

'''simple docstring'''





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase = None    ,__UpperCamelCase = None    ,__UpperCamelCase = None    ,)       ->			Optional[Any]:
					if config_name_or_path is None:
										lowerCamelCase_  				=      'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

					if generator_tokenizer_name_or_path is None:
										lowerCamelCase_  				=      generator_name_or_path

					if question_encoder_tokenizer_name_or_path is None:
										lowerCamelCase_  				=      question_encoder_name_or_path

					lowerCamelCase_  				=      RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

					# Save model.
					lowerCamelCase_  				=      RagConfig.from_pretrained(__UpperCamelCase	)
					lowerCamelCase_  				=      AutoConfig.from_pretrained(__UpperCamelCase	)
					lowerCamelCase_  				=      AutoConfig.from_pretrained(__UpperCamelCase	)

					lowerCamelCase_  				=      gen_config
					lowerCamelCase_  				=      question_encoder_config

					lowerCamelCase_  				=      model_class.from_pretrained_question_encoder_generator(
					    __UpperCamelCase    ,__UpperCamelCase    ,config=__UpperCamelCase	)
					rag_model.save_pretrained(__UpperCamelCase	)

					# Sanity check.
					model_class.from_pretrained(__UpperCamelCase	)

					# Save tokenizers.
					lowerCamelCase_  				=      AutoTokenizer.from_pretrained(__UpperCamelCase	)
					gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'	)
					lowerCamelCase_  				=      AutoTokenizer.from_pretrained(__UpperCamelCase	)
					question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'	)


if __name__ == "__main__":
				A_	= argparse.ArgumentParser()
				parser.add_argument(
				    "--model_type",
				    choices=["rag_sequence", "rag_token"],
				    required=True,
				    type=str,
				    help="RAG model type: rag_sequence, rag_token",
				)
				parser.add_argument("--dest", type=str, required=True, help="Path to the output checkpoint directory.")
				parser.add_argument("--generator_name_or_path", type=str, required=True, help="Generator model identifier")
				parser.add_argument(
				    "--question_encoder_name_or_path", type=str, required=True, help="Question encoder model identifier"
				)

				parser.add_argument(
				    "--generator_tokenizer_name_or_path",
				    type=str,
				    help="Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``",
				)
				parser.add_argument(
				    "--question_encoder_tokenizer_name_or_path",
				    type=str,
				    help="Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``",
				)
				parser.add_argument(
				    "--config_name_or_path",
				    type=str,
				    help=(
				        "Identifier of the model config to use, if not provided, resolves to a base config for a given"
				        " ``model_type``"
				    ),
				)

				A_	= parser.parse_args()

				A_	= Path(args.dest)
				dest_dir.mkdir(exist_ok=True)

				consolidate(
				    args.model_type,
				    args.generator_name_or_path,
				    args.question_encoder_name_or_path,
				    dest_dir,
				    args.config_name_or_path,
				    args.generator_tokenizer_name_or_path,
				    args.question_encoder_tokenizer_name_or_path,
				)





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      :							Tuple     	=    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=1_28, 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      :							Optional[int]     	=    tf.keras.preprocessing.image.ImageDataGenerator(
						    rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
						)

						_snake_case      :							Any     	=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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

						_snake_case      :							Dict     	=    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      :							Any     	=    tf.keras.preprocessing.image.img_to_array(test_image)
						_snake_case      :							int     	=    np.expand_dims(test_image, axis=0)
						_snake_case      :							Optional[Any]     	=    classifier.predict(test_image)
						# training_set.class_indices
						if result[0][0] == 0:
												_snake_case      :							str     	=    "Normal"
						if result[0][0] == 1:
												_snake_case      :							str     	=    "Abnormality detected"
