





'''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  a__    (    lowercase :					Optional[Any], lowercase :					List[str]				)		->		List[Any]:





					"""simple docstring"""

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

					_UpperCamelCase       =	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)				),
					    ]				)
					_UpperCamelCase       =	transform(__SCREAMING_SNAKE_CASE				).unsqueeze(0				).to(__SCREAMING_SNAKE_CASE				)
					return image







def  a__    (    lowercase :					Optional[Any]				)		->		List[str]:





					"""simple docstring"""

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

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

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

					return key







@torch.no_grad()
def  a__    (    lowercase :					Any, lowercase :					Optional[Any]=None				)		->		Optional[int]:





					"""simple docstring"""

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

					_UpperCamelCase       =	BlipForConditionalGeneration(__SCREAMING_SNAKE_CASE				).eval()

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

					_UpperCamelCase       =	blip_decoder(pretrained=__SCREAMING_SNAKE_CASE, image_size=384, vit='''base'''				)
					_UpperCamelCase       =	pt_model.eval()

					_UpperCamelCase       =	pt_model.state_dict()
					for key in modified_state_dict.copy():
										_UpperCamelCase       =	modified_state_dict.pop(__SCREAMING_SNAKE_CASE				)
										_UpperCamelCase       =	rename_key(__SCREAMING_SNAKE_CASE				)
										_UpperCamelCase       =	value

					hf_model.load_state_dict(__SCREAMING_SNAKE_CASE				)

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

					_UpperCamelCase       =	hf_model.generate(__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE				)

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

					_UpperCamelCase       =	hf_model.generate(__SCREAMING_SNAKE_CASE				)

					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(__SCREAMING_SNAKE_CASE				)

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

					_UpperCamelCase       =	blip_vqa(pretrained=__SCREAMING_SNAKE_CASE, image_size=__SCREAMING_SNAKE_CASE, vit='''base'''				)
					vqa_model.eval()

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

					_UpperCamelCase       =	BlipForQuestionAnswering(__SCREAMING_SNAKE_CASE				)

					hf_vqa_model.load_state_dict(__SCREAMING_SNAKE_CASE				)

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

					_UpperCamelCase       =	hf_vqa_model.generate(__SCREAMING_SNAKE_CASE, __SCREAMING_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'''				)

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

					_UpperCamelCase       =	blip_itm(pretrained=__SCREAMING_SNAKE_CASE, image_size=__SCREAMING_SNAKE_CASE, vit='''base'''				)
					itm_model.eval()

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

					_UpperCamelCase       =	BlipForImageTextRetrieval(__SCREAMING_SNAKE_CASE				)

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

					hf_itm_model.load_state_dict(__SCREAMING_SNAKE_CASE				)
					hf_itm_model.eval()

					_UpperCamelCase       =	hf_itm_model(__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, use_itm_head=__SCREAMING_SNAKE_CASE				)
					_UpperCamelCase       =	hf_itm_model(__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, use_itm_head=__SCREAMING_SNAKE_CASE				)

					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__":
			lowercase__						:      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')
			lowercase__						:      List[str]	=    parser.parse_args()

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










'''simple docstring'''

import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Tuple					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
                '''unet/diffusion_pytorch_model.bin''',
                # Removed: 'unet/diffusion_pytorch_model.safetensors',
            ]
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		List[Any]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Dict					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                # Removed: 'text_encoder/model.safetensors',
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Any					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Tuple					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Tuple					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[int]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)







       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                # 'text_encoder/model.fp16.safetensors',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)



