



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 ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_       :str				  =				logging.get_logger(__name__)
def 	lowercase_							(A			:			Any				):
       snake_case__							:     Union[str, Any]            =     ConvNextConfig()

       if "tiny" in checkpoint_url:
              snake_case__							:     Optional[Any]            =     [3, 3, 9, 3]
              snake_case__							:     Dict            =     [9_6, 1_9_2, 3_8_4, 7_6_8]
       if "small" in checkpoint_url:
              snake_case__							:     Optional[Any]            =     [3, 3, 2_7, 3]
              snake_case__							:     Optional[int]            =     [9_6, 1_9_2, 3_8_4, 7_6_8]
       if "base" in checkpoint_url:
              snake_case__							:     Union[str, Any]            =     [3, 3, 2_7, 3]
              snake_case__							:     List[str]            =     [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
       if "large" in checkpoint_url:
              snake_case__							:     Optional[Any]            =     [3, 3, 2_7, 3]
              snake_case__							:     Any            =     [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
       if "xlarge" in checkpoint_url:
              snake_case__							:     Any            =     [3, 3, 2_7, 3]
              snake_case__							:     str            =     [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

       if "1k" in checkpoint_url:
              snake_case__							:     Any            =     1_0_0_0
              snake_case__							:     Optional[Any]            =     'imagenet-1k-id2label.json'
              snake_case__							:     Optional[int]            =     (1, 1_0_0_0)
       else:
              snake_case__							:     Optional[int]            =     2_1_8_4_1
              snake_case__							:     Optional[Any]            =     'imagenet-22k-id2label.json'
              snake_case__							:     Union[str, Any]            =     (1, 2_1_8_4_1)

       snake_case__							:     int            =     'huggingface/label-files'
       snake_case__							:     List[Any]            =     num_labels
       snake_case__							:     Dict            =     json.load(open(hf_hub_download(A		, A		, repo_type='dataset'				)		, 'r'				)				)
       snake_case__							:     Any            =     {int(A				): v for k, v in idalabel.items()}
       if "1k" not in checkpoint_url:
              # this dataset contains 21843 labels but the model only has 21841
              # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
              del idalabel[9_2_0_5]
              del idalabel[1_5_0_2_7]
       snake_case__							:     Union[str, Any]            =     idalabel
       snake_case__							:     Union[str, Any]            =     {v: k for k, v in idalabel.items()}
       snake_case__							:     Tuple            =     hidden_sizes
       snake_case__							:     Dict            =     depths

       return config, expected_shape
def 	lowercase_							(A			:			List[str]				):
       if "downsample_layers.0.0" in name:
              snake_case__							:     List[Any]            =     name.replace('downsample_layers.0.0'		, 'embeddings.patch_embeddings'				)
       if "downsample_layers.0.1" in name:
              snake_case__							:     List[str]            =     name.replace('downsample_layers.0.1'		, 'embeddings.norm'				)  # we rename to layernorm later on
       if "downsample_layers.1.0" in name:
              snake_case__							:     Tuple            =     name.replace('downsample_layers.1.0'		, 'stages.1.downsampling_layer.0'				)
       if "downsample_layers.1.1" in name:
              snake_case__							:     Dict            =     name.replace('downsample_layers.1.1'		, 'stages.1.downsampling_layer.1'				)
       if "downsample_layers.2.0" in name:
              snake_case__							:     List[str]            =     name.replace('downsample_layers.2.0'		, 'stages.2.downsampling_layer.0'				)
       if "downsample_layers.2.1" in name:
              snake_case__							:     Union[str, Any]            =     name.replace('downsample_layers.2.1'		, 'stages.2.downsampling_layer.1'				)
       if "downsample_layers.3.0" in name:
              snake_case__							:     Tuple            =     name.replace('downsample_layers.3.0'		, 'stages.3.downsampling_layer.0'				)
       if "downsample_layers.3.1" in name:
              snake_case__							:     Dict            =     name.replace('downsample_layers.3.1'		, 'stages.3.downsampling_layer.1'				)
       if "stages" in name and "downsampling_layer" not in name:
              # stages.0.0. for instance should be renamed to stages.0.layers.0.
              snake_case__							:     List[Any]            =     name[: len('stages.0'				)] + '.layers' + name[len('stages.0'				) :]
       if "stages" in name:
              snake_case__							:     Optional[int]            =     name.replace('stages'		, 'encoder.stages'				)
       if "norm" in name:
              snake_case__							:     Union[str, Any]            =     name.replace('norm'		, 'layernorm'				)
       if "gamma" in name:
              snake_case__							:     List[Any]            =     name.replace('gamma'		, 'layer_scale_parameter'				)
       if "head" in name:
              snake_case__							:     Optional[int]            =     name.replace('head'		, 'classifier'				)

       return name
def 	lowercase_							():
       snake_case__							:     Union[str, Any]            =     'http://images.cocodataset.org/val2017/000000039769.jpg'
       snake_case__							:     Union[str, Any]            =     Image.open(requests.get(A		, stream=A				).raw				)
       return im
@torch.no_grad()
def 	lowercase_							(A			:			Optional[int]		, A			:			int				):
       snake_case__     ,    snake_case__							:     Tuple            =     get_convnext_config(A				)
       # load original state_dict from URL
       snake_case__							:     Union[str, Any]            =     torch.hub.load_state_dict_from_url(A				)['model']
       # rename keys
       for key in state_dict.copy().keys():
              snake_case__							:     Tuple            =     state_dict.pop(A				)
              snake_case__							:     Dict            =     val
       # add prefix to all keys expect classifier head
       for key in state_dict.copy().keys():
              snake_case__							:     List[Any]            =     state_dict.pop(A				)
              if not key.startswith('classifier'				):
                     snake_case__							:     List[Any]            =     'convnext.' + key
              snake_case__							:     List[str]            =     val

