

"""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)
   _a       =							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')

   _a       =							tf.keras.preprocessing.image.ImageDataGenerator(
       rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
   )

   _a       =							tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

   _a       =							train_datagen.flow_from_directory(
       'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
   )

   _a       =							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

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







"""simple docstring"""





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class 					_lowerCAmelCase  (	pl.LightningModule   ):







 """simple docstring"""







 def __init__(			self							:			Optional[Any],					UpperCAmelCase__							:			str	):
    super().__init__()
    __lowercase								=						model
    __lowercase								=						2
    __lowercase								=						nn.Linear(self.model.config.hidden_size,					self.num_labels	)



 def 					_lowercase (			self							:			Optional[int]	):
    pass







def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						str:
   '''simple docstring'''


   __lowercase								=						LongformerModel.from_pretrained(UpperCamelCase_)
   __lowercase								=						LightningModel(UpperCamelCase_)

   __lowercase								=						torch.load(UpperCamelCase_,			map_location=torch.device("cpu"))
   lightning_model.load_state_dict(ckpt["state_dict"])

   # init longformer question answering model
   __lowercase								=						LongformerForQuestionAnswering.from_pretrained(UpperCamelCase_)

   # transfer weights
   longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
   longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
   longformer_for_qa.eval()

   # save model
   longformer_for_qa.save_pretrained(UpperCamelCase_)

   print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""")


if __name__ == "__main__":
   _a       =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--longformer_model',
       default=None,
       type=str,
       required=True,
       help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
   )
   parser.add_argument(
       '--longformer_question_answering_ckpt_path',
       default=None,
       type=str,
       required=True,
       help='Path the official PyTorch Lightning Checkpoint.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _a       =							parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )




