



'''simple docstring'''






import random




def        snake_case_   (	lowerCAmelCase_						)->						int:





       '''simple docstring'''


       def is_sorted(lowerCAmelCase_						):
              for i in range(len(lowerCAmelCase_						) - 1						):
                     if collection[i] > collection[i + 1]:
                            return False
              return True

       while not is_sorted(lowerCAmelCase_						):
              random.shuffle(lowerCAmelCase_						)
       return collection


if __name__ == "__main__":
   A_				:					int							=      input("""Enter numbers separated by a comma:\n""").strip()
   A_				:					Union[str, Any]							=      [int(item) for item in user_input.split(""",""")]
   print(bogo_sort(unsorted))







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()
UpperCAmelCase     =       logging.get_logger(__name__)






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    Tuple:




      """simple docstring"""

      snake_case_			= ConvNextConfig()

      if "tiny" in checkpoint_url:
            snake_case_			= [3, 3, 9, 3]
            snake_case_			= [96, 192, 384, 768]
      if "small" in checkpoint_url:
            snake_case_			= [3, 3, 27, 3]
            snake_case_			= [96, 192, 384, 768]
      if "base" in checkpoint_url:
            snake_case_			= [3, 3, 27, 3]
            snake_case_			= [128, 256, 512, 1024]
      if "large" in checkpoint_url:
            snake_case_			= [3, 3, 27, 3]
            snake_case_			= [192, 384, 768, 1536]
      if "xlarge" in checkpoint_url:
            snake_case_			= [3, 3, 27, 3]
            snake_case_			= [256, 512, 1024, 2048]

      if "1k" in checkpoint_url:
            snake_case_			= 1000
            snake_case_			= '''imagenet-1k-id2label.json'''
            snake_case_			= (1, 1000)
      else:
            snake_case_			= 2_1841
            snake_case_			= '''imagenet-22k-id2label.json'''
            snake_case_			= (1, 2_1841)

      snake_case_			= '''huggingface/label-files'''
      snake_case_			= num_labels
      snake_case_			= json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   repo_type='''dataset'''   )   ,   '''r'''   )   )
      snake_case_			= {int(SCREAMING_SNAKE_CASE   ): 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[9205]
            del idalabel[1_5027]
      snake_case_			= idalabel
      snake_case_			= {v: k for k, v in idalabel.items()}
      snake_case_			= hidden_sizes
      snake_case_			= depths

      return config, expected_shape






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    Tuple:




      """simple docstring"""

      if "downsample_layers.0.0" in name:
            snake_case_			= name.replace('''downsample_layers.0.0'''   ,   '''embeddings.patch_embeddings'''   )
      if "downsample_layers.0.1" in name:
            snake_case_			= name.replace('''downsample_layers.0.1'''   ,   '''embeddings.norm'''   )  # we rename to layernorm later on
      if "downsample_layers.1.0" in name:
            snake_case_			= name.replace('''downsample_layers.1.0'''   ,   '''stages.1.downsampling_layer.0'''   )
      if "downsample_layers.1.1" in name:
            snake_case_			= name.replace('''downsample_layers.1.1'''   ,   '''stages.1.downsampling_layer.1'''   )
      if "downsample_layers.2.0" in name:
            snake_case_			= name.replace('''downsample_layers.2.0'''   ,   '''stages.2.downsampling_layer.0'''   )
      if "downsample_layers.2.1" in name:
            snake_case_			= name.replace('''downsample_layers.2.1'''   ,   '''stages.2.downsampling_layer.1'''   )
      if "downsample_layers.3.0" in name:
            snake_case_			= name.replace('''downsample_layers.3.0'''   ,   '''stages.3.downsampling_layer.0'''   )
      if "downsample_layers.3.1" in name:
            snake_case_			= 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_			= name[: len('''stages.0'''   )] + '''.layers''' + name[len('''stages.0'''   ) :]
      if "stages" in name:
            snake_case_			= name.replace('''stages'''   ,   '''encoder.stages'''   )
      if "norm" in name:
            snake_case_			= name.replace('''norm'''   ,   '''layernorm'''   )
      if "gamma" in name:
            snake_case_			= name.replace('''gamma'''   ,   '''layer_scale_parameter'''   )
      if "head" in name:
            snake_case_			= name.replace('''head'''   ,   '''classifier'''   )

      return name






def 			__lowerCAmelCase       ()->    Optional[Any]:




      """simple docstring"""

      snake_case_			= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      snake_case_			= Image.open(requests.get(SCREAMING_SNAKE_CASE   ,   stream=SCREAMING_SNAKE_CASE   ).raw   )
      return im






@torch.no_grad()
def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    str:




      """simple docstring"""

      snake_case_			,       snake_case_			= get_convnext_config(SCREAMING_SNAKE_CASE   )
      # load original state_dict from URL
      snake_case_			= torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE   )['''model''']
      # rename keys
      for key in state_dict.copy().keys():
            snake_case_			= state_dict.pop(SCREAMING_SNAKE_CASE   )
            snake_case_			= val
      # add prefix to all keys expect classifier head
      for key in state_dict.copy().keys():
            snake_case_			= state_dict.pop(SCREAMING_SNAKE_CASE   )
            if not key.startswith('''classifier'''   ):
                  snake_case_			= '''convnext.''' + key
            snake_case_			= val

      # load HuggingFace model
      snake_case_			= ConvNextForImageClassification(SCREAMING_SNAKE_CASE   )
      model.load_state_dict(SCREAMING_SNAKE_CASE   )
      model.eval()

      # Check outputs on an image, prepared by ConvNextImageProcessor
      snake_case_			= 224 if '''224''' in checkpoint_url else 384
      snake_case_			= ConvNextImageProcessor(size=SCREAMING_SNAKE_CASE   )
      snake_case_			= image_processor(images=prepare_img()   ,   return_tensors='''pt'''   ).pixel_values

      snake_case_			= model(SCREAMING_SNAKE_CASE   ).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_			= torch.tensor([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
            snake_case_			= torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
            snake_case_			= torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
            snake_case_			= torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
            snake_case_			= torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
            snake_case_			= torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
            snake_case_			= torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
            snake_case_			= torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
            snake_case_			= torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
            snake_case_			= torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
            snake_case_			= torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
            snake_case_			= torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
            snake_case_			= torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
            snake_case_			= torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9]   )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
            snake_case_			= torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4]   )
      else:
            raise ValueError(f'''Unknown URL: {checkpoint_url}'''   )

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

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

      print('''Pushing model to the hub...'''   )
      snake_case_			= '''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(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )   ,   organization='''nielsr'''   ,   commit_message='''Add model'''   ,   )


if __name__ == "__main__":
   UpperCAmelCase     =       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.""",
   )

   UpperCAmelCase     =       parser.parse_args()
   convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)