


"""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__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_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      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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

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

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

					__SCREAMING_SNAKE_CASE		     =       __SCREAMING_SNAKE_CASE		     =       CvtConfig(num_labels=lowerCAmelCase_						,		idalabel=lowerCAmelCase_						,		labelaid=lowerCAmelCase_     )

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

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

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

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

					__SCREAMING_SNAKE_CASE		     =       OrderedDict()
					__SCREAMING_SNAKE_CASE		     =       []

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

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

					model.load_state_dict(lowerCAmelCase_     )
					model.save_pretrained(lowerCAmelCase_     )
					image_processor.save_pretrained(lowerCAmelCase_     )


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

if __name__ == "__main__":
		a__       :							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=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 numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return (data["data"], data["target"])




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       XGBClassifier()
					classifier.fit(lowerCAmelCase_						,		lowerCAmelCase_     )
					return classifier




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       load_iris()
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       data_handling(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       train_test_split(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		test_size=0.25     )

					__SCREAMING_SNAKE_CASE		     =       iris["target_names"]

					# Create an XGBoost Classifier from the training data
					__SCREAMING_SNAKE_CASE		     =       xgboost(lowerCAmelCase_						,		lowerCAmelCase_     )

					# Display the confusion matrix of the classifier with both training and test sets
					ConfusionMatrixDisplay.from_estimator(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		display_labels=lowerCAmelCase_						,		cmap="Blues"						,		normalize="true"						,		)
					plt.title("Normalized Confusion Matrix - IRIS Dataset"     )
					plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		main()
