


import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
			# Initialising the CNN
			# (Sequential- Building the model layer by layer)
			_snake_case					:		Optional[Any]      =    models.Sequential()

			# Step 1 - Convolution
			# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
			# (3,3) is the kernel size (filter matrix)
			classifier.add(
			    layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
			)

			# Step 2 - Pooling
			classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

			# Adding a second convolutional layer
			classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
			classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

			# Step 3 - Flattening
			classifier.add(layers.Flatten())

			# Step 4 - Full connection
			classifier.add(layers.Dense(units=128, activation="relu"))
			classifier.add(layers.Dense(units=1, activation="sigmoid"))

			# Compiling the CNN
			classifier.compile(
			    optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
			)

			# Part 2 - Fitting the CNN to the images

			# Load Trained model weights

			# from keras.models import load_model
			# regressor=load_model('cnn.h5')

			_snake_case					:		Tuple      =    tf.keras.preprocessing.image.ImageDataGenerator(
			    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
			)

			_snake_case					:		int      =    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

			_snake_case					:		Tuple      =    train_datagen.flow_from_directory(
			    "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
			)

			_snake_case					:		str      =    test_datagen.flow_from_directory(
			    "dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
			)

			classifier.fit_generator(
			    training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
			)

			classifier.save("cnn.h5")

			# Part 3 - Making new predictions

			_snake_case					:		int      =    tf.keras.preprocessing.image.load_img(
			    "dataset/single_prediction/image.png", target_size=(64, 64)
			)
			_snake_case					:		int      =    tf.keras.preprocessing.image.img_to_array(test_image)
			_snake_case					:		List[Any]      =    np.expand_dims(test_image, axis=0)
			_snake_case					:		Any      =    classifier.predict(test_image)
			# training_set.class_indices
			if result[0][0] == 0:
						_snake_case					:		Dict      =    "Normal"
			if result[0][0] == 1:
						_snake_case					:		str      =    "Abnormality detected"





import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_snake_case					:		Union[str, Any]      =    ["small", "medium", "large"]

_snake_case					:		List[Any]      =    "lm_head.decoder.weight"
_snake_case					:		Optional[Any]      =    "lm_head.weight"







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Tuple										=       torch.load(__lowerCamelCase     )
							__snake_case		:      Dict										=       d.pop(__lowerCamelCase     )
							os.makedirs(__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							torch.save(__lowerCamelCase							,     os.path.join(__lowerCamelCase							,     __lowerCamelCase     )     )


if __name__ == "__main__":
			_snake_case					:		Dict      =    argparse.ArgumentParser()
			parser.add_argument("--dialogpt_path", default=".", type=str)
			_snake_case					:		Any      =    parser.parse_args()
			for MODEL in DIALOGPT_MODELS:
						_snake_case					:		Dict      =    os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
						_snake_case					:		List[str]      =    f'''./DialoGPT-{MODEL}'''
						convert_dialogpt_checkpoint(
						    checkpoint_path,
						    pytorch_dump_folder_path,
						)

