



'''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 os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property





@unittest.skipUnless(os.path.exists(UpperCAmelCase_     )		,				'Tatoeba directory does not exist.'     )
class      A							(				unittest.TestCase     ):


   @cached_property
   def 			lowercase_      (self     :						Optional[int]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  tempfile.mkdtemp()
         return TatoebaConverter(save_dir=__UpperCAmelCase		)


   @slow
   def 			lowercase_      (self     :						List[Any]		)						->				Optional[int]:
         """simple docstring"""



         self.resolver.convert_models(["heb-eng"]		)





   @slow
   def 			lowercase_      (self     :						Dict		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__		,      UpperCAmelCase__								=  self.resolver.write_model_card("opus-mt-he-en"       ,  dry_run=__UpperCAmelCase		)
         assert mmeta["long_pair"] == "heb-eng"



