
'''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_		(				__SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
						lowercase_	:     Union[str, Any]													=       Image.open(requests.get(UpperCAmelCase_      ,      stream=UpperCAmelCase_							).raw							).convert('''RGB'''							)

						lowercase_	:     int													=       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_	:     int													=       transform(UpperCAmelCase_							).unsqueeze(0							).to(UpperCAmelCase_							)
						return image
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





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

						if "encoder.pos_embed" in key:
												lowercase_	:     Dict													=       re.sub(R'''encoder.pos_embed'''      ,      '''embeddings.position_embedding'''      ,      UpperCAmelCase_							)
						if "encoder.cls_token" in key:
												lowercase_	:     List[str]													=       re.sub(R'''encoder.cls_token'''      ,      '''embeddings.class_embedding'''      ,      UpperCAmelCase_							)

						if "self_attn" in key:
												lowercase_	:     Union[str, Any]													=       re.sub(R'''self_attn.proj'''      ,      '''self_attn.projection'''      ,      UpperCAmelCase_							)

						return key
@torch.no_grad()
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: List[Any]=None							):



						"""simple docstring"""





						if config_path is not None:
												lowercase_	:     List[str]													=       BlipConfig.from_pretrained(UpperCAmelCase_							)
						else:
												lowercase_	:     List[str]													=       BlipConfig(projection_dim=512      ,      text_config={}      ,      vision_config={}							)

						lowercase_	:     Optional[int]													=       BlipForConditionalGeneration(UpperCAmelCase_							).eval()

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

						lowercase_	:     Union[str, Any]													=       blip_decoder(pretrained=UpperCAmelCase_      ,      image_size=384      ,      vit='''base'''							)
						lowercase_	:     Union[str, Any]													=       pt_model.eval()

						lowercase_	:     Tuple													=       pt_model.state_dict()
						for key in modified_state_dict.copy():
												lowercase_	:     List[str]													=       modified_state_dict.pop(UpperCAmelCase_							)
												lowercase_	:     Optional[int]													=       rename_key(UpperCAmelCase_							)
												lowercase_	:     int													=       value

						hf_model.load_state_dict(UpperCAmelCase_							)

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

						lowercase_	:     Optional[Any]													=       hf_model.generate(UpperCAmelCase_      ,      UpperCAmelCase_							)

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

						lowercase_	:     List[str]													=       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'
						lowercase_	:     Optional[int]													=       (
						    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
						)

						lowercase_	:     Dict													=       blip_vqa(pretrained=UpperCAmelCase_      ,      image_size=UpperCAmelCase_      ,      vit='''base'''							)
						vqa_model.eval()

						lowercase_	:     int													=       vqa_model.state_dict()
						for key in modified_state_dict.copy():
												lowercase_	:     List[Any]													=       modified_state_dict.pop(UpperCAmelCase_							)
												lowercase_	:     Optional[Any]													=       rename_key(UpperCAmelCase_							)
												lowercase_	:     Any													=       value

						lowercase_	:     Tuple													=       BlipForQuestionAnswering(UpperCAmelCase_							)

						hf_vqa_model.load_state_dict(UpperCAmelCase_							)

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

						lowercase_	:     str													=       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'''							)

						lowercase_	:     Optional[Any]													=       'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

						lowercase_	:     Optional[Any]													=       blip_itm(pretrained=UpperCAmelCase_      ,      image_size=UpperCAmelCase_      ,      vit='''base'''							)
						itm_model.eval()

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

						lowercase_	:     int													=       BlipForImageTextRetrieval(UpperCAmelCase_							)

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

						lowercase_	:     Tuple													=       hf_itm_model(UpperCAmelCase_      ,      UpperCAmelCase_      ,      use_itm_head=UpperCAmelCase_							)
						lowercase_	:     int													=       hf_itm_model(UpperCAmelCase_      ,      UpperCAmelCase_      ,      use_itm_head=UpperCAmelCase_							)

						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__":
							_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 :  int    							=       parser.parse_args()

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







'''simple docstring'''



import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
      import torch

      from transformers import AutoModelForImageClassification

if is_vision_available():
      from transformers import AutoImageProcessor





@require_torch
@require_vision
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    @slow
    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Any								=		AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         __lowerCamelCase   :	Tuple								=		AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         model.to(SCREAMING_SNAKE_CASE_	)

         from datasets import load_dataset

         __lowerCamelCase   :	str								=		load_dataset('nielsr/rvlcdip-demo'	)

         __lowerCamelCase   :	List[Any]								=		dataset['train'][0]['image'].convert('RGB'	)

         __lowerCamelCase   :	str								=		image_processor(SCREAMING_SNAKE_CASE_			,     return_tensors='pt'	).to(SCREAMING_SNAKE_CASE_	)

         # forward pass
         with torch.no_grad():
              __lowerCamelCase   :	str								=		model(**SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	int								=		outputs.logits

         __lowerCamelCase   :	List[Any]								=		torch.Size((1, 16)	)
         self.assertEqual(logits.shape			,     SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		torch.tensor(
             [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]			,     device=SCREAMING_SNAKE_CASE_			,     dtype=torch.float			,     )
         self.assertTrue(torch.allclose(logits[0, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-4	)	)






