

import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled():
  A__										=      True
  A__										=      "p3dn.24xlarge"
  import smdistributed.dataparallel.tensorflow as sdp
else:
  A__										=      False





def 				__a							(       __A			,							__A			,							__A			,							__A			,							__A			,							__A			,							__A=None						):
    SCREAMING_SNAKE_CASE__         =    tqdm(__A						)
    for i, batch in enumerate(__A						):
        with tf.GradientTape() as tape:
            SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    batch
            SCREAMING_SNAKE_CASE__         =    model(__A						)
            SCREAMING_SNAKE_CASE__         =    loss(__A			,							outputs.logits						)

        if SDP_ENABLED:
            SCREAMING_SNAKE_CASE__         =    sdp.DistributedGradientTape(__A			,							sparse_as_dense=__A						)

        SCREAMING_SNAKE_CASE__         =    tape.gradient(__A			,							model.trainable_variables						)
        opt.apply_gradients(zip(__A			,							model.trainable_variables						)						)

        pbar.set_description(f"Loss: {loss_value:.4f}"						)

        if SDP_ENABLED and i == 0:
            sdp.broadcast_variables(model.variables			,							root_rank=0						)
            sdp.broadcast_variables(opt.variables()			,							root_rank=0						)

        if max_steps and i >= max_steps:
            break

    SCREAMING_SNAKE_CASE__         =    {"loss": loss_value.numpy()}
    return train_results





def 				__a							(       __A			,							__A			,							__A						):
    # Load dataset
    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    load_dataset("imdb"			,							split=["train", "test"]						)

    # Preprocess train dataset
    SCREAMING_SNAKE_CASE__         =    train_dataset.map(
        lambda __A						: tokenizer(e["text"]			,							truncation=__A			,							padding="max_length"						)			,							batched=__A						)
    train_dataset.set_format(type="tensorflow"			,							columns=["input_ids", "attention_mask", "label"]						)

    SCREAMING_SNAKE_CASE__         =    {
        x: train_dataset[x].to_tensor(default_value=0			,							shape=[None, tokenizer.model_max_length]						)
        for x in ["input_ids", "attention_mask"]
    }
    SCREAMING_SNAKE_CASE__         =    tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])						)

    # Preprocess test dataset
    SCREAMING_SNAKE_CASE__         =    test_dataset.map(
        lambda __A						: tokenizer(e["text"]			,							truncation=__A			,							padding="max_length"						)			,							batched=__A						)
    test_dataset.set_format(type="tensorflow"			,							columns=["input_ids", "attention_mask", "label"]						)

    SCREAMING_SNAKE_CASE__         =    {
        x: test_dataset[x].to_tensor(default_value=0			,							shape=[None, tokenizer.model_max_length]						)
        for x in ["input_ids", "attention_mask"]
    }
    SCREAMING_SNAKE_CASE__         =    tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])						)

    if SDP_ENABLED:
        SCREAMING_SNAKE_CASE__         =    tf_train_dataset.shard(sdp.size()			,							sdp.rank()						)
        SCREAMING_SNAKE_CASE__         =    tf_test_dataset.shard(sdp.size()			,							sdp.rank()						)
    SCREAMING_SNAKE_CASE__         =    tf_train_dataset.batch(__A			,							drop_remainder=__A						)
    SCREAMING_SNAKE_CASE__         =    tf_test_dataset.batch(__A			,							drop_remainder=__A						)

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
  A__										=      argparse.ArgumentParser()

  # Hyperparameters sent by the client are passed as command-line arguments to the script.
  parser.add_argument("--epochs", type=int, default=3)
  parser.add_argument("--per_device_train_batch_size", type=int, default=16)
  parser.add_argument("--per_device_eval_batch_size", type=int, default=8)
  parser.add_argument("--model_name_or_path", type=str)
  parser.add_argument("--learning_rate", type=str, default=5E-5)
  parser.add_argument("--do_train", type=bool, default=True)
  parser.add_argument("--do_eval", type=bool, default=True)
  parser.add_argument("--output_dir", type=str)
  parser.add_argument("--max_steps", type=int, default=None)

  # Data, model, and output directories
  parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
  parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
  parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])

  A__							,     A__										=      parser.parse_known_args()

  # Set up logging
  A__										=      logging.getLogger(__name__)

  logging.basicConfig(
      level=logging.getLevelName("INFO"),
      handlers=[logging.StreamHandler(sys.stdout)],
      format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
  )

  if SDP_ENABLED:
    sdp.init()

