


'''simple docstring'''



from __future__ import annotations

from math import gcd






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int = 2						,      _SCREAMING_SNAKE_CASE      :   int = 1						,      _SCREAMING_SNAKE_CASE      :   int = 3						,      ):
					# A value less than 2 can cause an infinite loop in the algorithm.
					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(_SCREAMING_SNAKE_CASE						,      2   ) + step) % modulus

					for _ in range(_SCREAMING_SNAKE_CASE   ):
										# These track the position within the cycle detection logic.
										__a			:       int					    =  seed
										__a			:       Tuple					    =  seed

										while True:
															# At each iteration, the tortoise moves one step and the hare moves two.
															__a			:       List[str]					    =  rand_fn(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
															__a			:       Optional[int]					    =  rand_fn(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
															__a			:       Optional[Any]					    =  rand_fn(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_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``.
															__a			:       List[str]					    =  gcd(hare - tortoise						,      _SCREAMING_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.
										__a			:       List[str]					    =  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

	__lowercase	:						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',
	)
	__lowercase	:						List[str]       =	parser.parse_args()

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







'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase	:						Tuple       =	logging.get_logger(__name__)

__lowercase	:						List[Any]       =	torch.device('cpu')






def   lowerCamelCase							():
					__a			:       int					    =  'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a			:       Tuple					    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,      stream=_SCREAMING_SNAKE_CASE   ).raw   )
					return im






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					if swiftformer_name == "swiftformer_xs":
										return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]   )

					elif swiftformer_name == "swiftformer_s":
										return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]   )

					elif swiftformer_name == "swiftformer_l1":
										return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]   )

					elif swiftformer_name == "swiftformer_l3":
										return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       int					    =  dct.pop(_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  val






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       Dict					    =  []
					for k in state_dict.keys():
										__a			:       List[Any]					    =  k
										if ".pwconv" in k:
															__a			:       List[Any]					    =  k_new.replace('.pwconv'						,      '.point_wise_conv'   )
										if ".dwconv" in k:
															__a			:       Dict					    =  k_new.replace('.dwconv'						,      '.depth_wise_conv'   )
										if ".Proj." in k:
															__a			:       Optional[int]					    =  k_new.replace('.Proj.'						,      '.proj.'   )
										if "patch_embed" in k_new:
															__a			:       List[Any]					    =  k_new.replace('patch_embed'						,      'swiftformer.patch_embed.patch_embedding'   )
										if "network" in k_new:
															__a			:       Union[str, Any]					    =  k_new.split('.'   )
															if ls[2].isdigit():
																				__a			:       Union[str, Any]					    =  'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]   )
															else:
																				__a			:       Union[str, Any]					    =  k_new.replace('network'						,      'swiftformer.encoder.network'   )
										rename_keys.append((k, k_new)   )
					return rename_keys






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       Union[str, Any]					    =  SwiftFormerConfig()

					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					__a			:       List[str]					    =  1_000
					__a			:       Tuple					    =  'huggingface/label-files'
					__a			:       str					    =  'imagenet-1k-id2label.json'
					__a			:       Dict					    =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      repo_type='dataset'   )						,      'r'   )   )
					__a			:       Optional[int]					    =  {int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
					__a			:       Any					    =  idalabel
					__a			:       str					    =  {v: k for k, v in idalabel.items()}

					# size of the architecture
					if swiftformer_name == "swiftformer_xs":
										__a			:       Dict					    =  [3, 3, 6, 4]
										__a			:       int					    =  [48, 56, 112, 220]

					elif swiftformer_name == "swiftformer_s":
										__a			:       Dict					    =  [3, 3, 9, 6]
										__a			:       List[str]					    =  [48, 64, 168, 224]

					elif swiftformer_name == "swiftformer_l1":
										__a			:       Dict					    =  [4, 3, 10, 5]
										__a			:       Optional[int]					    =  [48, 96, 192, 384]

					elif swiftformer_name == "swiftformer_l3":
										__a			:       Tuple					    =  [4, 4, 12, 6]
										__a			:       Dict					    =  [64, 128, 320, 512]

					# load state_dict of original model, remove and rename some keys
					if original_ckpt:
										if original_ckpt.startswith('https'   ):
															__a			:       List[Any]					    =  torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE						,      map_location='cpu'						,      check_hash=_SCREAMING_SNAKE_CASE   )
										else:
															__a			:       Union[str, Any]					    =  torch.load(_SCREAMING_SNAKE_CASE						,      map_location='cpu'   )
					__a			:       Optional[Any]					    =  checkpoint

					__a			:       Dict					    =  create_rename_keys(_SCREAMING_SNAKE_CASE   )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# load HuggingFace model
					__a			:       Tuple					    =  SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE   ).eval()
					hf_model.load_state_dict(_SCREAMING_SNAKE_CASE   )

					# prepare test inputs
					__a			:       Tuple					    =  prepare_img()
					__a			:       str					    =  ViTImageProcessor.from_pretrained('preprocessor_config'   )
					__a			:       Tuple					    =  processor(images=_SCREAMING_SNAKE_CASE						,      return_tensors='pt'   )

					# compare outputs from both models
					__a			:       List[Any]					    =  get_expected_output(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  hf_model(inputs['pixel_values']   ).logits

					assert hf_logits.shape == torch.Size([1, 1_000]   )
					assert torch.allclose(hf_logits[0, 0:5]						,      _SCREAMING_SNAKE_CASE						,      atol=1e-3   )

					Path(_SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE   )
					print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""   )
					hf_model.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	__lowercase	:						Optional[int]       =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--swiftformer_name',
	    default='swiftformer_xs',
	    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
	    type=str,
	    help='Name of the SwiftFormer model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='./converted_outputs/',
	    type=str,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

	__lowercase	:						Tuple       =	parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



