


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_				:List[str]		,    lowerCAmelCase_				:int				)->Any:
   '''simple docstring'''






   snake_case_            =       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
   snake_case_            =       Image.open(requests.get(lowerCAmelCase_		,    stream=lowerCAmelCase_				).raw				).convert("RGB"				)

   snake_case_            =       transforms.Compose(
       [
           transforms.Resize((image_size, image_size)		,    interpolation=InterpolationMode.BICUBIC				),
           transforms.ToTensor(),
           transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)		,    (0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)				),
       ]				)
   snake_case_            =       transform(lowerCAmelCase_				).unsqueeze(0				).to(lowerCAmelCase_				)
   return image



def       _lowerCAmelCase    (				lowerCAmelCase_				:int				)->Dict:
   '''simple docstring'''






   if "visual_encoder" in key:
      snake_case_            =       re.sub("visual_encoder*"		,    "vision_model.encoder"		,    lowerCAmelCase_				)
   if "blocks" in key:
      snake_case_            =       re.sub(r"blocks"		,    "layers"		,    lowerCAmelCase_				)
   if "attn" in key:
      snake_case_            =       re.sub(r"attn"		,    "self_attn"		,    lowerCAmelCase_				)
   if "norm1" in key:
      snake_case_            =       re.sub(r"norm1"		,    "layer_norm1"		,    lowerCAmelCase_				)
   if "norm2" in key:
      snake_case_            =       re.sub(r"norm2"		,    "layer_norm2"		,    lowerCAmelCase_				)
   if "encoder.norm" in key:
      snake_case_            =       re.sub(r"encoder.norm"		,    "post_layernorm"		,    lowerCAmelCase_				)
   if "encoder.patch_embed.proj" in key:
      snake_case_            =       re.sub(r"encoder.patch_embed.proj"		,    "embeddings.patch_embedding"		,    lowerCAmelCase_				)

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

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

   return key



@torch.no_grad()
def       _lowerCAmelCase    (				lowerCAmelCase_				:Any		,    lowerCAmelCase_				:Optional[int]=None				)->Optional[Any]:
   '''simple docstring'''






   if config_path is not None:
      snake_case_            =       BlipConfig.from_pretrained(lowerCAmelCase_				)
   else:
      snake_case_            =       BlipConfig(projection_dim=512		,    text_config={}		,    vision_config={}				)

   snake_case_            =       BlipForConditionalGeneration(lowerCAmelCase_				).eval()

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

   snake_case_            =       blip_decoder(pretrained=lowerCAmelCase_		,    image_size=384		,    vit="base"				)
   snake_case_            =       pt_model.eval()

   snake_case_            =       pt_model.state_dict()
   for key in modified_state_dict.copy():
      snake_case_            =       modified_state_dict.pop(lowerCAmelCase_				)
      snake_case_            =       rename_key(lowerCAmelCase_				)
      snake_case_            =       value

   hf_model.load_state_dict(lowerCAmelCase_				)

   snake_case_            =       384
   snake_case_            =       load_demo_image(image_size=lowerCAmelCase_		,    device="cpu"				)
   snake_case_            =       BertTokenizer.from_pretrained("bert-base-uncased"				)
   snake_case_            =       tokenizer(["a picture of"]				).input_ids

   snake_case_            =       hf_model.generate(lowerCAmelCase_		,    lowerCAmelCase_				)

   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]

   snake_case_            =       hf_model.generate(lowerCAmelCase_				)

   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(lowerCAmelCase_				)

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

   snake_case_            =       blip_vqa(pretrained=lowerCAmelCase_		,    image_size=lowerCAmelCase_		,    vit="base"				)
   vqa_model.eval()

   snake_case_            =       vqa_model.state_dict()
   for key in modified_state_dict.copy():
      snake_case_            =       modified_state_dict.pop(lowerCAmelCase_				)
      snake_case_            =       rename_key(lowerCAmelCase_				)
      snake_case_            =       value

   snake_case_            =       BlipForQuestionAnswering(lowerCAmelCase_				)

   hf_vqa_model.load_state_dict(lowerCAmelCase_				)

   snake_case_            =       ["How many dogs are in this image?"]
   snake_case_            =       tokenizer(lowerCAmelCase_		,    return_tensors="pt"				).input_ids

   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"				)

