


'''simple docstring'''
import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification


def 			_lowercase       (				UpperCamelCase__       :					Any			):
 __A  :		Any       =			SwinConfig()
 __A  :		int       =			swin_name.split('_'			)

 __A  :		str       =			name_split[1]
 __A  :		Any       =			int(name_split[4]			)
 __A  :		Union[str, Any]       =			int(name_split[3][-1]			)

 if model_size == "tiny":
  __A  :		Union[str, Any]       =			96
  __A  :		Tuple       =			(2, 2, 6, 2)
  __A  :		str       =			(3, 6, 12, 24)
 elif model_size == "small":
  __A  :		Tuple       =			96
  __A  :		Optional[int]       =			(2, 2, 18, 2)
  __A  :		Optional[int]       =			(3, 6, 12, 24)
 elif model_size == "base":
  __A  :		Tuple       =			128
  __A  :		Optional[Any]       =			(2, 2, 18, 2)
  __A  :		str       =			(4, 8, 16, 32)
 else:
  __A  :		Dict       =			192
  __A  :		str       =			(2, 2, 18, 2)
  __A  :		int       =			(6, 12, 24, 48)

 if "in22k" in swin_name:
  __A  :		Dict       =			21841
 else:
  __A  :		Any       =			1000
  __A  :		List[Any]       =			'huggingface/label-files'
  __A  :		Any       =			'imagenet-1k-id2label.json'
  __A  :		str       =			json.load(open(hf_hub_download(UpperCamelCase__,				UpperCamelCase__,				repo_type='dataset'			),				'r'			)			)
  __A  :		Optional[Any]       =			{int(UpperCamelCase__			): v for k, v in idalabel.items()}
  __A  :		List[Any]       =			idalabel
  __A  :		Optional[int]       =			{v: k for k, v in idalabel.items()}

 __A  :		Tuple       =			img_size
 __A  :		int       =			num_classes
 __A  :		Optional[int]       =			embed_dim
 __A  :		Optional[int]       =			depths
 __A  :		Optional[int]       =			num_heads
 __A  :		Optional[int]       =			window_size

 return config


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
 if "patch_embed.proj" in name:
  __A  :		str       =			name.replace('patch_embed.proj',				'embeddings.patch_embeddings.projection'			)
 if "patch_embed.norm" in name:
  __A  :		Optional[Any]       =			name.replace('patch_embed.norm',				'embeddings.norm'			)
 if "layers" in name:
  __A  :		Union[str, Any]       =			'encoder.' + name
 if "attn.proj" in name:
  __A  :		List[str]       =			name.replace('attn.proj',				'attention.output.dense'			)
 if "attn" in name:
  __A  :		int       =			name.replace('attn',				'attention.self'			)
 if "norm1" in name:
  __A  :		Any       =			name.replace('norm1',				'layernorm_before'			)
 if "norm2" in name:
  __A  :		List[Any]       =			name.replace('norm2',				'layernorm_after'			)
 if "mlp.fc1" in name:
  __A  :		Union[str, Any]       =			name.replace('mlp.fc1',				'intermediate.dense'			)
 if "mlp.fc2" in name:
  __A  :		Tuple       =			name.replace('mlp.fc2',				'output.dense'			)

 if name == "norm.weight":
  __A  :		List[str]       =			'layernorm.weight'
 if name == "norm.bias":
  __A  :		Tuple       =			'layernorm.bias'

 if "head" in name:
  __A  :		Optional[int]       =			name.replace('head',				'classifier'			)
 else:
  __A  :		Optional[int]       =			'swin.' + name

 return name


def 			_lowercase       (				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					int			):
 for key in orig_state_dict.copy().keys():
  __A  :		Dict       =			orig_state_dict.pop(UpperCamelCase__			)

  if "mask" in key:
   continue
  elif "qkv" in key:
   __A  :		List[str]       =			key.split('.'			)
   __A  :		Dict       =			int(key_split[1]			)
   __A  :		Optional[int]       =			int(key_split[3]			)
   __A  :		Dict       =			model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

   if "weight" in key:
    __A  :		Any       =			val[:dim, :]
    __A  :		List[Any]       =			val[
        dim : dim * 2, :
    ]
    __A  :		Dict       =			val[-dim:, :]
   else:
    __A  :		Optional[Any]       =			val[
        :dim
    ]
    __A  :		Dict       =			val[
        dim : dim * 2
    ]
    __A  :		List[Any]       =			val[
        -dim:
    ]
  else:
   __A  :		str       =			val

 return orig_state_dict


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Dict			):
 __A  :		Dict       =			timm.create_model(UpperCamelCase__,				pretrained=UpperCamelCase__			)
 timm_model.eval()

 __A  :		str       =			get_swin_config(UpperCamelCase__			)
 __A  :		Dict       =			SwinForImageClassification(UpperCamelCase__			)
 model.eval()

 __A  :		Any       =			convert_state_dict(timm_model.state_dict(),				UpperCamelCase__			)
 model.load_state_dict(UpperCamelCase__			)

 __A  :		str       =			'http://images.cocodataset.org/val2017/000000039769.jpg'

 __A  :		Any       =			AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_',				'-'			)			)			)
 __A  :		Tuple       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
 __A  :		Optional[int]       =			image_processor(images=UpperCamelCase__,				return_tensors='pt'			)

 __A  :		int       =			timm_model(inputs['pixel_values']			)
 __A  :		List[Any]       =			model(**UpperCamelCase__			).logits

 assert torch.allclose(UpperCamelCase__,				UpperCamelCase__,				atol=1E-3			)

 print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""			)
 model.save_pretrained(UpperCamelCase__			)

 print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
 image_processor.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
 UpperCAmelCase_ :     int								=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--swin_name',
     default='swin_tiny_patch4_window7_224',
     type=str,
     help='Name of the Swin timm model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )

 UpperCAmelCase_ :     Union[str, Any]								=      parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)










'''simple docstring'''
from __future__ import annotations


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 __A  :		Dict       =			[]
 __A     ,__A  :		Dict       =			input_list[low:mid], input_list[mid : high + 1]
 while left and right:
  result.append((left if left[0] <= right[0] else right).pop(0			)			)
 __A  :		Optional[Any]       =			result + left + right
 return input_list


def 			_lowercase       (				UpperCamelCase__       :					list			):
 if len(UpperCamelCase__			) <= 1:
  return input_list
 __A  :		Any       =			list(UpperCamelCase__			)

 # iteration for two-way merging
 __A  :		Dict       =			2
 while p <= len(UpperCamelCase__			):
  # getting low, high and middle value for merge-sort of single list
  for i in range(0,				len(UpperCamelCase__			),				UpperCamelCase__			):
   __A  :		Union[str, Any]       =			i
   __A  :		Tuple       =			i + p - 1
   __A  :		Union[str, Any]       =			(low + high + 1) // 2
   __A  :		Tuple       =			merge(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
  # final merge of last two parts
  if p * 2 >= len(UpperCamelCase__			):
   __A  :		Optional[int]       =			i
   __A  :		str       =			merge(UpperCamelCase__,				0,				UpperCamelCase__,				len(UpperCamelCase__			) - 1			)
   break
  p *= 2

 return input_list


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






