type
											return output






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		Any       =     version.parse('''1.11'''						)



				@property
				def 			__magic_name__					(self					)      ->	Mapping[str, Mapping[int, str]]:



											"""simple docstring"""




											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											        ("""pixel_mask""", {0: """batch"""}),
											    ]					)



				@property
				def 			__magic_name__					(self					)      ->	float:



											"""simple docstring"""




											return 1E-5



				@property
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return 12




