






"""simple docstring"""







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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  []
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  []
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  []
					token.append((f'cvt.encoder.stages.{idx}.cls_token', '''stage2.cls_token'''))
					return token





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  []
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  '''imagenet-1k-id2label.json'''
					a__          =  1000

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

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

					a__          =  a__          =  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":
										a__          =  [1, 2, 10]

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

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

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

					a__          =  OrderedDict()
					a__          =  []

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

					a__          =  list_of_state_dict + final()
					for gg in list_of_state_dict:
										print(lowerCamelCase_)
					for i in range(len(lowerCamelCase_)):
										a__          =  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__":
	__a       :	Any             = 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.'
	)

	__a       :	List[Any]             = parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)










"""simple docstring"""







def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					assert column_title.isupper()
					a__          =  0
					a__          =  len(lowerCamelCase_) - 1
					a__          =  0

					while index >= 0:
										a__          =  (ord(column_title[index]) - 64) * pow(26 ,     lowerCamelCase_)
										answer += value
										power += 1
										index -= 1

					return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()


