

from __future__ import annotations

from math import gcd







def _a (       SCREAMING_SNAKE_CASE		:			int    ,					SCREAMING_SNAKE_CASE		:			int = 2    ,					SCREAMING_SNAKE_CASE		:			int = 1    ,					SCREAMING_SNAKE_CASE		:			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(SCREAMING_SNAKE_CASE		:			int    ,					SCREAMING_SNAKE_CASE		:			int    ,					SCREAMING_SNAKE_CASE		:			int		) -> int:
  return (pow(_snake_case    ,					2		) + step) % modulus

 for _ in range(_snake_case		):
  # These track the position within the cycle detection logic.
  __lowerCAmelCase:   str   	=      seed
  __lowerCAmelCase:   int   	=      seed

  while True:
   # At each iteration, the tortoise moves one step and the hare moves two.
   __lowerCAmelCase:   List[str]   	=      rand_fn(_snake_case    ,					_snake_case    ,					_snake_case		)
   __lowerCAmelCase:   Dict   	=      rand_fn(_snake_case    ,					_snake_case    ,					_snake_case		)
   __lowerCAmelCase:   int   	=      rand_fn(_snake_case    ,					_snake_case    ,					_snake_case		)

   # 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``.
   __lowerCAmelCase:   List[str]   	=      gcd(hare - tortoise    ,					_snake_case		)

   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.
  __lowerCAmelCase:   int   	=      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

  _a		       =   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''',
  )
  _a		       =   parser.parse_args()

  _a		       =   pollard_rho(args.num, attempts=args.attempts)
  if divisor is None:
    print(f"{args.num} is probably prime")
  else:
    _a		       =   args.num // divisor
    print(f"{args.num} = {divisor} * {quotient}")







import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def   lowerCAmelCase_						(					)				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
	__magic_name__		:		Union[str, Any]	   =     Image.open(requests.get(_snake_case      ,   stream=_snake_case   ).raw   ).convert("RGB"   )

	return image






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     []
	# fmt: off

	# vision encoder
	rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")   )
	rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")   )
	rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")   )
	rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")   )
	rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")   )
	rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")   )

	for i in range(config.vision_config.num_hidden_layers   ):
		rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')   )

	# QFormer
	rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight")   )
	rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")   )

	# fmt: on
	return rename_keys






def   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Optional[Any]   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Tuple	   =     dct.pop(_snake_case   )
	__magic_name__		:		int	   =     val






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Optional[Any]   )				->		Dict:







	'''simple docstring'''


	for i in range(config.vision_config.num_hidden_layers   ):
		# read in original q and v biases
		__magic_name__		:		List[Any]	   =     state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''   )
		__magic_name__		:		Optional[Any]	   =     state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''   )

		# next, set bias in the state dict
		__magic_name__		:		Optional[int]	   =     torch.cat((q_bias, torch.zeros_like(_snake_case      ,   requires_grad=_snake_case   ), v_bias)   )
		__magic_name__		:		Union[str, Any]	   =     qkv_bias






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  str   )				->		int:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     364 if "coco" in model_name else 224
	__magic_name__		:		Union[str, Any]	   =     BlipaVisionConfig(image_size=_snake_case   ).to_dict()

	# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
	# seems like flan-T5 models don't have bos_token_id properly set?
	if "opt-2.7b" in model_name:
		__magic_name__		:		List[str]	   =     OPTConfig.from_pretrained("facebook/opt-2.7b"      ,   eos_token_id=_snake_case   ).to_dict()
	elif "opt-6.7b" in model_name:
		__magic_name__		:		Any	   =     OPTConfig.from_pretrained("facebook/opt-6.7b"      ,   eos_token_id=_snake_case   ).to_dict()
	elif "t5-xl" in model_name:
		__magic_name__		:		Dict	   =     TaConfig.from_pretrained("google/flan-t5-xl"      ,   dense_act_fn="gelu"      ,   bos_token_id=1   ).to_dict()
	elif "t5-xxl" in model_name:
		__magic_name__		:		int	   =     TaConfig.from_pretrained("google/flan-t5-xxl"      ,   dense_act_fn="gelu"      ,   bos_token_id=1   ).to_dict()