       # load HuggingFace model
       snake_case__							:     Union[str, Any]            =     ConvNextForImageClassification(A				)
       model.load_state_dict(A				)
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       snake_case__							:     str            =     2_2_4 if '224' in checkpoint_url else 3_8_4
       snake_case__							:     Union[str, Any]            =     ConvNextImageProcessor(size=A				)
       snake_case__							:     Union[str, Any]            =     image_processor(images=prepare_img()		, return_tensors='pt'				).pixel_values

       snake_case__							:     List[str]            =     model(A				).logits

       # note: the logits below were obtained without center cropping
       if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
              snake_case__							:     int            =     torch.tensor([-0.1210, -0.6605, 0.1918]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
              snake_case__							:     List[Any]            =     torch.tensor([-0.4473, -0.1847, -0.6365]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
              snake_case__							:     List[Any]            =     torch.tensor([0.4525, 0.7539, 0.0308]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
              snake_case__							:     str            =     torch.tensor([0.3561, 0.6350, -0.0384]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
              snake_case__							:     Optional[int]            =     torch.tensor([0.4174, -0.0989, 0.1489]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
              snake_case__							:     Any            =     torch.tensor([0.2513, -0.1349, -0.1613]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
              snake_case__							:     int            =     torch.tensor([1.2980, 0.3631, -0.1198]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
              snake_case__							:     Dict            =     torch.tensor([1.2963, 0.1227, 0.1723]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
              snake_case__							:     List[str]            =     torch.tensor([1.7956, 0.8390, 0.2820]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
              snake_case__							:     Any            =     torch.tensor([-0.2822, -0.0502, -0.0878]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
              snake_case__							:     str            =     torch.tensor([-0.5672, -0.0730, -0.4348]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
              snake_case__							:     Optional[Any]            =     torch.tensor([0.2681, 0.2365, 0.6246]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
              snake_case__							:     Optional[Any]            =     torch.tensor([-0.2642, 0.3931, 0.5116]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
              snake_case__							:     List[str]            =     torch.tensor([-0.6677, -0.1873, -0.8379]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
              snake_case__							:     int            =     torch.tensor([-0.7749, -0.2967, -0.6444]				)
       else:
              raise ValueError(F'''Unknown URL: {checkpoint_url}'''				)

       assert torch.allclose(logits[0, :3]		, A		, atol=1e-3				)
       assert logits.shape == expected_shape

       Path(A				).mkdir(exist_ok=A				)
       print(F'''Saving model to {pytorch_dump_folder_path}'''				)
       model.save_pretrained(A				)
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
       image_processor.save_pretrained(A				)

       print('Pushing model to the hub...'				)
       snake_case__							:     List[Any]            =     'convnext'
       if "tiny" in checkpoint_url:
              model_name += "-tiny"
       elif "small" in checkpoint_url:
              model_name += "-small"
       elif "base" in checkpoint_url:
              model_name += "-base"
       elif "xlarge" in checkpoint_url:
              model_name += "-xlarge"
       elif "large" in checkpoint_url:
              model_name += "-large"
       if "224" in checkpoint_url:
              model_name += "-224"
       elif "384" in checkpoint_url:
              model_name += "-384"
       if "22k" in checkpoint_url and "1k" not in checkpoint_url:
              model_name += "-22k"
       if "22k" in checkpoint_url and "1k" in checkpoint_url:
              model_name += "-22k-1k"

       model.push_to_hub(
           repo_path_or_name=Path(A		, A				)		, organization='nielsr'		, commit_message='Add model'		, )


if __name__ == "__main__":
 a_       :Dict				  =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_url",
     default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
     type=str,
     help="URL of the original ConvNeXT checkpoint you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     type=str,
     required=True,
     help="Path to the output PyTorch model directory.",
 )

 a_       :Any				  =				parser.parse_args()
 convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











import math
def 	lowercase_							(A			:			int				):

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5		, int(math.sqrt(A				) + 1				)		, 6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True
def 	lowercase_							(A			:			int = 1_0_0_0		, A			:			int = 1_0_0_0				):
       snake_case__							:     str            =     [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1		, A				):
              for b in range(2		, A				):
                     if is_prime(A				):
                            snake_case__							:     Optional[Any]            =     0
                            snake_case__							:     Optional[int]            =     0
                            while is_prime((n**2) + (a * n) + b				):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   snake_case__							:     Union[str, Any]            =     [count, a, b]
       snake_case__							:     List[str]            =     longest[1] * longest[2]
       return ans


if __name__ == "__main__":
 print(solution(1_000, 1_000))






