


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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		Dict	):





   """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((0.4814_5466, 0.457_8275, 0.4082_1073)       ,  (0.2686_2954, 0.2613_0258, 0.2757_7711)	),
       ]	)
   UpperCamelCase         =					transform(SCREAMING_SNAKE_CASE_	).unsqueeze(0	).to(SCREAMING_SNAKE_CASE_	)
   return image






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict	):





   """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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Tuple=None	):





   """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() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

   UpperCamelCase         =					hf_model.generate(SCREAMING_SNAKE_CASE_	)

   assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 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() == 0.2110_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            =   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            =   parser.parse_args()

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








import math






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					[True] * n
			UpperCamelCase         =					False
			UpperCamelCase         =					False
			UpperCamelCase         =					True

			for i in range(3       ,  int(n**0.5 + 1	)       ,  2	):
						UpperCamelCase         =					i * 2
						while index < n:
									UpperCamelCase         =					False
									UpperCamelCase         =					index + i

			UpperCamelCase         =					[2]

			for i in range(3       ,  SCREAMING_SNAKE_CASE_       ,  2	):
						if is_prime[i]:
									primes.append(SCREAMING_SNAKE_CASE_	)

			return primes






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 999_966_663_333	):





			"""simple docstring"""




			UpperCamelCase         =					math.floor(math.sqrt(SCREAMING_SNAKE_CASE_	)	) + 100
			UpperCamelCase         =					prime_sieve(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					0
			UpperCamelCase         =					0
			UpperCamelCase         =					primes[prime_index]

			while (last_prime**2) <= limit:
						UpperCamelCase         =					primes[prime_index + 1]

						UpperCamelCase         =					last_prime**2
						UpperCamelCase         =					next_prime**2

						# Get numbers divisible by lps(current)
						UpperCamelCase         =					lower_bound + last_prime
						while upper_bound > current <= limit:
									matches_sum += current
									current += last_prime

						# Reset the upper_bound
						while (upper_bound - next_prime) > limit:
									upper_bound -= next_prime

						# Add the numbers divisible by ups(current)
						UpperCamelCase         =					upper_bound - next_prime
						while current > lower_bound:
									matches_sum += current
									current -= next_prime

						# Remove the numbers divisible by both ups and lps
						UpperCamelCase         =					0
						while upper_bound > current <= limit:
									if current <= lower_bound:
												# Increment the current number
												current += last_prime * next_prime
												continue

									if current > limit:
												break

									# Remove twice since it was added by both ups and lps
									matches_sum -= current * 2

									# Increment the current number
									current += last_prime * next_prime

						# Setup for next pair
						UpperCamelCase         =					next_prime
						prime_index += 1

			return matches_sum


if __name__ == "__main__":
			print(solution())