	__magic_name__		:		List[Any]	   =     BlipaConfig(vision_config=_snake_case      ,   text_config=_snake_case   )

	return config, image_size






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  str=None      ,   _snake_case      :  Dict=False   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     (
	    AutoTokenizer.from_pretrained("facebook/opt-2.7b"   )
	    if "opt" in model_name
	    else AutoTokenizer.from_pretrained("google/flan-t5-xl"   )
	)
	__magic_name__		:		List[Any]	   =     tokenizer("\n"      ,   add_special_tokens=_snake_case   ).input_ids[0]
	__magic_name__   ,    __magic_name__		:		Tuple	   =     get_blipa_config(_snake_case      ,   eos_token_id=_snake_case   )

	__magic_name__		:		Union[str, Any]	   =     BlipaForConditionalGeneration(_snake_case   ).eval()

	__magic_name__		:		Any	   =     {
	    "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
	    "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
	    "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
	    "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
	    "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
	    "blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
	    "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
	}

	__magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     model_name_to_original[model_name]

	# load original model
	print("Loading original model..."   )
	__magic_name__		:		Union[str, Any]	   =     "cuda" if torch.cuda.is_available() else "cpu"
	__magic_name__   ,    __magic_name__   ,    __magic_name__		:		Optional[Any]	   =     load_model_and_preprocess(
	    name=_snake_case      ,   model_type=_snake_case      ,   is_eval=_snake_case      ,   device=_snake_case   )
	original_model.eval()
	print("Done!"   )

	# update state dict keys
	__magic_name__		:		Dict	   =     original_model.state_dict()
	__magic_name__		:		str	   =     create_rename_keys(_snake_case   )
	for src, dest in rename_keys:
		rename_key(_snake_case      ,   _snake_case      ,   _snake_case   )

	# some keys can be renamed efficiently
	for key, val in state_dict.copy().items():
		__magic_name__		:		Any	   =     state_dict.pop(_snake_case   )
		if key.startswith("Qformer.bert"   ):
			__magic_name__		:		Optional[int]	   =     key.replace("Qformer.bert"      ,   "qformer"   )
		if "attention.self" in key:
			__magic_name__		:		Any	   =     key.replace("self"      ,   "attention"   )
		if "opt_proj" in key:
			__magic_name__		:		Union[str, Any]	   =     key.replace("opt_proj"      ,   "language_projection"   )
		if "t5_proj" in key:
			__magic_name__		:		Optional[int]	   =     key.replace("t5_proj"      ,   "language_projection"   )
		if key.startswith("opt"   ):
			__magic_name__		:		List[str]	   =     key.replace("opt"      ,   "language"   )
		if key.startswith("t5"   ):
			__magic_name__		:		Tuple	   =     key.replace("t5"      ,   "language"   )
		__magic_name__		:		Dict	   =     val

	# read in qv biases
	read_in_q_v_bias(_snake_case      ,   _snake_case   )

	__magic_name__   ,    __magic_name__		:		Tuple	   =     hf_model.load_state_dict(_snake_case      ,   strict=_snake_case   )
	assert len(_snake_case   ) == 0
	assert unexpected_keys == ["qformer.embeddings.position_ids"]

	__magic_name__		:		List[Any]	   =     load_demo_image()
	__magic_name__		:		Tuple	   =     vis_processors["eval"](_snake_case   ).unsqueeze(0   ).to(_snake_case   )
	__magic_name__		:		Dict	   =     tokenizer(["\n"]      ,   return_tensors="pt"   ).input_ids.to(_snake_case   )

	# create processor
	__magic_name__		:		Optional[Any]	   =     BlipImageProcessor(
	    size={"height": image_size, "width": image_size}      ,   image_mean=_snake_case      ,   image_std=_snake_case   )
	__magic_name__		:		Dict	   =     BlipaProcessor(image_processor=_snake_case      ,   tokenizer=_snake_case   )
	__magic_name__		:		Union[str, Any]	   =     processor(images=_snake_case      ,   return_tensors="pt"   ).pixel_values.to(_snake_case   )

