
'''simple docstring'''







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():
						_snake_case					: List[str]     			=					True
						_snake_case					: Dict     			=					"""p3dn.24xlarge"""
						import smdistributed.dataparallel.tensorflow as sdp
else:
						_snake_case					: Union[str, Any]     			=					False



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: str						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[Any]=None	):
						_SCREAMING_SNAKE_CASE					=			tqdm(_SCREAMING_SNAKE_CASE	)
						for i, batch in enumerate(_SCREAMING_SNAKE_CASE	):
												with tf.GradientTape() as tape:
																		_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			batch
																		_SCREAMING_SNAKE_CASE					=			model(_SCREAMING_SNAKE_CASE	)
																		_SCREAMING_SNAKE_CASE					=			loss(_SCREAMING_SNAKE_CASE						,      outputs.logits	)

												if SDP_ENABLED:
																		_SCREAMING_SNAKE_CASE					=			sdp.DistributedGradientTape(_SCREAMING_SNAKE_CASE						,      sparse_as_dense=_SCREAMING_SNAKE_CASE	)

												_SCREAMING_SNAKE_CASE					=			tape.gradient(_SCREAMING_SNAKE_CASE						,      model.trainable_variables	)
												opt.apply_gradients(zip(_SCREAMING_SNAKE_CASE						,      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		(						_SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: int	):
						# Load dataset
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			load_dataset("imdb"						,      split=["train", "test"]	)

						# Preprocess train dataset
						_SCREAMING_SNAKE_CASE					=			train_dataset.map(
						    lambda _SCREAMING_SNAKE_CASE	: tokenizer(e["text"]						,      truncation=_SCREAMING_SNAKE_CASE						,      padding="max_length"	)						,      batched=_SCREAMING_SNAKE_CASE	)
						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 _SCREAMING_SNAKE_CASE	: tokenizer(e["text"]						,      truncation=_SCREAMING_SNAKE_CASE						,      padding="max_length"	)						,      batched=_SCREAMING_SNAKE_CASE	)
						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(_SCREAMING_SNAKE_CASE						,      drop_remainder=_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			tf_test_dataset.batch(_SCREAMING_SNAKE_CASE						,      drop_remainder=_SCREAMING_SNAKE_CASE	)

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
						_snake_case					: List[str]     			=					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"""])

						_snake_case			,		_snake_case					: Tuple     			=					parser.parse_known_args()

						# Set up logging
						_snake_case					: Optional[Any]     			=					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()

												_snake_case					: Optional[Any]     			=					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
						_snake_case					: Dict     			=					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_snake_case					: List[Any]     			=					AutoTokenizer.from_pretrained(args.model_name_or_path)

						# get datasets
						_snake_case			,		_snake_case					: List[Any]     			=					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
						_snake_case					: Optional[Any]     			=					tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
						_snake_case					: Optional[Any]     			=					tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
						_snake_case					: Optional[int]     			=					[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)
												_snake_case					: Optional[Any]     			=					time.time()
												_snake_case					: Union[str, Any]     			=					fit(
												    model,
												    loss,
												    optimizer,
												    tf_train_dataset,
												    args.epochs,
												    args.per_device_train_batch_size,
												    max_steps=args.max_steps,
												)
												_snake_case					: Union[str, Any]     			=					time.time() - start_train_time
												logger.info("""*** Train ***""")
												logger.info(F"train_runtime = {end_train_time}")

												_snake_case					: Dict     			=					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):
												_snake_case					: Optional[int]     			=					model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
												logger.info("""*** Evaluate ***""")

												_snake_case					: Union[str, Any]     			=					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)

'''simple docstring'''







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_snake_case					: Union[str, Any]     			=					180



def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			k_size // 2
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						_SCREAMING_SNAKE_CASE					=			(
						    1
						    / (2 * np.pi * sigma)
						    * np.exp(-(np.square(_SCREAMING_SNAKE_CASE	) + np.square(_SCREAMING_SNAKE_CASE	)) / (2 * np.square(_SCREAMING_SNAKE_CASE	))	)
						)
						return g



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			np.zeros(_SCREAMING_SNAKE_CASE	)

						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		_SCREAMING_SNAKE_CASE					=			gradient_direction[row, col]

																		if (
																		    0 <= direction < PI / 8
																		    or 15 * PI / 8 <= direction <= 2 * PI
																		    or 7 * PI / 8 <= direction <= 9 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col + 1]
																								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    PI / 8 <= direction < 3 * PI / 8
																		    or 9 * PI / 8 <= direction < 11 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col + 1]
																								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    3 * PI / 8 <= direction < 5 * PI / 8
																		    or 11 * PI / 8 <= direction < 13 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col]
																								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    5 * PI / 8 <= direction < 7 * PI / 8
																		    or 13 * PI / 8 <= direction < 15 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col + 1]
																								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

						return destination



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		if destination[row, col] >= threshold_high:
																								_SCREAMING_SNAKE_CASE					=			strong
																		elif destination[row, col] <= threshold_low:
																								_SCREAMING_SNAKE_CASE					=			0
																		else:
																								_SCREAMING_SNAKE_CASE					=			weak



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Any	):
						for row in range(1						,      image_shape[0]	):
												for col in range(1						,      image_shape[1]	):
																		if destination[row, col] == weak:
																								if 255 in (
																								    destination[row, col + 1],
																								    destination[row, col - 1],
																								    destination[row - 1, col],
																								    destination[row + 1, col],
																								    destination[row - 1, col - 1],
																								    destination[row + 1, col - 1],
																								    destination[row - 1, col + 1],
																								    destination[row + 1, col + 1],
																								):
																														_SCREAMING_SNAKE_CASE					=			strong
																								else:
																														_SCREAMING_SNAKE_CASE					=			0



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: List[str]=15						,      _SCREAMING_SNAKE_CASE		: List[Any]=30						,      _SCREAMING_SNAKE_CASE		: Optional[int]=128						,      _SCREAMING_SNAKE_CASE		: List[Any]=255	):
						# gaussian_filter
						_SCREAMING_SNAKE_CASE					=			img_convolve(_SCREAMING_SNAKE_CASE						,      gen_gaussian_kernel(9						,      sigma=1.4	)	)
						# get the gradient and degree by sobel_filter
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			sobel_filter(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			PI + np.radadeg(_SCREAMING_SNAKE_CASE	)

						_SCREAMING_SNAKE_CASE					=			suppress_non_maximum(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						detect_high_low_threshold(
						    image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						track_edge(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						return destination


if __name__ == "__main__":
						# read original image in gray mode
						_snake_case					: Any     			=					cva.imread(R"""../image_data/lena.jpg""", 0)
						# canny edge detection
						_snake_case					: int     			=					canny(lena)
						cva.imshow("""canny""", canny_destination)
						cva.waitKey(0)