






"""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        snake_case   (     A__		):
			UpperCAmelCase_  :				Tuple      =      []
			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        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				List[Any]      =      []
			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        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      []
			token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")		)
			return token

def        snake_case   (     ):
			UpperCAmelCase_  :				str      =      []
			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        snake_case   (     A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				int      =      "imagenet-1k-id2label.json"
			UpperCAmelCase_  :				Union[str, Any]      =      10_00

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

			UpperCAmelCase_  :				Optional[int]      =      idalabel
			UpperCAmelCase_  :				Optional[Any]      =      {v: k for k, v in idalabel.items()}

			UpperCAmelCase_  :				str      =      CvtConfig(num_labels=A__			,idalabel=A__			,labelaid=A__		)

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

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

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

			UpperCAmelCase_  :				Tuple      =      CvtForImageClassification(A__		)
			UpperCAmelCase_  :				Tuple      =      AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"		)
			UpperCAmelCase_  :				int      =      image_size
			UpperCAmelCase_  :				List[str]      =      torch.load(A__			,map_location=torch.device("cpu"		)		)

			UpperCAmelCase_  :				int      =      OrderedDict()
			UpperCAmelCase_  :				Optional[Any]      =      []

			for idx in range(len(config.depth		)		):
						if config.cls_token[idx]:
									UpperCAmelCase_  :				Optional[Any]      =      list_of_state_dict + cls_token(A__		)
						UpperCAmelCase_  :				Any      =      list_of_state_dict + embeddings(A__		)
						for cnt in range(config.depth[idx]		):
									UpperCAmelCase_  :				Dict      =      list_of_state_dict + attention(A__			,A__		)

			UpperCAmelCase_  :				Union[str, Any]      =      list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(A__		)
			for i in range(len(A__		)		):
						UpperCAmelCase_  :				Any      =      original_weights[list_of_state_dict[i][1]]

			model.load_state_dict(A__		)
			model.save_pretrained(A__		)
			image_processor.save_pretrained(A__		)


# 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"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize

def        snake_case   (     A__		):
			return np.dot(A__			,A__		)







class     UpperCamelCase_ :





	def __init__(   self      :				int			,			*,
	    lowerCAmelCase_      :				float = np.inf			,			lowerCAmelCase_      :				str = "linear"			,			lowerCAmelCase_      :				float = 0.0			,			)		->    None:
				UpperCAmelCase_  :				List[str]      =      regularization
				UpperCAmelCase_  :				Tuple      =      gamma
				if kernel == "linear":
							UpperCAmelCase_  :				List[Any]      =      self.__linear
				elif kernel == "rbf":
							if self.gamma == 0:
										raise ValueError("rbf kernel requires gamma"		)
							if not isinstance(self.gamma			,			(float, int)		):
										raise ValueError("gamma must be float or int"		)
							if not self.gamma > 0:
										raise ValueError("gamma must be > 0"		)
							UpperCAmelCase_  :				Union[str, Any]      =      self.__rbf
							# in the future, there could be a default value like in sklearn
							# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
							# previously it was 1/(n_features)
				else:
							UpperCAmelCase_  :				Tuple      =      f"""Unknown kernel: {kernel}"""
							raise ValueError(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.dot(lowerCAmelCase_			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				list[ndarray]			,			lowerCAmelCase_      :				ndarray		)		->    None:
				UpperCAmelCase_  :				int      =      observations
				UpperCAmelCase_  :				Optional[Any]      =      classes

				# using Wolfe's Dual to calculate w.
				# Primal problem: minimize 1/2*norm_squared(w)
				#   constraint: yn(w . xn + b) >= 1
				#
				# With l a vector
				# Dual problem: maximize sum_n(ln) -
				#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
				#   constraint: self.C >= ln >= 0
				#           and sum_n(ln*yn) = 0
				# Then we get w using w = sum_n(ln*yn*xn)
				# At the end we can get b ~= mean(yn - w . xn)
				#
				# Since we use kernels, we only need l_star to calculate b
				# and to classify observations

				((UpperCAmelCase_)  ,			)  :				Optional[int]      =      np.shape(lowerCAmelCase_		)

				def to_minimize(lowerCAmelCase_      :				ndarray		) -> float:
							UpperCAmelCase_  :				int      =      0
							((UpperCAmelCase_)  ,			)  :				str      =      np.shape(lowerCAmelCase_		)
							for i in range(lowerCAmelCase_		):
										for j in range(lowerCAmelCase_		):
													s += (
													    candidate[i]
													    * candidate[j]
													    * classes[i]
													    * classes[j]
													    * self.kernel(observations[i]			,			observations[j]		)
													)
							return 1 / 2 * s - sum(lowerCAmelCase_		)

				UpperCAmelCase_  :				int      =      LinearConstraint(lowerCAmelCase_			,			0			,			0		)
				UpperCAmelCase_  :				Dict      =      Bounds(0			,			self.regularization		)

				UpperCAmelCase_  :				Optional[Any]      =      minimize(
				    lowerCAmelCase_			,			np.ones(lowerCAmelCase_		)			,			bounds=lowerCAmelCase_			,			constraints=[ly_contraint]		).x
				UpperCAmelCase_  :				Optional[Any]      =      l_star

				# calculating mean offset of separation plane to points
				UpperCAmelCase_  :				Optional[int]      =      0
				for i in range(lowerCAmelCase_		):
							for j in range(lowerCAmelCase_		):
										s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
										    observations[i]			,			observations[j]		)
				UpperCAmelCase_  :				str      =      s / n





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				ndarray		)		->    int:
				UpperCAmelCase_  :				List[Any]      =      sum(
				    self.optimum[n]
				    * self.classes[n]
				    * self.kernel(self.observations[n]			,			lowerCAmelCase_		)
				    for n in range(len(self.classes		)		)		)
				return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()



