






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 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   []
				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 					_lowerCamelCase(			lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		Dict      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   []
				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 					_lowerCamelCase(			lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	int													=   []
				token.append((F'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token')      )
				return token



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	int													=   []
				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 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[int]    ,    lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		Dict      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Tuple													=   'imagenet-1k-id2label.json'
				SCREAMING_SNAKE_CASE_   :	str													=   1000

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   'huggingface/label-files'
				SCREAMING_SNAKE_CASE_   :	List[str]													=   num_labels
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   json.load(open(cached_download(hf_hub_url(__lowercase    ,    __lowercase    ,    repo_type='dataset'      )      )    ,    'r'      )      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {int(__lowercase      ): v for k, v in idalabel.items()}

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

				SCREAMING_SNAKE_CASE_   :	Tuple													=   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":
								SCREAMING_SNAKE_CASE_   :	str													=   [1, 2, 10]

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

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

				SCREAMING_SNAKE_CASE_   :	Dict													=   CvtForImageClassification(__lowercase      )
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   image_size
				SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.load(__lowercase    ,    map_location=torch.device('cpu'      )      )

				SCREAMING_SNAKE_CASE_   :	List[Any]													=   OrderedDict()
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   []

				for idx in range(len(config.depth      )      ):
								if config.cls_token[idx]:
												SCREAMING_SNAKE_CASE_   :	str													=   list_of_state_dict + cls_token(__lowercase      )
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   list_of_state_dict + embeddings(__lowercase      )
								for cnt in range(config.depth[idx]      ):
												SCREAMING_SNAKE_CASE_   :	Dict													=   list_of_state_dict + attention(__lowercase    ,    __lowercase      )

				SCREAMING_SNAKE_CASE_   :	str													=   list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(__lowercase      )
				for i in range(len(__lowercase      )      ):
								SCREAMING_SNAKE_CASE_   :	List[str]													=   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__":
				A        =			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        =			parser.parse_args()
				convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)










import math



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return math.pow(lowerCAmelCase__    ,    2      ) - a



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return 2 * x



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   2.0

				while start <= a:
								SCREAMING_SNAKE_CASE_   :	str													=   math.pow(lowerCAmelCase__    ,    2      )

				return start



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int = 9999    ,    lowerCAmelCase__				:		float = 0.00_000_000_000_001      ):





				'''simple docstring'''







				if a < 0:
								raise ValueError('math domain error'      )

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_initial_point(lowerCAmelCase__      )

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Tuple													=   value
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   value - fx(lowerCAmelCase__    ,    lowerCAmelCase__      ) / fx_derivative(lowerCAmelCase__      )
								if abs(prev_value - value      ) < tolerance:
												return value

				return value


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

				testmod()