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

   snake_case_            =       blip_itm(pretrained=lowerCAmelCase_		,    image_size=lowerCAmelCase_		,    vit="base"				)
   itm_model.eval()

   snake_case_            =       itm_model.state_dict()
   for key in modified_state_dict.copy():
      snake_case_            =       modified_state_dict.pop(lowerCAmelCase_				)
      snake_case_            =       rename_key(lowerCAmelCase_				)
      snake_case_            =       value

   snake_case_            =       BlipForImageTextRetrieval(lowerCAmelCase_				)

   snake_case_            =       ["A picture of a woman with a dog sitting in a beach"]
   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()

   snake_case_            =       hf_itm_model(lowerCAmelCase_		,    lowerCAmelCase_		,    use_itm_head=lowerCAmelCase_				)
   snake_case_            =       hf_itm_model(lowerCAmelCase_		,    lowerCAmelCase_		,    use_itm_head=lowerCAmelCase_				)

   assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
   assert torch.nn.functional.softmax(out_itm[0]		,    dim=1				)[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						: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''')
   SCREAMING_SNAKE_CASE						:Tuple			=  parser.parse_args()

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








from __future__ import annotations

from math import gcd



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int = 2		,    lowerCAmelCase_				:int = 1		,    lowerCAmelCase_				:int = 3		,    )->int | None:
   '''simple docstring'''






   if num < 2:
      raise ValueError("The input value cannot be less than 2"				)

   # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
   # algorithm struggles to find factors that are divisible by two.
   # As a workaround, we specifically check for two and even inputs.
   #   See: https://math.stackexchange.com/a/2856214/165820
   if num > 2 and num % 2 == 0:
      return 2

   # Pollard's Rho algorithm requires a function that returns pseudorandom
   # values between 0 <= X < ``num``.  It doesn't need to be random in the
   # sense that the output value is cryptographically secure or difficult
   # to calculate, it only needs to be random in the sense that all output
   # values should be equally likely to appear.
   # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
   # However, the success of Pollard's algorithm isn't guaranteed and is
   # determined in part by the initial seed and the chosen random function.
   # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
   # where ``C`` is a value that we can modify between each attempt.
   def rand_fn(lowerCAmelCase_				:int		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				) -> int:
      return (pow(lowerCAmelCase_		,    2				) + step) % modulus

   for _ in range(lowerCAmelCase_				):
      # These track the position within the cycle detection logic.
      snake_case_            =       seed
      snake_case_            =       seed

      while True:
         # At each iteration, the tortoise moves one step and the hare moves two.
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)

         # At some point both the tortoise and the hare will enter a cycle whose
         # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
         # the tortoise and hare will end up on the same value modulo ``p``.
         # We can detect when this happens because the position difference between
         # the tortoise and the hare will share a common divisor with ``num``.
         snake_case_            =       gcd(hare - tortoise		,    lowerCAmelCase_				)

         if divisor == 1:
            # No common divisor yet, just keep searching.
            continue
         else:
            # We found a common divisor!
            if divisor == num:
               # Unfortunately, the divisor is ``num`` itself and is useless.
               break
            else:
               # The divisor is a nontrivial factor of ``num``!
               return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
      snake_case_            =       hare

      # The new step value for the random function can just be incremented.
      # At first the results will be similar to what the old function would
      # have produced, but the value will quickly diverge after a bit.
      step += 1

   # We haven't found a divisor within the requested number of attempts.
   # We were unlucky or ``num`` itself is actually prime.
   return None


if __name__ == "__main__":
   import argparse

   SCREAMING_SNAKE_CASE						:Union[str, Any]			=  argparse.ArgumentParser()
   parser.add_argument(
       '''num''',
       type=int,
       help='''The value to find a divisor of''',
   )
   parser.add_argument(
       '''--attempts''',
       type=int,
       default=3,
       help='''The number of attempts before giving up''',
   )
   SCREAMING_SNAKE_CASE						:List[str]			=  parser.parse_args()

   SCREAMING_SNAKE_CASE						:List[Any]			=  pollard_rho(args.num, attempts=args.attempts)
   if divisor is None:
      print(F'''{args.num} is probably prime''')
   else:
      SCREAMING_SNAKE_CASE						:List[str]			=  args.num // divisor
      print(F'''{args.num} = {divisor} * {quotient}''')




