


'''simple docstring'''







def _UpperCamelCase       (     __A    )							->		list:


      '''simple docstring'''



      def merge(__A      ,			__A    ) -> list:

            def _merge():
                  while left and right:
                        yield (left if left[0] <= right[0] else right).pop(0    )
                  yield from left
                  yield from right

            return list(_merge()    )

      if len(__A    ) <= 1:
            return collection
      UpperCamelCase__										=				len(__A    ) // 2
      return merge(merge_sort(collection[:mid]    )      ,			merge_sort(collection[mid:]    )    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  a__  :					List[str]        =       input('Enter numbers separated by a comma:\n').strip()
  a__  :					Tuple        =       [int(item) for item in user_input.split(',')]
  print(*merge_sort(unsorted), sep=',')






import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def 			lowercase_				(			_lowerCamelCase							:       int):
    lowercase__ :   int    					=				[]
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
            f'''stage{idx}.patch_embed.proj.weight''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
            f'''stage{idx}.patch_embed.proj.bias''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
            f'''stage{idx}.patch_embed.norm.weight''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
            f'''stage{idx}.patch_embed.norm.bias''',
        ))
    return embed

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       int):
    lowercase__ :   Optional[Any]    					=				[]
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
        ))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias'''))
    return attention_weights

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]):
    lowercase__ :   Tuple    					=				[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token"))
    return token

def 			lowercase_				(			):
    lowercase__ :   List[str]    					=				[]
    head.append(("layernorm.weight", "norm.weight"))
    head.append(("layernorm.bias", "norm.bias"))
    head.append(("classifier.weight", "head.weight"))
    head.append(("classifier.bias", "head.bias"))
    return head

def 			lowercase_				(			_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Union[str, Any]):
    lowercase__ :   Optional[Any]    					=				"imagenet-1k-id2label.json"
    lowercase__ :   List[str]    					=				1000

    lowercase__ :   Dict    					=				"huggingface/label-files"
    lowercase__ :   List[Any]    					=				num_labels
    lowercase__ :   Tuple    					=				json.load(open(cached_download(hf_hub_url(_lowerCamelCase		,	_lowerCamelCase		,	repo_type="dataset"))		,	"r"))
    lowercase__ :   Tuple    					=				{int(_lowerCamelCase): v for k, v in idalabel.items()}

    lowercase__ :   Any    					=				idalabel
    lowercase__ :   List[Any]    					=				{v: k for k, v in idalabel.items()}

    lowercase__ :   Optional[int]    					=				CvtConfig(num_labels=_lowerCamelCase		,	idalabel=_lowerCamelCase		,	labelaid=_lowerCamelCase)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit("/"		,	1)[-1][4:6] == "13":
        lowercase__ :   Any    					=				[1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit("/"		,	1)[-1][4:6] == "21":
        lowercase__ :   Tuple    					=				[1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowercase__ :   Union[str, Any]    					=				[2, 2, 20]
        lowercase__ :   Optional[Any]    					=				[3, 12, 16]
        lowercase__ :   Optional[Any]    					=				[192, 768, 1024]

    lowercase__ :   Union[str, Any]    					=				CvtForImageClassification(_lowerCamelCase)
    lowercase__ :   Tuple    					=				AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
    lowercase__ :   int    					=				image_size
    lowercase__ :   Dict    					=				torch.load(_lowerCamelCase		,	map_location=torch.device("cpu"))

    lowercase__ :   Any    					=				OrderedDict()
    lowercase__ :   int    					=				[]

    for idx in range(len(config.depth)):
        if config.cls_token[idx]:
            lowercase__ :   Dict    					=				list_of_state_dict + cls_token(_lowerCamelCase)
        lowercase__ :   List[str]    					=				list_of_state_dict + embeddings(_lowerCamelCase)
        for cnt in range(config.depth[idx]):
            lowercase__ :   Any    					=				list_of_state_dict + attention(_lowerCamelCase		,	_lowerCamelCase)

    lowercase__ :   List[str]    					=				list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_lowerCamelCase)
    for i in range(len(_lowerCamelCase)):
        lowercase__ :   Dict    					=				original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_lowerCamelCase)
    model.save_pretrained(_lowerCamelCase)
    image_processor.save_pretrained(_lowerCamelCase)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
      UpperCamelCase  							=						argparse.ArgumentParser()
      parser.add_argument(
          '''--cvt_model''',
          default='''cvt-w24''',
          type=str,
          help='''Name of the cvt model you\'d like to convert.''',
      )
      parser.add_argument(
          '''--image_size''',
          default=384,
          type=int,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--cvt_file_name''',
          default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
          type=str,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      UpperCamelCase  							=						parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



