






'''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 		_UpperCAmelCase   (							__A							:   List[Any]      ):
				a_		:  List[str]											=      []
				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 		_UpperCAmelCase   (							__A							:   str						,		__A							:   int      ):
				a_		:  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 		_UpperCAmelCase   (							__A							:   Tuple      ):
				a_		:  int											=      []
				token.append((f'cvt.encoder.stages.{idx}.cls_token', '''stage2.cls_token''')      )
				return token





def 		_UpperCAmelCase   (							):
				a_		:  Union[str, Any]											=      []
				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 		_UpperCAmelCase   (							__A							:   Union[str, Any]						,		__A							:   Union[str, Any]						,		__A							:   Optional[Any]						,		__A							:   List[Any]      ):
				a_		:  Union[str, Any]											=      """imagenet-1k-id2label.json"""
				a_		:  Optional[Any]											=      10_00

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

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

				a_		:  Dict											=      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":
								a_		:  Any											=      [1, 2, 10]

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

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								a_		:  Optional[int]											=      [2, 2, 20]
								a_		:  str											=      [3, 12, 16]
								a_		:  Any											=      [1_92, 7_68, 10_24]

				a_		:  Union[str, Any]											=      CvtForImageClassification(__UpperCamelCase      )
				a_		:  str											=      AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''      )
				a_		:  List[Any]											=      image_size
				a_		:  str											=      torch.load(__UpperCamelCase						,		map_location=torch.device('''cpu'''      )      )

				a_		:  Any											=      OrderedDict()
				a_		:  Tuple											=      []

				for idx in range(len(config.depth      )      ):
								if config.cls_token[idx]:
												a_		:  Optional[Any]											=      list_of_state_dict + cls_token(__UpperCamelCase      )
								a_		:  str											=      list_of_state_dict + embeddings(__UpperCamelCase      )
								for cnt in range(config.depth[idx]      ):
												a_		:  List[str]											=      list_of_state_dict + attention(__UpperCamelCase						,		__UpperCamelCase      )

				a_		:  str											=      list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(__UpperCamelCase      )
				for i in range(len(__UpperCamelCase      )      ):
								a_		:  Union[str, Any]											=      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__":
		__lowerCAmelCase       		=      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       		=      parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)











'''simple docstring'''







from math import pi, sqrt, tan





def 		_UpperCAmelCase   (							__A							:   float      ):
				if side_length < 0:
								raise ValueError('''surface_area_cube() only accepts non-negative values'''      )
				return 6 * side_length**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if length < 0 or breadth < 0 or height < 0:
								raise ValueError('''surface_area_cuboid() only accepts non-negative values'''      )
				return 2 * ((length * breadth) + (breadth * height) + (length * height))





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''surface_area_sphere() only accepts non-negative values'''      )
				return 4 * pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''surface_area_hemisphere() only accepts non-negative values'''      )
				return 3 * pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius < 0 or height < 0:
								raise ValueError('''surface_area_cone() only accepts non-negative values'''      )
				return pi * radius * (radius + (height**2 + radius**2) ** 0.5)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if radius_a < 0 or radius_a < 0 or height < 0:
								raise ValueError(
								    '''surface_area_conical_frustum() only accepts non-negative values'''      )
				a_		:  Any											=      (height**2 + (radius_a - radius_a) ** 2) ** 0.5
				return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius < 0 or height < 0:
								raise ValueError('''surface_area_cylinder() only accepts non-negative values'''      )
				return 2 * pi * radius * (height + radius)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if torus_radius < 0 or tube_radius < 0:
								raise ValueError('''surface_area_torus() only accepts non-negative values'''      )
				if torus_radius < tube_radius:
								raise ValueError(
								    '''surface_area_torus() does not support spindle or self intersecting tori'''      )
				return 4 * pow(__A						,		2      ) * torus_radius * tube_radius





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if length < 0 or width < 0:
								raise ValueError('''area_rectangle() only accepts non-negative values'''      )
				return length * width





def 		_UpperCAmelCase   (							__A							:   float      ):
				if side_length < 0:
								raise ValueError('''area_square() only accepts non-negative values'''      )
				return side_length**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if base < 0 or height < 0:
								raise ValueError('''area_triangle() only accepts non-negative values'''      )
				return (base * height) / 2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if sidea < 0 or sidea < 0 or sidea < 0:
								raise ValueError('''area_triangle_three_sides() only accepts non-negative values'''      )
				elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
								raise ValueError('''Given three sides do not form a triangle'''      )
				a_		:  int											=      (sidea + sidea + sidea) / 2
				a_		:  Optional[Any]											=      sqrt(
				    semi_perimeter
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)      )
				return area





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if base < 0 or height < 0:
								raise ValueError('''area_parallelogram() only accepts non-negative values'''      )
				return base * height





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if basea < 0 or basea < 0 or height < 0:
								raise ValueError('''area_trapezium() only accepts non-negative values'''      )
				return 1 / 2 * (basea + basea) * height





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''area_circle() only accepts non-negative values'''      )
				return pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius_x < 0 or radius_y < 0:
								raise ValueError('''area_ellipse() only accepts non-negative values'''      )
				return pi * radius_x * radius_y





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if diagonal_a < 0 or diagonal_a < 0:
								raise ValueError('''area_rhombus() only accepts non-negative values'''      )
				return 1 / 2 * diagonal_a * diagonal_a





def 		_UpperCAmelCase   (							__A							:   int						,		__A							:   float      ):
				if not isinstance(__A						,		__A      ) or sides < 3:
								raise ValueError(
								    '''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'''      )
				elif length < 0:
								raise ValueError(
								    '''area_reg_polygon() only accepts non-negative values as \
length of a side'''      )
				return (sides * length**2) / (4 * tan(pi / sides      ))
				return (sides * length**2) / (4 * tan(pi / sides      ))


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

		print('[DEMO] Areas of various geometric shapes: \n')
		print(F"""Rectangle: {area_rectangle(10, 20) = }""")
		print(F"""Square: {area_square(10) = }""")
		print(F"""Triangle: {area_triangle(10, 10) = }""")
		print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
		print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
		print(F"""Rhombus: {area_rhombus(10, 20) = }""")
		print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
		print(F"""Circle: {area_circle(20) = }""")
		print(F"""Ellipse: {area_ellipse(10, 20) = }""")
		print('\nSurface Areas of various geometric shapes: \n')
		print(F"""Cube: {surface_area_cube(20) = }""")
		print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
		print(F"""Sphere: {surface_area_sphere(20) = }""")
		print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
		print(F"""Cone: {surface_area_cone(10, 20) = }""")
		print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
		print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
		print(F"""Torus: {surface_area_torus(20, 10) = }""")
		print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
		print(F"""Square: {area_reg_polygon(4, 10) = }""")
		print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")




