


"""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 _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []
			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			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []
			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			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []
			token.append((f"cvt.encoder.stages.{idx}.cls_token", "stage2.cls_token")   )
			return token


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    []
			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			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    "imagenet-1k-id2label.json"
			__lowerCAmelCase       					=    1000

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

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

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

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

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

			__lowerCAmelCase       					=    CvtForImageClassification(_UpperCamelCase   )
			__lowerCAmelCase       					=    AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"   )
			__lowerCAmelCase       					=    image_size
			__lowerCAmelCase       					=    torch.load(_UpperCamelCase					,				map_location=torch.device("cpu"   )   )

			__lowerCAmelCase       					=    OrderedDict()
			__lowerCAmelCase       					=    []

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

			__lowerCAmelCase       					=    list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(_UpperCamelCase   )
			for i in range(len(_UpperCamelCase   )   ):
						__lowerCAmelCase       					=    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__":
	A  :     List[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=3_8_4,
	    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  :     Dict    					=    parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)








"""simple docstring"""


import cmath
import math


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    math.radians(_UpperCamelCase   )
			__lowerCAmelCase       					=    math.radians(_UpperCamelCase   )

			# Convert voltage and current to rectangular form
			__lowerCAmelCase       					=    cmath.rect(_UpperCamelCase					,				_UpperCamelCase   )
			__lowerCAmelCase       					=    cmath.rect(_UpperCamelCase					,				_UpperCamelCase   )

			# Calculate apparent power
			return voltage_rect * current_rect


if __name__ == "__main__":
	import doctest

	doctest.testmod()