    A__										=      tf.config.experimental.list_physical_devices("GPU")
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
    if gpus:
      tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU")

    # Load model and tokenizer
  A__										=      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  A__										=      AutoTokenizer.from_pretrained(args.model_name_or_path)

  # get datasets
  A__							,     A__										=      get_datasets(
      tokenizer=tokenizer,
      train_batch_size=args.per_device_train_batch_size,
      eval_batch_size=args.per_device_eval_batch_size,
  )

  # fine optimizer and loss
  A__										=      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  A__										=      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  A__										=      [tf.keras.metrics.SparseCategoricalAccuracy()]
  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

  # Training
  if args.do_train:
    # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
    A__										=      time.time()
    A__										=      fit(
        model,
        loss,
        optimizer,
        tf_train_dataset,
        args.epochs,
        args.per_device_train_batch_size,
        max_steps=args.max_steps,
    )
    A__										=      time.time() - start_train_time
    logger.info("*** Train ***")
    logger.info(f'''train_runtime = {end_train_time}''')

    A__										=      os.path.join(args.output_dir, "train_results.txt")

    if not SDP_ENABLED or sdp.rank() == 0:
      with open(output_eval_file, "w") as writer:
        logger.info("***** Train results *****")
        logger.info(train_results)
        for key, value in train_results.items():
          logger.info(f'''  {key} = {value}''')
          writer.write(f'''{key} = {value}\n''')

    # Evaluation
  if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
    A__										=      model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
    logger.info("*** Evaluate ***")

    A__										=      os.path.join(args.output_dir, "eval_results.txt")

    with open(output_eval_file, "w") as writer:
      logger.info("***** Eval results *****")
      logger.info(result)
      for key, value in result.items():
        logger.info(f'''  {key} = {value}''')
        writer.write(f'''{key} = {value}\n''')

    # Save result
  if SDP_ENABLED:
    if sdp.rank() == 0:
      model.save_pretrained(args.output_dir)
      tokenizer.save_pretrained(args.output_dir)
  else:
    model.save_pretrained(args.output_dir)
    tokenizer.save_pretrained(args.output_dir)





from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    np.stack((horizontal_flow, vertical_flow)			,							2						)

    # Create a grid of all pixel coordinates and subtract the flow to get the
    # target pixels coordinates
    SCREAMING_SNAKE_CASE__         =    np.stack(
        np.meshgrid(np.arange(0			,							image.shape[1]						)			,							np.arange(0			,							image.shape[0]						)						)			,							2						)
    SCREAMING_SNAKE_CASE__         =    np.round(grid - flow						).astype(np.intaa						)

    # Find the locations outside of the original image
    SCREAMING_SNAKE_CASE__         =    (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]						))
    SCREAMING_SNAKE_CASE__         =    0

    SCREAMING_SNAKE_CASE__         =    image[grid[:, :, 1], grid[:, :, 0]]

    # Set pixels at invalid locations to 0
    SCREAMING_SNAKE_CASE__         =    0

    return warped





def 				__a							(       __A			,							__A			,							__A			,							__A = None			,							):
    if alpha is None:
        SCREAMING_SNAKE_CASE__         =    0.1

    # Initialize flow
    SCREAMING_SNAKE_CASE__         =    np.zeros_like(__A						)
    SCREAMING_SNAKE_CASE__         =    np.zeros_like(__A						)

    # Prepare kernels for the calculation of the derivatives and the average velocity
    SCREAMING_SNAKE_CASE__         =    np.array([[-1, 1], [-1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array([[-1, -1], [1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array([[1, 1], [1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array(
        [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]						)

    # Iteratively refine the flow
    for _ in range(__A						):
        SCREAMING_SNAKE_CASE__         =    warp(__A			,							__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							-kernel_t						)

        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						)

        # This updates the flow as proposed in the paper (Step 12)
        SCREAMING_SNAKE_CASE__         =    (
            derivative_x * avg_horizontal_velocity
            + derivative_y * avg_vertical_velocity
            + derivative_t
        )
        SCREAMING_SNAKE_CASE__         =    update / (alpha**2 + derivative_x**2 + derivative_y**2)

        SCREAMING_SNAKE_CASE__         =    avg_horizontal_velocity - derivative_x * update
        SCREAMING_SNAKE_CASE__         =    avg_vertical_velocity - derivative_y * update

    return horizontal_flow, vertical_flow


if __name__ == "__main__":
  import doctest

  doctest.testmod()


