


'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification





def __snake_case      (   UpperCAmelCase_		:		Dict	):
	lowerCamelCase_  				=						SwinvaConfig()
	lowerCamelCase_  				=						swinva_name.split("_"	)

	lowerCamelCase_  				=						name_split[1]
	if "to" in name_split[3]:
		lowerCamelCase_  				=						int(name_split[3][-3:]	)
	else:
		lowerCamelCase_  				=						int(name_split[3]	)
	if "to" in name_split[2]:
		lowerCamelCase_  				=						int(name_split[2][-2:]	)
	else:
		lowerCamelCase_  				=						int(name_split[2][6:]	)

	if model_size == "tiny":
		lowerCamelCase_  				=						96
		lowerCamelCase_  				=						(2, 2, 6, 2)
		lowerCamelCase_  				=						(3, 6, 12, 24)
	elif model_size == "small":
		lowerCamelCase_  				=						96
		lowerCamelCase_  				=						(2, 2, 18, 2)
		lowerCamelCase_  				=						(3, 6, 12, 24)
	elif model_size == "base":
		lowerCamelCase_  				=						128
		lowerCamelCase_  				=						(2, 2, 18, 2)
		lowerCamelCase_  				=						(4, 8, 16, 32)
	else:
		lowerCamelCase_  				=						192
		lowerCamelCase_  				=						(2, 2, 18, 2)
		lowerCamelCase_  				=						(6, 12, 24, 48)

	if "to" in swinva_name:
		lowerCamelCase_  				=						(12, 12, 12, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		lowerCamelCase_  				=						21841
		lowerCamelCase_  				=						"huggingface/label-files"
		lowerCamelCase_  				=						"imagenet-22k-id2label.json"
		lowerCamelCase_  				=						json.load(open(hf_hub_download(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()}

	else:
		lowerCamelCase_  				=						1000
		lowerCamelCase_  				=						"huggingface/label-files"
		lowerCamelCase_  				=						"imagenet-1k-id2label.json"
		lowerCamelCase_  				=						json.load(open(hf_hub_download(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_  				=						img_size
	lowerCamelCase_  				=						num_classes
	lowerCamelCase_  				=						embed_dim
	lowerCamelCase_  				=						depths
	lowerCamelCase_  				=						num_heads
	lowerCamelCase_  				=						window_size

	return config





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]	):
	if "patch_embed.proj" in name:
		lowerCamelCase_  				=						name.replace("patch_embed.proj"      ,       "embeddings.patch_embeddings.projection"	)
	if "patch_embed.norm" in name:
		lowerCamelCase_  				=						name.replace("patch_embed.norm"      ,       "embeddings.norm"	)
	if "layers" in name:
		lowerCamelCase_  				=						"encoder." + name
	if "attn.proj" in name:
		lowerCamelCase_  				=						name.replace("attn.proj"      ,       "attention.output.dense"	)
	if "attn" in name:
		lowerCamelCase_  				=						name.replace("attn"      ,       "attention.self"	)
	if "norm1" in name:
		lowerCamelCase_  				=						name.replace("norm1"      ,       "layernorm_before"	)
	if "norm2" in name:
		lowerCamelCase_  				=						name.replace("norm2"      ,       "layernorm_after"	)
	if "mlp.fc1" in name:
		lowerCamelCase_  				=						name.replace("mlp.fc1"      ,       "intermediate.dense"	)
	if "mlp.fc2" in name:
		lowerCamelCase_  				=						name.replace("mlp.fc2"      ,       "output.dense"	)
	if "q_bias" in name:
		lowerCamelCase_  				=						name.replace("q_bias"      ,       "query.bias"	)
	if "k_bias" in name:
		lowerCamelCase_  				=						name.replace("k_bias"      ,       "key.bias"	)
	if "v_bias" in name:
		lowerCamelCase_  				=						name.replace("v_bias"      ,       "value.bias"	)
	if "cpb_mlp" in name:
		lowerCamelCase_  				=						name.replace("cpb_mlp"      ,       "continuous_position_bias_mlp"	)
	if name == "norm.weight":
		lowerCamelCase_  				=						"layernorm.weight"
	if name == "norm.bias":
		lowerCamelCase_  				=						"layernorm.bias"

