



'''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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Optional[Any]:
		_a     :      List[str]        =							'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
		_a     :      Optional[Any]        =							Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_  ).raw  ).convert('RGB'  )

		_a     :      Optional[Any]        =							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)  ),
		    ]  )
		_a     :      List[str]        =							transform(lowerCAmelCase_  ).unsqueeze(0  ).to(lowerCAmelCase_  )
		return image



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      int:
		if "visual_encoder" in key:
				_a     :      List[str]        =							re.sub('visual_encoder*'						,			'vision_model.encoder'						,			lowerCAmelCase_  )
		if "blocks" in key:
				_a     :      int        =							re.sub(r'blocks'						,			'layers'						,			lowerCAmelCase_  )
		if "attn" in key:
				_a     :      Optional[int]        =							re.sub(r'attn'						,			'self_attn'						,			lowerCAmelCase_  )
		if "norm1" in key:
				_a     :      List[str]        =							re.sub(r'norm1'						,			'layer_norm1'						,			lowerCAmelCase_  )
		if "norm2" in key:
				_a     :      str        =							re.sub(r'norm2'						,			'layer_norm2'						,			lowerCAmelCase_  )
		if "encoder.norm" in key:
				_a     :      int        =							re.sub(r'encoder.norm'						,			'post_layernorm'						,			lowerCAmelCase_  )
		if "encoder.patch_embed.proj" in key:
				_a     :      Union[str, Any]        =							re.sub(r'encoder.patch_embed.proj'						,			'embeddings.patch_embedding'						,			lowerCAmelCase_  )

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

		if "self_attn" in key:
				_a     :      Any        =							re.sub(r'self_attn.proj'						,			'self_attn.projection'						,			lowerCAmelCase_  )

		return key



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=None  )			->      int:
		if config_path is not None:
				_a     :      Tuple        =							BlipConfig.from_pretrained(lowerCAmelCase_  )
		else:
				_a     :      int        =							BlipConfig(projection_dim=512						,			text_config={}						,			vision_config={}  )

		_a     :      Dict        =							BlipForConditionalGeneration(lowerCAmelCase_  ).eval()

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

		_a     :      List[str]        =							blip_decoder(pretrained=lowerCAmelCase_						,			image_size=384						,			vit='base'  )
		_a     :      int        =							pt_model.eval()

		_a     :      Any        =							pt_model.state_dict()
		for key in modified_state_dict.copy():
				_a     :      List[str]        =							modified_state_dict.pop(lowerCAmelCase_  )
				_a     :      Optional[Any]        =							rename_key(lowerCAmelCase_  )
				_a     :      Any        =							value

		hf_model.load_state_dict(lowerCAmelCase_  )

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

		_a     :      List[str]        =							hf_model.generate(lowerCAmelCase_						,			lowerCAmelCase_  )

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

		_a     :      List[str]        =							hf_model.generate(lowerCAmelCase_  )

		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(lowerCAmelCase_  )

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

		_a     :      Optional[Any]        =							blip_vqa(pretrained=lowerCAmelCase_						,			image_size=lowerCAmelCase_						,			vit='base'  )
		vqa_model.eval()

		_a     :      Optional[int]        =							vqa_model.state_dict()
		for key in modified_state_dict.copy():
				_a     :      int        =							modified_state_dict.pop(lowerCAmelCase_  )
				_a     :      Optional[int]        =							rename_key(lowerCAmelCase_  )
				_a     :      Dict        =							value

		_a     :      Optional[int]        =							BlipForQuestionAnswering(lowerCAmelCase_  )

		hf_vqa_model.load_state_dict(lowerCAmelCase_  )

		_a     :      List[str]        =							['How many dogs are in this image?']
		_a     :      str        =							tokenizer(lowerCAmelCase_						,			return_tensors='pt'  ).input_ids

		_a     :      Dict        =							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'  )

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

		_a     :      List[str]        =							blip_itm(pretrained=lowerCAmelCase_						,			image_size=lowerCAmelCase_						,			vit='base'  )
		itm_model.eval()

		_a     :      Any        =							itm_model.state_dict()
		for key in modified_state_dict.copy():
				_a     :      Optional[int]        =							modified_state_dict.pop(lowerCAmelCase_  )
				_a     :      Any        =							rename_key(lowerCAmelCase_  )
				_a     :      Dict        =							value

