
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:					Any     =logging.get_logger(__name__)



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[int]    ):






  """simple docstring"""




  _lowerCAmelCase						=		ConvNextConfig()

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

  if "1k" in checkpoint_url:
    _lowerCAmelCase						=		1000
    _lowerCAmelCase						=		'imagenet-1k-id2label.json'
    _lowerCAmelCase						=		(1, 1000)
  else:
    _lowerCAmelCase						=		21841
    _lowerCAmelCase						=		'imagenet-22k-id2label.json'
    _lowerCAmelCase						=		(1, 21841)

  _lowerCAmelCase						=		'huggingface/label-files'
  _lowerCAmelCase						=		num_labels
  _lowerCAmelCase						=		json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				repo_type='dataset'    )		,				'r'    )    )
  _lowerCAmelCase						=		{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[15027]
  _lowerCAmelCase						=		idalabel
  _lowerCAmelCase						=		{v: k for k, v in idalabel.items()}
  _lowerCAmelCase						=		hidden_sizes
  _lowerCAmelCase						=		depths

  return config, expected_shape



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




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

  return name



def 	_a  (					):






  """simple docstring"""




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



@torch.no_grad()
def 	_a  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




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

  # load HuggingFace model
  _lowerCAmelCase						=		ConvNextForImageClassification(__SCREAMING_SNAKE_CASE    )
  model.load_state_dict(__SCREAMING_SNAKE_CASE    )
  model.eval()

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

  _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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":
    _lowerCAmelCase						=		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...'    )
  _lowerCAmelCase						=		'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:					List[str]     =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:					str     =parser.parse_args()
       convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


import random



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




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

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


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