	if "head" in name:
		lowerCamelCase_  				=						name.replace("head"      ,       "classifier"	)
	else:
		lowerCamelCase_  				=						"swinv2." + name

	return name





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		List[Any]	):
	for key in orig_state_dict.copy().keys():
		lowerCamelCase_  				=						orig_state_dict.pop(UpperCAmelCase_	)

		if "mask" in key:
			continue
		elif "qkv" in key:
			lowerCamelCase_  				=						key.split("."	)
			lowerCamelCase_  				=						int(key_split[1]	)
			lowerCamelCase_  				=						int(key_split[3]	)
			lowerCamelCase_  				=						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				lowerCamelCase_  				=						val[:dim, :]
				lowerCamelCase_  				=						val[dim : dim * 2, :]
				lowerCamelCase_  				=						val[-dim:, :]
			else:
				lowerCamelCase_  				=						val[:dim]
				lowerCamelCase_  				=						val[
				    dim : dim * 2
				]
				lowerCamelCase_  				=						val[-dim:]
		else:
			lowerCamelCase_  				=						val

	return orig_state_dict





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Optional[Any]	):
	lowerCamelCase_  				=						timm.create_model(UpperCAmelCase_      ,       pretrained=UpperCAmelCase_	)
	timm_model.eval()

	lowerCamelCase_  				=						get_swinva_config(UpperCAmelCase_	)
	lowerCamelCase_  				=						SwinvaForImageClassification(UpperCAmelCase_	)
	model.eval()

	lowerCamelCase_  				=						convert_state_dict(timm_model.state_dict()      ,       UpperCAmelCase_	)
	model.load_state_dict(UpperCAmelCase_	)

	lowerCamelCase_  				=						"http://images.cocodataset.org/val2017/000000039769.jpg"

	lowerCamelCase_  				=						AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"      ,       "-"	)	)	)
	lowerCamelCase_  				=						Image.open(requests.get(UpperCAmelCase_      ,       stream=UpperCAmelCase_	).raw	)
	lowerCamelCase_  				=						image_processor(images=UpperCAmelCase_      ,       return_tensors="pt"	)

	lowerCamelCase_  				=						timm_model(inputs["pixel_values"]	)
	lowerCamelCase_  				=						model(**UpperCAmelCase_	).logits

	assert torch.allclose(UpperCAmelCase_      ,       UpperCAmelCase_      ,       atol=1E-3	)

	print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''	)
	model.save_pretrained(UpperCAmelCase_	)

	print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
	image_processor.save_pretrained(UpperCAmelCase_	)

	model.push_to_hub(
	    repo_path_or_name=Path(UpperCAmelCase_      ,       UpperCAmelCase_	)      ,       organization="nandwalritik"      ,       commit_message="Add model"      ,       )


if __name__ == "__main__":
					a_						:       Dict      			=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--swinv2_name""",
					    default="""swinv2_tiny_patch4_window8_256""",
					    type=str,
					    help="""Name of the Swinv2 timm model you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)

					a_						:       Optional[Any]      			=				parser.parse_args()
					convert_swinva_checkpoint(args.swinva_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 __snake_case      (   UpperCAmelCase_		:		dict	):
 return (data["data"], data["target"])





def __snake_case      (   UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		np.ndarray	):
 lowerCamelCase_  				=						XGBClassifier()
 classifier.fit(UpperCAmelCase_      ,       UpperCAmelCase_	)
 return classifier





def __snake_case      (   ):
 lowerCamelCase_  				=						load_iris()
 lowerCamelCase_				,lowerCamelCase_  				=						data_handling(UpperCAmelCase_	)
 lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						train_test_split(
     UpperCAmelCase_      ,       UpperCAmelCase_      ,       test_size=0.25	)

 lowerCamelCase_  				=						iris["target_names"]

 # Create an XGBoost Classifier from the training data
 lowerCamelCase_  				=						xgboost(UpperCAmelCase_      ,       UpperCAmelCase_	)

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


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()



