


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

				_SCREAMING_SNAKE_CASE			  =transforms.Compose(
				    [
				        transforms.Resize((image_size, image_size)	,interpolation=InterpolationMode.BICUBIC),
				        transforms.ToTensor(),
				        transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)	,(0.2686_2954, 0.2613_0258, 0.2757_7711)),
				    ])
				_SCREAMING_SNAKE_CASE			  =transform(__lowerCAmelCase).unsqueeze(0).to(__lowerCAmelCase)
				return image







def 						lowerCamelCase(   a__):
				if "visual_encoder" in key:
								_SCREAMING_SNAKE_CASE			  =re.sub('''visual_encoder*'''	,'''vision_model.encoder'''	,__lowerCAmelCase)
				if "blocks" in key:
								_SCREAMING_SNAKE_CASE			  =re.sub(R'''blocks'''	,'''layers'''	,__lowerCAmelCase)
				if "attn" in key:
								_SCREAMING_SNAKE_CASE			  =re.sub(R'''attn'''	,'''self_attn'''	,__lowerCAmelCase)
				if "norm1" in key:
								_SCREAMING_SNAKE_CASE			  =re.sub(R'''norm1'''	,'''layer_norm1'''	,__lowerCAmelCase)
				if "norm2" in key:
								_SCREAMING_SNAKE_CASE			  =re.sub(R'''norm2'''	,'''layer_norm2'''	,__lowerCAmelCase)
				if "encoder.norm" in key:
								_SCREAMING_SNAKE_CASE			  =re.sub(R'''encoder.norm'''	,'''post_layernorm'''	,__lowerCAmelCase)
				if "encoder.patch_embed.proj" in key:
								_SCREAMING_SNAKE_CASE			  =re.sub(R'''encoder.patch_embed.proj'''	,'''embeddings.patch_embedding'''	,__lowerCAmelCase)

				if "encoder.pos_embed" in key:
								_SCREAMING_SNAKE_CASE			  =re.sub(R'''encoder.pos_embed'''	,'''embeddings.position_embedding'''	,__lowerCAmelCase)
				if "encoder.cls_token" in key:
								_SCREAMING_SNAKE_CASE			  =re.sub(R'''encoder.cls_token'''	,'''embeddings.class_embedding'''	,__lowerCAmelCase)

				if "self_attn" in key:
								_SCREAMING_SNAKE_CASE			  =re.sub(R'''self_attn.proj'''	,'''self_attn.projection'''	,__lowerCAmelCase)

				return key







@torch.no_grad()
def 						lowerCamelCase(   a__	,a__=None):
				if config_path is not None:
								_SCREAMING_SNAKE_CASE			  =BlipConfig.from_pretrained(__lowerCAmelCase)
				else:
								_SCREAMING_SNAKE_CASE			  =BlipConfig(projection_dim=512	,text_config={}	,vision_config={})

				_SCREAMING_SNAKE_CASE			  =BlipForConditionalGeneration(__lowerCAmelCase).eval()

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

				_SCREAMING_SNAKE_CASE			  =blip_decoder(pretrained=__lowerCAmelCase	,image_size=384	,vit='''base''')
				_SCREAMING_SNAKE_CASE			  =pt_model.eval()

				_SCREAMING_SNAKE_CASE			  =pt_model.state_dict()
				for key in modified_state_dict.copy():
								_SCREAMING_SNAKE_CASE			  =modified_state_dict.pop(__lowerCAmelCase)
								_SCREAMING_SNAKE_CASE			  =rename_key(__lowerCAmelCase)
								_SCREAMING_SNAKE_CASE			  =value

				hf_model.load_state_dict(__lowerCAmelCase)

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

				_SCREAMING_SNAKE_CASE			  =hf_model.generate(__lowerCAmelCase	,__lowerCAmelCase)

				assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

				_SCREAMING_SNAKE_CASE			  =hf_model.generate(__lowerCAmelCase)

				assert out[0].tolist() == [3_0522, 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'
				_SCREAMING_SNAKE_CASE			  =(
				    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
				)

				_SCREAMING_SNAKE_CASE			  =blip_vqa(pretrained=__lowerCAmelCase	,image_size=__lowerCAmelCase	,vit='''base''')
				vqa_model.eval()

				_SCREAMING_SNAKE_CASE			  =vqa_model.state_dict()
				for key in modified_state_dict.copy():
								_SCREAMING_SNAKE_CASE			  =modified_state_dict.pop(__lowerCAmelCase)
								_SCREAMING_SNAKE_CASE			  =rename_key(__lowerCAmelCase)
								_SCREAMING_SNAKE_CASE			  =value

				_SCREAMING_SNAKE_CASE			  =BlipForQuestionAnswering(__lowerCAmelCase)

				hf_vqa_model.load_state_dict(__lowerCAmelCase)

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

				_SCREAMING_SNAKE_CASE			  =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''')

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

				_SCREAMING_SNAKE_CASE			  =blip_itm(pretrained=__lowerCAmelCase	,image_size=__lowerCAmelCase	,vit='''base''')
				itm_model.eval()

				_SCREAMING_SNAKE_CASE			  =itm_model.state_dict()
				for key in modified_state_dict.copy():
								_SCREAMING_SNAKE_CASE			  =modified_state_dict.pop(__lowerCAmelCase)
								_SCREAMING_SNAKE_CASE			  =rename_key(__lowerCAmelCase)
								_SCREAMING_SNAKE_CASE			  =value

				_SCREAMING_SNAKE_CASE			  =BlipForImageTextRetrieval(__lowerCAmelCase)

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

				_SCREAMING_SNAKE_CASE			  =hf_itm_model(__lowerCAmelCase	,__lowerCAmelCase	,use_itm_head=__lowerCAmelCase)
				_SCREAMING_SNAKE_CASE			  =hf_itm_model(__lowerCAmelCase	,__lowerCAmelCase	,use_itm_head=__lowerCAmelCase)

				assert out[0].item() == 0.2110_6874_9427_7954
				assert torch.nn.functional.softmax(out_itm[0]	,dim=1)[:, 1].item() == 0.4_5698_8453_8650_5127

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


if __name__ == "__main__":
							snake_case_						:			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''')
							snake_case_						:			int													=							parser.parse_args()

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



import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LxmertTokenizer
						UpperCAmelCase             =						LxmertTokenizerFast
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Tuple:


										"""simple docstring"""





										super().setUp()

										_SCREAMING_SNAKE_CASE			  =[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''  ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Tuple  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''UNwant\u00E9d,running'''
										_SCREAMING_SNAKE_CASE			  ='''unwanted, running'''
										return input_text, output_text




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class(self.vocab_file  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize('''UNwant\u00E9d,running'''  )
										self.assertListEqual(_a				,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )				,    [7, 4, 5, 10, 8, 9]  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										if not self.test_rust_tokenizer:
														return

										_SCREAMING_SNAKE_CASE			  =self.get_tokenizer()
										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()

										_SCREAMING_SNAKE_CASE			  ='''I was born in 92000, and this is falsé.'''

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.tokenize(_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a				,    add_special_tokens=_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a				,    add_special_tokens=_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()
										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a  )
										self.assertListEqual(_a				,    _a  )