





'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_   :   Any       =					logging.get_logger(__name__)

lowerCAmelCase_   :   int       =					{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			List[str]								=						"roberta"







	def __init__(				self			:   Any , lowerCamelCase			:   int=5_0_2_6_5 , lowerCamelCase			:   Optional[Any]=7_6_8 , lowerCamelCase			:   List[Any]=1_2 , lowerCamelCase			:   str=1_2 , lowerCamelCase			:   Optional[int]=3_0_7_2 , lowerCamelCase			:   Union[str, Any]="gelu" , lowerCamelCase			:   int=0.1 , lowerCamelCase			:   List[str]=0.1 , lowerCamelCase			:   Tuple=5_1_2 , lowerCamelCase			:   Union[str, Any]=2 , lowerCamelCase			:   Dict=0.02 , lowerCamelCase			:   int=1e-12 , lowerCamelCase			:   Tuple=1 , lowerCamelCase			:   int=0 , lowerCamelCase			:   Optional[Any]=2 , lowerCamelCase			:   str="absolute" , lowerCamelCase			:   Optional[int]=True , lowerCamelCase			:   Optional[int]=None , **lowerCamelCase			:   str , ):




				'''simple docstring'''
				super().__init__(pad_token_id=lowerCamelCase , bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , **lowerCamelCase		)

				a__     = vocab_size
				a__     = hidden_size
				a__     = num_hidden_layers
				a__     = num_attention_heads
				a__     = hidden_act
				a__     = intermediate_size
				a__     = hidden_dropout_prob
				a__     = attention_probs_dropout_prob
				a__     = max_position_embeddings
				a__     = type_vocab_size
				a__     = initializer_range
				a__     = layer_norm_eps
				a__     = position_embedding_type
				a__     = use_cache
				a__     = classifier_dropout






class       UpperCamelCase__  (		__lowerCAmelCase     ):







	@property
	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				if self.task == "multiple-choice":
							a__     = {0: "batch", 1: "choice", 2: "sequence"}
				else:
							a__     = {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				    ]		)










'''simple docstring'''


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)
		lowerCAmelCase_   :   str       =					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')

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

		lowerCAmelCase_   :   List[Any]       =					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

		lowerCAmelCase_   :   Any       =					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

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



