
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 							A			(  _UpperCAmelCase       :     List[Any]					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     []
			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 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     int					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     []
			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 							A			(  _UpperCAmelCase       :     Tuple					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     []
			token.append((F"cvt.encoder.stages.{idx}.cls_token", 'stage2.cls_token')					)
			return token





def 							A			(  )       ->				int:







			'''simple docstring'''
			_UpperCAmelCase	 =     []
			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 							A			(  _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     List[Any]					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     """imagenet-1k-id2label.json"""
			_UpperCAmelCase	 =     1_000

			_UpperCAmelCase	 =     """huggingface/label-files"""
			_UpperCAmelCase	 =     num_labels
			_UpperCAmelCase	 =     json.load(open(cached_download(hf_hub_url(__UpperCamelCase							,     __UpperCamelCase							,     repo_type='dataset'					)					)							,     'r'					)					)
			_UpperCAmelCase	 =     {int(__UpperCamelCase					): v for k, v in idalabel.items()}

			_UpperCAmelCase	 =     idalabel
			_UpperCAmelCase	 =     {v: k for k, v in idalabel.items()}

			_UpperCAmelCase	 =     CvtConfig(num_labels=__UpperCamelCase							,     idalabel=__UpperCamelCase							,     labelaid=__UpperCamelCase					)

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

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

			# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
			else:
						_UpperCAmelCase	 =     [2, 2, 20]
						_UpperCAmelCase	 =     [3, 12, 16]
						_UpperCAmelCase	 =     [192, 768, 1_024]

			_UpperCAmelCase	 =     CvtForImageClassification(__UpperCamelCase					)
			_UpperCAmelCase	 =     AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					)
			_UpperCAmelCase	 =     image_size
			_UpperCAmelCase	 =     torch.load(__UpperCamelCase							,     map_location=torch.device('cpu'					)					)

			_UpperCAmelCase	 =     OrderedDict()
			_UpperCAmelCase	 =     []

			for idx in range(len(config.depth					)					):
						if config.cls_token[idx]:
									_UpperCAmelCase	 =     list_of_state_dict + cls_token(__UpperCamelCase					)
						_UpperCAmelCase	 =     list_of_state_dict + embeddings(__UpperCamelCase					)
						for cnt in range(config.depth[idx]					):
									_UpperCAmelCase	 =     list_of_state_dict + attention(__UpperCamelCase							,     __UpperCamelCase					)

			_UpperCAmelCase	 =     list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(__UpperCamelCase					)
			for i in range(len(__UpperCamelCase					)					):
						_UpperCAmelCase	 =     original_weights[list_of_state_dict[i][1]]

			model.load_state_dict(__UpperCamelCase					)
			model.save_pretrained(__UpperCamelCase					)
			image_processor.save_pretrained(__UpperCamelCase					)


# 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)





def 							A			(  _UpperCAmelCase       :     list					)       ->				list:







			'''simple docstring'''
			if len(_UpperCAmelCase					) <= 1:
						return lst

			_UpperCAmelCase	 =     1

			while i < len(_UpperCAmelCase					):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									_UpperCAmelCase ,  _UpperCAmelCase	 =     lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												_UpperCAmelCase	 =     1

			return lst


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