
"""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
#
########################################################################

SCREAMING_SNAKE_CASE      =     16
SCREAMING_SNAKE_CASE      =     32



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_ = 16				)->							Optional[Any]:







       """simple docstring"""
       UpperCamelCase					 =		AutoTokenizer.from_pretrained("bert-base-cased"				)
       UpperCamelCase					 =		load_dataset("glue"		,      "mrpc"				)

       def tokenize_function(UpperCAmelCase_				):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase					 =		tokenizer(examples["sentence1"]		,      examples["sentence2"]		,      truncation=UpperCAmelCase_		,      max_length=UpperCAmelCase_				)
              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					 =		datasets.map(
                  UpperCAmelCase_		,      batched=UpperCAmelCase_		,      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					 =		tokenized_datasets.rename_column("label"		,      "labels"				)

       def collate_fn(UpperCAmelCase_				):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCamelCase					 =		1_28 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					 =		16
              elif accelerator.mixed_precision != "no":
                     UpperCamelCase					 =		8
              else:
                     UpperCamelCase					 =		None

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

       # Instantiate dataloaders.
       UpperCamelCase					 =		DataLoader(
           tokenized_datasets["train"]		,      shuffle=UpperCAmelCase_		,      collate_fn=UpperCAmelCase_		,      batch_size=UpperCAmelCase_				)
       UpperCamelCase					 =		DataLoader(
           tokenized_datasets["validation"]		,      shuffle=UpperCAmelCase_		,      collate_fn=UpperCAmelCase_		,      batch_size=UpperCAmelCase_				)

       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

      SCREAMING_SNAKE_CASE      =     mocked_dataloaders  # noqa: F811



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Dict:







       """simple docstring"""
       # For testing only
       if os.environ.get("TESTING_MOCKED_DATALOADERS"		,      UpperCAmelCase_				) == "1":
              UpperCamelCase					 =		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					 =		Accelerator(
                  cpu=args.cpu		,      mixed_precision=args.mixed_precision		,      log_with="all"		,      project_dir=args.project_dir				)
       else:
              UpperCamelCase					 =		Accelerator(cpu=args.cpu		,      mixed_precision=args.mixed_precision				)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCamelCase					 =		config["lr"]
       UpperCamelCase					 =		int(config["num_epochs"]				)
       UpperCamelCase					 =		int(config["seed"]				)
       UpperCamelCase					 =		int(config["batch_size"]				)
       set_seed(UpperCAmelCase_				)

       UpperCamelCase ,       UpperCamelCase					 =		get_dataloaders(UpperCAmelCase_		,      UpperCAmelCase_				)
       UpperCamelCase					 =		evaluate.load("glue"		,      "mrpc"				)

       # If the batch size is too big we use gradient accumulation
       UpperCamelCase					 =		1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              UpperCamelCase					 =		batch_size // MAX_GPU_BATCH_SIZE
              UpperCamelCase					 =		MAX_GPU_BATCH_SIZE

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCamelCase					 =		AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,      return_dict=UpperCAmelCase_				)

       # 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					 =		model.to(accelerator.device				)

       # Instantiate optimizer
       UpperCamelCase					 =		AdamW(params=model.parameters()		,      lr=UpperCAmelCase_				)

       # Instantiate scheduler
       UpperCamelCase					 =		get_linear_schedule_with_warmup(
           optimizer=UpperCAmelCase_		,      num_warmup_steps=1_00		,      num_training_steps=(len(UpperCAmelCase_				) * 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					 =		accelerator.prepare(
           UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)

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

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

              model.eval()
              for step, batch in enumerate(UpperCAmelCase_				):
                     # 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					 =		model(**UpperCAmelCase_				)
                     UpperCamelCase					 =		outputs.logits.argmax(dim=-1				)
                     UpperCamelCase ,       UpperCamelCase					 =		accelerator.gather_for_metrics((predictions, batch["labels"])				)
                     metric.add_batch(
                         predictions=UpperCAmelCase_		,      references=UpperCAmelCase_		,      )

              UpperCamelCase					 =		metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"epoch {epoch}:"		,      UpperCAmelCase_				)

              # 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(UpperCAmelCase_				),
                             "epoch": epoch,
                         }		,      step=UpperCAmelCase_		,      )

    # 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 	lowerCamelCase__			(							)->							Optional[int]:







       """simple docstring"""
       UpperCamelCase					 =		argparse.ArgumentParser(description="Simple example of training script."				)
       parser.add_argument(
           "--mixed_precision"		,      type=UpperCAmelCase_		,      default=UpperCAmelCase_		,      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=UpperCAmelCase_		,      default="logs"		,      help="Location on where to store experiment tracking logs` and relevent project information"		,      )
       UpperCamelCase					 =		parser.parse_args()
       UpperCamelCase					 =		{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(UpperCAmelCase_		,      UpperCAmelCase_				)


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)
      SCREAMING_SNAKE_CASE      =     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')

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

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

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

      SCREAMING_SNAKE_CASE      =     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

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