		_a     :      Tuple        =							BlipForImageTextRetrieval(lowerCAmelCase_  )

		_a     :      Any        =							['A picture of a woman with a dog sitting in a beach']
		_a     :      List[str]        =							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()

		_a     :      str        =							hf_itm_model(lowerCAmelCase_						,			lowerCAmelCase_						,			use_itm_head=lowerCAmelCase_  )
		_a     :      Any        =							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__":
							__lowerCAmelCase									=   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''')
							__lowerCAmelCase									=   parser.parse_args()

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






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class      A__							:






				@property
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							return self.get_dummy_input()






				@property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							if self.block_type == "down":
										return (4, 32, 16, 16)
							elif self.block_type == "mid":
										return (4, 32, 32, 32)
							elif self.block_type == "up":
										return (4, 32, 64, 64)

							raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''   )






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[Any]=True    ,     a					:      Any=False    ,     a					:      Dict=False    ,     a					:      Union[str, Any]=False    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		4
							lowerCAmelCase__      :      int         =		32
							lowerCAmelCase__      :      Tuple         =		(32, 32)

							lowerCAmelCase__      :      List[Any]         =		torch.manual_seed(0   )
							lowerCAmelCase__      :      List[Any]         =		torch.device(a   )
							lowerCAmelCase__      :      str         =		(batch_size, num_channels) + sizes
							lowerCAmelCase__      :      Tuple         =		randn_tensor(a    ,     generator=a    ,     device=a   )
							lowerCAmelCase__      :      Optional[Any]         =		{'hidden_states': hidden_states}

							if include_temb:
										lowerCAmelCase__      :      int         =		128
										lowerCAmelCase__      :      List[str]         =		randn_tensor((batch_size, temb_channels)    ,     generator=a    ,     device=a   )

							if include_res_hidden_states_tuple:
										lowerCAmelCase__      :      int         =		torch.manual_seed(1   )
										lowerCAmelCase__      :      str         =		(randn_tensor(a    ,     generator=a    ,     device=a   ),)

							if include_encoder_hidden_states:
										lowerCAmelCase__      :      Any         =		floats_tensor((batch_size, 32, 32)   ).to(a   )

							if include_skip_sample:
										lowerCAmelCase__      :      Union[str, Any]         =		randn_tensor(((batch_size, 3) + sizes)    ,     generator=a    ,     device=a   )

							return dummy_input






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		{
							    'in_channels': 32,
							    'out_channels': 32,
							    'temb_channels': 128,
							}
							if self.block_type == "up":
										lowerCAmelCase__      :      Union[str, Any]         =		32

							if self.block_type == "mid":
										init_dict.pop('out_channels'   )

							lowerCAmelCase__      :      Optional[int]         =		self.dummy_input
							return init_dict, inputs_dict






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		self.prepare_init_args_and_inputs_for_common()
							lowerCAmelCase__      :      int         =		self.block_class(**a   )
							unet_block.to(a   )
							unet_block.eval()

							with torch.no_grad():
										lowerCAmelCase__      :      int         =		unet_block(**a   )

							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      List[str]         =		output[0]

							self.assertEqual(output.shape    ,     self.output_shape   )

							lowerCAmelCase__      :      List[str]         =		output[0, -1, -3:, -3:]
							lowerCAmelCase__      :      Any         =		torch.tensor(a   ).to(a   )
							assert torch_all_close(output_slice.flatten()    ,     a    ,     atol=5E-3   )






				@unittest.skipIf(torch_device == 'mps'    ,     'Training is not supported in mps'   )
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		self.prepare_init_args_and_inputs_for_common()
							lowerCAmelCase__      :      Any         =		self.block_class(**a   )
							model.to(a   )
							model.train()
							lowerCAmelCase__      :      int         =		model(**a   )

							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      Dict         =		output[0]

							lowerCAmelCase__      :      Optional[int]         =		torch.device(a   )
							lowerCAmelCase__      :      List[Any]         =		randn_tensor(output.shape    ,     device=a   )
							lowerCAmelCase__      :      List[Any]         =		torch.nn.functional.mse_loss(a    ,     a   )
							loss.backward()