



import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase: Optional[int]      			=		logging.get_logger(__name__)

_lowercase: List[Any]      			=		{
    "b0": efficientnet.EfficientNetBa,
    "b1": efficientnet.EfficientNetBa,
    "b2": efficientnet.EfficientNetBa,
    "b3": efficientnet.EfficientNetBa,
    "b4": efficientnet.EfficientNetBa,
    "b5": efficientnet.EfficientNetBa,
    "b6": efficientnet.EfficientNetBa,
    "b7": efficientnet.EfficientNetBa,
}

_lowercase: Optional[int]      			=		{
    "b0": {
        "hidden_dim": 1280,
        "width_coef": 1.0,
        "depth_coef": 1.0,
        "image_size": 224,
        "dropout_rate": 0.2,
        "dw_padding": [],
    },
    "b1": {
        "hidden_dim": 1280,
        "width_coef": 1.0,
        "depth_coef": 1.1,
        "image_size": 240,
        "dropout_rate": 0.2,
        "dw_padding": [16],
    },
    "b2": {
        "hidden_dim": 1408,
        "width_coef": 1.1,
        "depth_coef": 1.2,
        "image_size": 260,
        "dropout_rate": 0.3,
        "dw_padding": [5, 8, 16],
    },
    "b3": {
        "hidden_dim": 1536,
        "width_coef": 1.2,
        "depth_coef": 1.4,
        "image_size": 300,
        "dropout_rate": 0.3,
        "dw_padding": [5, 18],
    },
    "b4": {
        "hidden_dim": 1792,
        "width_coef": 1.4,
        "depth_coef": 1.8,
        "image_size": 380,
        "dropout_rate": 0.4,
        "dw_padding": [6],
    },
    "b5": {
        "hidden_dim": 2048,
        "width_coef": 1.6,
        "depth_coef": 2.2,
        "image_size": 456,
        "dropout_rate": 0.4,
        "dw_padding": [13, 27],
    },
    "b6": {
        "hidden_dim": 2304,
        "width_coef": 1.8,
        "depth_coef": 2.6,
        "image_size": 528,
        "dropout_rate": 0.5,
        "dw_padding": [31],
    },
    "b7": {
        "hidden_dim": 2560,
        "width_coef": 2.0,
        "depth_coef": 3.1,
        "image_size": 600,
        "dropout_rate": 0.5,
        "dw_padding": [18],
    },
}






def     a(      A       :    str       )		->       str:
					"""simple docstring"""





					a					     =   EfficientNetConfig()
					a					     =   CONFIG_MAP[model_name]["hidden_dim"]
					a					     =   CONFIG_MAP[model_name]["width_coef"]
					a					     =   CONFIG_MAP[model_name]["depth_coef"]
					a					     =   CONFIG_MAP[model_name]["image_size"]
					a					     =   CONFIG_MAP[model_name]["dropout_rate"]
					a					     =   CONFIG_MAP[model_name]["dw_padding"]

					a					     =   "huggingface/label-files"
					a					     =   "imagenet-1k-id2label.json"
					a					     =   1000
					a					     =   json.load(open(hf_hub_download(A ,     A ,     repo_type="dataset"       ) ,     "r"       )       )
					a					     =   {int(A       ): v for k, v in idalabel.items()}

					a					     =   idalabel
					a					     =   {v: k for k, v in idalabel.items()}
					return config






def     a(      )		->       Optional[int]:
					"""simple docstring"""





					a					     =   "http://images.cocodataset.org/val2017/000000039769.jpg"
					a					     =   Image.open(requests.get(A ,     stream=A       ).raw       )
					return im






def     a(      A       :    List[str]       )		->       Dict:
					"""simple docstring"""





					a					     =   CONFIG_MAP[model_name]["image_size"]
					a					     =   EfficientNetImageProcessor(
					    size={"height": size, "width": size} ,     image_mean=[0.485, 0.456, 0.406] ,     image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163] ,     do_center_crop=A ,     )
					return preprocessor






def     a(      A       :    Union[str, Any]       )		->       int:
					"""simple docstring"""





					a					     =   [v.split("_"       )[0].split("block"       )[1] for v in original_param_names if v.startswith("block"       )]
					a					     =   sorted(set(A       )       )
					a					     =   len(A       )
					a					     =   {b: str(A       ) for b, i in zip(A ,     range(A       )       )}

					a					     =   []
					rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")       )
					rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")       )
					rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")       )
					rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")       )
					rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")       )

					for b in block_names:
										a					     =   block_name_mapping[b]
										rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')       )
										rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')       )
										rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')       )
										rename_keys.append(
										    (f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')       )
										rename_keys.append(
										    (f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')       )
										rename_keys.append(
										    (f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')       )
										rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')       )
										rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')       )
										rename_keys.append(
										    (f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')       )
										rename_keys.append(
										    (f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')       )

										rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')       )
										rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')       )
										rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')       )
										rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')       )
										rename_keys.append(
										    (f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''')       )
										rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''')       )
										rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''')       )
										rename_keys.append(
										    (f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')       )
										rename_keys.append(
										    (f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')       )

					rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight")       )
					rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight")       )
					rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias")       )
					rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean")       )
					rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var")       )

					a					     =   {}
					for item in rename_keys:
										if item[0] in original_param_names:
															a					     =   "efficientnet." + item[1]

					a					     =   "classifier.weight"
					a					     =   "classifier.bias"
					return key_mapping






def     a(      A       :    Union[str, Any] ,     A       :    Optional[int] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





					for key, value in tf_params.items():
										if "normalization" in key:
															continue

										a					     =   key_mapping[key]
										if "_conv" in key and "kernel" in key:
															a					     =   torch.from_numpy(A       ).permute(3 ,     2 ,     0 ,     1       )
										elif "depthwise_kernel" in key:
															a					     =   torch.from_numpy(A       ).permute(2 ,     3 ,     0 ,     1       )
										elif "kernel" in key:
															a					     =   torch.from_numpy(np.transpose(A       )       )
										else:
															a					     =   torch.from_numpy(A       )

										# Replace HF parameters with original TF model parameters
										assert hf_params[hf_key].shape == new_hf_value.shape
										hf_params[hf_key].copy_(A       )






@torch.no_grad()
def     a(      A       :    Tuple ,     A       :    Optional[int] ,     A       :    List[Any] ,     A       :    Any       )		->       Dict:
					"""simple docstring"""





					a					     =   model_classes[model_name](
					    include_top=A ,     weights="imagenet" ,     input_tensor=A ,     input_shape=A ,     pooling=A ,     classes=1000 ,     classifier_activation="softmax" ,     )

					a					     =   original_model.trainable_variables
					a					     =   original_model.non_trainable_variables
					a					     =   {param.name: param.numpy() for param in tf_params}
					for param in tf_non_train_params:
										a					     =   param.numpy()
					a					     =   list(tf_params.keys()       )

					# Load HuggingFace model
					a					     =   get_efficientnet_config(A       )
					a					     =   EfficientNetForImageClassification(A       ).eval()
					a					     =   hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print("Converting parameters..."       )
					a					     =   rename_keys(A       )
					replace_params(A ,     A ,     A       )

					# Initialize preprocessor and preprocess input image
					a					     =   convert_image_processor(A       )
					a					     =   preprocessor(images=prepare_img() ,     return_tensors="pt"       )

					# HF model inference
					hf_model.eval()
					with torch.no_grad():
										a					     =   hf_model(**A       )
					a					     =   outputs.logits.detach().numpy()

					# Original model inference
					a					     =   False
					a					     =   CONFIG_MAP[model_name]["image_size"]
					a					     =   prepare_img().resize((image_size, image_size) ,     resample=PIL.Image.NEAREST       )
					a					     =   image.img_to_array(A       )
					a					     =   np.expand_dims(A ,     axis=0       )
					a					     =   original_model.predict(A       )

					# Check whether original and HF model outputs match  -> np.allclose
					assert np.allclose(A ,     A ,     atol=1e-3       ), "The predicted logits are not the same."
					print("Model outputs match!"       )

					if save_model:
										# Create folder to save model
										if not os.path.isdir(A       ):
															os.mkdir(A       )
										# Save converted model and image processor
										hf_model.save_pretrained(A       )
										preprocessor.save_pretrained(A       )

					if push_to_hub:
										# Push model and image processor to hub
										print(f'''Pushing converted {model_name} to the hub...'''       )
										a					     =   f'''efficientnet-{model_name}'''
										preprocessor.push_to_hub(A       )
										hf_model.push_to_hub(A       )


if __name__ == "__main__":
				_lowercase: Optional[Any]      			=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model_name",
				    default="b0",
				    type=str,
				    help="Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default="hf_model",
				    type=str,
				    help="Path to the output PyTorch model directory.",
				)
				parser.add_argument("--save_model", action="store_true", help="Save model to local")
				parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

				_lowercase: List[str]      			=		parser.parse_args()
				convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





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     a(      A       :    dict       )		->       tuple:
					"""simple docstring"""





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






def     a(      A       :    np.ndarray ,     A       :    np.ndarray       )		->       XGBClassifier:
					"""simple docstring"""





					a					     =   XGBClassifier()
					classifier.fit(A ,     A       )
					return classifier






def     a(      )		->       None:
					"""simple docstring"""





					a					     =   load_iris()
					a     ,				a					     =   data_handling(A       )
					a     ,				a     ,				a     ,				a					     =   train_test_split(
					    A ,     A ,     test_size=0.25       )

					a					     =   iris["target_names"]

					# Create an XGBoost Classifier from the training data
					a					     =   xgboost(A ,     A       )

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


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()
