






'''simple docstring'''

from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


UpperCamelCase      					=logging.get_logger(__name__)


if is_torch_available():
      import torch
      from torch import nn
      from torch.utils.data import Dataset

      from transformers import Trainer




      class      A     (   SCREAMING_SNAKE_CASE__    ):






       """simple docstring"""





       def __init__(				self      ,  __lowerCAmelCase = 1_01			):
             UpperCamelCase_					: Optional[int]            =				length
       def __len__(				self			):
             return self.length





       def __getitem__(				self      ,  __lowerCAmelCase			):
             return i






      class      A     :






       """simple docstring"""





       def __call__(				self      ,  __lowerCAmelCase			):
             return {"input_ids": torch.tensor(__lowerCAmelCase			), "labels": torch.tensor(__lowerCAmelCase			)}






      class      A     (   nn.Module    ):






       """simple docstring"""





       def __init__(				self			):
             super().__init__()
             # Add some (unused) params otherwise DDP will complain.
             UpperCamelCase_					: List[str]            =				nn.Linear(1_20      ,  80			)





       def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=None			):
             if labels is not None:
                   return torch.tensor(0.0      ,  device=input_ids.device			), input_ids
             else:
                   return input_ids




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 @require_torch_neuroncore
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[str]            =				F"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
       UpperCamelCase_					: Any            =				self.get_auto_remove_tmp_dir()
       UpperCamelCase_					: Optional[Any]            =				F"--output_dir {output_dir}".split()
       UpperCamelCase_					: str            =				["""torchrun"""] + distributed_args + args
       execute_subprocess_async(__lowerCAmelCase      ,  env=self.get_env()			)
       # successful return here == success - any errors would have caused an error in the sub-call






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 @require_torch_multi_gpu
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				F"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
       UpperCamelCase_					: Optional[Any]            =				self.get_auto_remove_tmp_dir()
       UpperCamelCase_					: Dict            =				F"--output_dir {output_dir}".split()
       UpperCamelCase_					: Optional[int]            =				["""torchrun"""] + distributed_args + args
       execute_subprocess_async(__lowerCAmelCase      ,  env=self.get_env()			)
       # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
      # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
      #
      # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

      UpperCamelCase      					=HfArgumentParser((TrainingArguments,))
      UpperCamelCase      					=parser.parse_args_into_dataclasses()[0]

      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
          f"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
      )

      # Essentially, what we want to verify in the distributed case is that we get all samples back,
      # in the right order. (this is crucial for prediction for instance)
      for dataset_length in [101, 40, 7]:
            UpperCamelCase      					=DummyDataset(dataset_length)



            def 				snake_case       (  a_				:			EvalPrediction						)							->					Dict:





                  """simple docstring"""

                  UpperCamelCase_					: Optional[Any]            =				list(range(len(a_						)						)						)
                  UpperCamelCase_					: str            =				p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
                  if not success and training_args.local_rank == 0:
                        logger.warning(
                            """Predictions and/or labels do not match expected results:\n  - predictions: """
                            f"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"						)
                  return {"success": success}

            UpperCamelCase      					=Trainer(
                model=DummyModel(),
                args=training_args,
                data_collator=DummyDataCollator(),
                eval_dataset=dataset,
                compute_metrics=compute_metrics,
            )
            UpperCamelCase      					=trainer.evaluate()
            logger.info(metrics)
            if metrics["eval_success"] is not True:
                  logger.error(metrics)
                  exit(1)

            UpperCamelCase      					=trainer.predict(dataset)
            logger.info(p.metrics)
            if p.metrics["test_success"] is not True:
                  logger.error(p.metrics)
                  exit(1)

            UpperCamelCase      					=2

            UpperCamelCase      					=trainer.evaluate()
            logger.info(metrics)
            if metrics["eval_success"] is not True:
                  logger.error(metrics)
                  exit(1)

            UpperCamelCase      					=trainer.predict(dataset)
            logger.info(p.metrics)
            if p.metrics["test_success"] is not True:
                  logger.error(p.metrics)
                  exit(1)


            UpperCamelCase      					=None














'''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)
      UpperCamelCase      					=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')

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

      UpperCamelCase      					=tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

      UpperCamelCase      					=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

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






