






'''simple docstring'''

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCamelCase      					=16
UpperCamelCase      					=32



def 				snake_case       (  a_				:			Accelerator     , a_				:			int = 16						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				AutoTokenizer.from_pretrained("""bert-base-cased"""						)
      UpperCamelCase_					: int            =				load_dataset("""glue"""     , """mrpc"""						)

      def tokenize_function(a_				:			int						):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase_					: Any            =				tokenizer(examples["""sentence1"""]     , examples["""sentence2"""]     , truncation=a_     , max_length=a_						)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            UpperCamelCase_					: Any            =				datasets.map(
                a_     , batched=a_     , remove_columns=["""idx""", """sentence1""", """sentence2"""]     , )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      UpperCamelCase_					: List[Any]            =				tokenized_datasets.rename_column("""label"""     , """labels"""						)

      def collate_fn(a_				:			Dict						):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCamelCase_					: int            =				128 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  UpperCamelCase_					: Any            =				16
            elif accelerator.mixed_precision != "no":
                  UpperCamelCase_					: Union[str, Any]            =				8
            else:
                  UpperCamelCase_					: Dict            =				None

            return tokenizer.pad(
                a_     , padding="""longest"""     , max_length=a_     , pad_to_multiple_of=a_     , return_tensors="""pt"""     , )

      # Instantiate dataloaders.
      UpperCamelCase_					: Optional[Any]            =				DataLoader(
          tokenized_datasets["""train"""]     , shuffle=a_     , collate_fn=a_     , batch_size=a_						)
      UpperCamelCase_					: Tuple            =				DataLoader(
          tokenized_datasets["""validation"""]     , shuffle=a_     , collate_fn=a_     , batch_size=a_						)

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      UpperCamelCase      					=mocked_dataloaders  # noqa: F811



def 				snake_case       (  a_				:			Any     , a_				:			Any						)							->					str:





      """simple docstring"""

      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     , a_						) == "1":
            UpperCamelCase_					: Tuple            =				2
      # Initialize Accelerator

      # New Code #
      # We pass in "all" to `log_with` to grab all available trackers in the environment
      # Note: If using a custom `Tracker` class, should be passed in here such as:
      # >>> log_with = ["all", MyCustomTrackerClassInstance()]
      if args.with_tracking:
            UpperCamelCase_					: Tuple            =				Accelerator(
                cpu=args.cpu     , mixed_precision=args.mixed_precision     , log_with="""all"""     , project_dir=args.project_dir						)
      else:
            UpperCamelCase_					: Dict            =				Accelerator(cpu=args.cpu     , mixed_precision=args.mixed_precision						)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCamelCase_					: Tuple            =				config["""lr"""]
      UpperCamelCase_					: List[str]            =				int(config["""num_epochs"""]						)
      UpperCamelCase_					: Tuple            =				int(config["""seed"""]						)
      UpperCamelCase_					: Union[str, Any]            =				int(config["""batch_size"""]						)
      set_seed(a_						)

      UpperCamelCase_     ,      UpperCamelCase_					: Optional[int]            =				get_dataloaders(a_     , a_						)
      UpperCamelCase_					: Any            =				evaluate.load("""glue"""     , """mrpc"""						)

      # If the batch size is too big we use gradient accumulation
      UpperCamelCase_					: Union[str, Any]            =				1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            UpperCamelCase_					: Optional[Any]            =				batch_size // MAX_GPU_BATCH_SIZE
            UpperCamelCase_					: Tuple            =				MAX_GPU_BATCH_SIZE

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCamelCase_					: Optional[int]            =				AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""     , return_dict=a_						)

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      UpperCamelCase_					: str            =				model.to(accelerator.device						)

      # Instantiate optimizer
      UpperCamelCase_					: Optional[int]            =				AdamW(params=model.parameters()     , lr=a_						)

      # Instantiate scheduler
      UpperCamelCase_					: Union[str, Any]            =				get_linear_schedule_with_warmup(
          optimizer=a_     , num_warmup_steps=100     , num_training_steps=(len(a_						) * num_epochs) // gradient_accumulation_steps     , )

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: int            =				accelerator.prepare(
          a_     , a_     , a_     , a_     , a_						)

      # New Code #
      # We need to initialize the trackers we use. Overall configurations can also be stored
      if args.with_tracking:
            UpperCamelCase_					: Dict            =				os.path.split(a_						)[-1].split("""."""						)[0]
            accelerator.init_trackers(a_     , a_						)

      # Now we train the model
      for epoch in range(a_						):
            model.train()
            # New Code #
            # For our tracking example, we will log the total loss of each epoch
            if args.with_tracking:
                  UpperCamelCase_					: List[Any]            =				0
            for step, batch in enumerate(a_						):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device						)
                  UpperCamelCase_					: Dict            =				model(**a_						)
                  UpperCamelCase_					: str            =				outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  UpperCamelCase_					: Any            =				loss / gradient_accumulation_steps
                  accelerator.backward(a_						)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(a_						):
                  # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                  batch.to(accelerator.device						)
                  with torch.no_grad():
                        UpperCamelCase_					: int            =				model(**a_						)
                  UpperCamelCase_					: Dict            =				outputs.logits.argmax(dim=-1						)
                  UpperCamelCase_     ,      UpperCamelCase_					: Dict            =				accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
                  metric.add_batch(
                      predictions=a_     , references=a_     , )

            UpperCamelCase_					: List[str]            =				metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"     , a_						)

            # New Code #
            # To actually log, we call `Accelerator.log`
            # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
            if args.with_tracking:
                  accelerator.log(
                      {
                          """accuracy""": eval_metric["""accuracy"""],
                          """f1""": eval_metric["""f1"""],
                          """train_loss""": total_loss.item() / len(a_						),
                          """epoch""": epoch,
                      }     , step=a_     , )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
      if args.with_tracking:
            accelerator.end_training()



def 				snake_case       (  )							->					int:





      """simple docstring"""

      UpperCamelCase_					: Union[str, Any]            =				argparse.ArgumentParser(description="""Simple example of training script."""						)
      parser.add_argument(
          """--mixed_precision"""     , type=a_     , default=a_     , choices=["""no""", """fp16""", """bf16""", """fp8"""]     , help="""Whether to use mixed precision. Choose"""
          """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
          """and an Nvidia Ampere GPU."""     , )
      parser.add_argument("""--cpu"""     , action="""store_true"""     , help="""If passed, will train on the CPU."""						)
      parser.add_argument(
          """--with_tracking"""     , action="""store_true"""     , help="""Whether to load in all available experiment trackers from the environment and use them for logging."""     , )
      parser.add_argument(
          """--project_dir"""     , type=a_     , default="""logs"""     , help="""Location on where to store experiment tracking logs` and relevent project information"""     , )
      UpperCamelCase_					: Tuple            =				parser.parse_args()
      UpperCamelCase_					: Any            =				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(a_     , a_						)


if __name__ == "__main__":
      main()














'''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"






