



"""simple docstring"""






import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
		_lowercase					:					Optional[Any] 		=		argparse.ArgumentParser()

		# Hyperparameters sent by the client are passed as command-line arguments to the script.
		parser.add_argument("--epochs", type=int, default=1)
		parser.add_argument("--per_device_train_batch_size", type=int, default=1_6)
		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)

		_lowercase		,      _lowercase					:					Tuple 		=		parser.parse_known_args()

		# overwrite batch size until we have tf_glue.py
		_lowercase					:					Optional[int] 		=		1_6
		_lowercase					:					int 		=		1_6

		# Set up logging
		_lowercase					:					int 		=		logging.getLogger(__name__)

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

		# Load model and tokenizer
		_lowercase					:					Any 		=		TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
		_lowercase					:					Dict 		=		AutoTokenizer.from_pretrained(args.model_name_or_path)

		# Load dataset
		_lowercase		,      _lowercase					:					Dict 		=		load_dataset("imdb", split=["train", "test"])
		_lowercase					:					int 		=		train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
		_lowercase					:					Union[str, Any] 		=		test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

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

		_lowercase					:					Union[str, Any] 		=		{
		    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
		    for x in ["input_ids", "attention_mask"]
		}
		_lowercase					:					Dict 		=		tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])).batch(
		    args.per_device_train_batch_size
		)

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

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

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

		_lowercase					:					Union[str, Any] 		=		time.time()
		_lowercase					:					Union[str, Any] 		=		model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
		_lowercase					:					str 		=		time.time() - start_train_time

		logger.info("*** Train ***")
		logger.info(f'train_runtime = {end_train_time}')
		for key, value in train_results.history.items():
				logger.info(f'  {key} = {value}')







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()


