





'''simple docstring'''


from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase_   :   Optional[Any]       =					logging.get_logger(__name__)

lowerCAmelCase_   :   Optional[Any]       =					{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			List[Any]								=						"mctct"







	def __init__(				self			:   Union[str, Any] , lowerCamelCase			:   str=8_0_6_5 , lowerCamelCase			:   Optional[int]=1_5_3_6 , lowerCamelCase			:   int=3_6 , lowerCamelCase			:   int=6_1_4_4 , lowerCamelCase			:   Dict=4 , lowerCamelCase			:   Any=3_8_4 , lowerCamelCase			:   str=9_2_0 , lowerCamelCase			:   Optional[Any]=1e-5 , lowerCamelCase			:   Any=0.3 , lowerCamelCase			:   Any="relu" , lowerCamelCase			:   str=0.02 , lowerCamelCase			:   int=0.3 , lowerCamelCase			:   str=0.3 , lowerCamelCase			:   Any=1 , lowerCamelCase			:   Optional[int]=0 , lowerCamelCase			:   str=2 , lowerCamelCase			:   Optional[Any]=1 , lowerCamelCase			:   Union[str, Any]=0.3 , lowerCamelCase			:   Tuple=1 , lowerCamelCase			:   List[Any]=(7,) , lowerCamelCase			:   int=(3,) , lowerCamelCase			:   Any=8_0 , lowerCamelCase			:   Any=1 , lowerCamelCase			:   Tuple=None , lowerCamelCase			:   Optional[Any]="sum" , lowerCamelCase			:   Tuple=False , **lowerCamelCase			:   List[Any] , ):




				'''simple docstring'''
				super().__init__(**lowerCamelCase , pad_token_id=lowerCamelCase , bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase		)
				a__     = vocab_size
				a__     = hidden_size
				a__     = num_hidden_layers
				a__     = intermediate_size
				a__     = num_attention_heads
				a__     = attention_head_dim
				a__     = max_position_embeddings
				a__     = layer_norm_eps
				a__     = layerdrop
				a__     = hidden_act
				a__     = initializer_range
				a__     = hidden_dropout_prob
				a__     = attention_probs_dropout_prob
				a__     = pad_token_id
				a__     = bos_token_id
				a__     = eos_token_id
				a__     = conv_glu_dim
				a__     = conv_dropout
				a__     = num_conv_layers
				a__     = input_feat_per_channel
				a__     = input_channels
				a__     = conv_channels
				a__     = ctc_loss_reduction
				a__     = ctc_zero_infinity

				# prevents config testing fail with exporting to json
				a__     = list(lowerCamelCase		)
				a__     = list(lowerCamelCase		)

				if len(self.conv_kernel		) != self.num_conv_layers:
							raise ValueError(
							    "Configuration for convolutional module is incorrect. "
							    "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
							    F'''but is `len(config.conv_kernel) = {len(self.conv_kernel		)}`, '''
							    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''		)










'''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"