	# make sure processor creates exact same pixel values
	assert torch.allclose(_snake_case      ,   _snake_case   )

	original_model.to(_snake_case   )
	hf_model.to(_snake_case   )
	with torch.no_grad():
		if "opt" in model_name:
			__magic_name__		:		List[Any]	   =     original_model({"image": original_pixel_values, "text_input": [""]}   ).logits
			__magic_name__		:		Optional[int]	   =     hf_model(_snake_case      ,   _snake_case   ).logits
		else:
			__magic_name__		:		int	   =     original_model(
			    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}   ).logits
			__magic_name__		:		Tuple	   =     input_ids.masked_fill(input_ids == tokenizer.pad_token_id      ,   -100   )
			__magic_name__		:		List[str]	   =     hf_model(_snake_case      ,   _snake_case      ,   labels=_snake_case   ).logits

	assert original_logits.shape == logits.shape
	print("First values of original logits:"      ,   original_logits[0, :3, :3]   )
	print("First values of HF logits:"      ,   logits[0, :3, :3]   )

	# assert values
	if model_name == "blip2-flan-t5-xl":
		__magic_name__		:		List[str]	   =     torch.tensor(
		    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]      ,   device=_snake_case   )
		assert torch.allclose(logits[0, :3, :3]      ,   _snake_case      ,   atol=1E-4   )
	elif model_name == "blip2-flan-t5-xl-coco":
		__magic_name__		:		Tuple	   =     torch.tensor(
		    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]      ,   device=_snake_case   )
	else:
		# cast to same type
		__magic_name__		:		str	   =     logits.dtype
		assert torch.allclose(original_logits.to(_snake_case   )      ,   _snake_case      ,   atol=1E-2   )
	print("Looks ok!"   )

	print("Generating a caption..."   )
	__magic_name__		:		Optional[int]	   =     ""
	__magic_name__		:		Dict	   =     tokenizer(_snake_case      ,   return_tensors="pt"   ).input_ids.to(_snake_case   )

	__magic_name__		:		int	   =     original_model.generate({"image": original_pixel_values}   )
	__magic_name__		:		Optional[Any]	   =     hf_model.generate(
	    _snake_case      ,   _snake_case      ,   do_sample=_snake_case      ,   num_beams=5      ,   max_length=30      ,   min_length=1      ,   top_p=0.9      ,   repetition_penalty=1.0      ,   length_penalty=1.0      ,   temperature=1      ,   )
	print("Original generation:"      ,   _snake_case   )
	__magic_name__		:		Tuple	   =     input_ids.shape[1]
	__magic_name__		:		int	   =     processor.batch_decode(outputs[:, prompt_length:]      ,   skip_special_tokens=_snake_case   )
	__magic_name__		:		Union[str, Any]	   =     [text.strip() for text in output_text]
	print("HF generation:"      ,   _snake_case   )

	if pytorch_dump_folder_path is not None:
		processor.save_pretrained(_snake_case   )
		hf_model.save_pretrained(_snake_case   )

	if push_to_hub:
		processor.push_to_hub(F'''nielsr/{model_name}'''   )
		hf_model.push_to_hub(F'''nielsr/{model_name}'''   )


if __name__ == "__main__":
				snake_case :		Any							      =  argparse.ArgumentParser()
				snake_case :		Union[str, Any]							      =  [
				    "blip2-opt-2.7b",
				    "blip2-opt-6.7b",
				    "blip2-opt-2.7b-coco",
				    "blip2-opt-6.7b-coco",
				    "blip2-flan-t5-xl",
				    "blip2-flan-t5-xl-coco",
				    "blip2-flan-t5-xxl",
				]
				parser.add_argument(
				    "--model_name",
				    default="blip2-opt-2.7b",
				    choices=choices,
				    type=str,
				    help="Path to hf config.json of model to convert",
				)
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument(
				    "--push_to_hub",
				    action="store_true",
				    help="Whether to push the model and processor to the hub after converting",
				)

				snake_case :		int							      =  parser.parse_args()

				convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
