
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_		(       __lowercase   :	Tuple ) ->  Tuple:
			_snake_case		=			[]
			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_		(       __lowercase   :	Tuple       ,					__lowercase   :	List[str] ) ->  Optional[int]:
			_snake_case		=			[]
			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_		(       __lowercase   :	Dict ) ->  Optional[int]:
			_snake_case		=			[]
			token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
			return token






def        a_		(       ) ->  List[str]:
			_snake_case		=			[]
			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_		(       __lowercase   :	Tuple       ,					__lowercase   :	Tuple       ,					__lowercase   :	List[Any]       ,					__lowercase   :	Dict ) ->  Optional[Any]:
			_snake_case		=			'imagenet-1k-id2label.json'
			_snake_case		=			1_000

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

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

			_snake_case		=			_snake_case		=			CvtConfig(num_labels=__lowercase       ,					idalabel=__lowercase       ,					labelaid=__lowercase )

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

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

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

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

			_snake_case		=			OrderedDict()
			_snake_case		=			[]

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

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

			model.load_state_dict(__lowercase )
			model.save_pretrained(__lowercase )
			image_processor.save_pretrained(__lowercase )


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

if __name__ == "__main__":
			_lowerCamelCase       :				int       =			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.'''
			)

			_lowerCamelCase       :				int       =			parser.parse_args()
			convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)

def        a_		(       __lowercase   :	str ) ->  int:
			_snake_case		=			hex_num.strip()
			if not hex_num:
						raise ValueError('No value was passed to the function' )

			_snake_case		=			hex_num[0] == '-'
			if is_negative:
						_snake_case		=			hex_num[1:]

			try:
						_snake_case		=			int(__lowercase       ,					16 )
			except ValueError:
						raise ValueError('Invalid value was passed to the function' )

			_snake_case		=			''
			while int_num > 0:
						_snake_case		=			str(int_num % 2 ) + bin_str
						int_num >>= 1

			return int(('-' + bin_str) if is_negative else bin_str )


if __name__ == "__main__":
			import doctest

			doctest.testmod()