


def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	list[int]:
				if hue < 0 or hue > 360:
								raise Exception('''hue should be between 0 and 360'''	)

				if saturation < 0 or saturation > 1:
								raise Exception('''saturation should be between 0 and 1'''	)

				if value < 0 or value > 1:
								raise Exception('''value should be between 0 and 1'''	)

				lowerCAmelCase_	: List[str]      			=			value * saturation
				lowerCAmelCase_	: str      			=			hue / 60
				lowerCAmelCase_	: int      			=			chroma * (1 - abs(hue_section % 2 - 1	))
				lowerCAmelCase_	: Any      			=			value - chroma

				if hue_section >= 0 and hue_section <= 1:
								lowerCAmelCase_	: Union[str, Any]      			=			round(255 * (chroma + match_value)	)
								lowerCAmelCase_	: List[Any]      			=			round(255 * (second_largest_component + match_value)	)
								lowerCAmelCase_	: Optional[int]      			=			round(255 * (match_value)	)
				elif hue_section > 1 and hue_section <= 2:
								lowerCAmelCase_	: Dict      			=			round(255 * (second_largest_component + match_value)	)
								lowerCAmelCase_	: Tuple      			=			round(255 * (chroma + match_value)	)
								lowerCAmelCase_	: Tuple      			=			round(255 * (match_value)	)
				elif hue_section > 2 and hue_section <= 3:
								lowerCAmelCase_	: List[Any]      			=			round(255 * (match_value)	)
								lowerCAmelCase_	: Optional[int]      			=			round(255 * (chroma + match_value)	)
								lowerCAmelCase_	: List[Any]      			=			round(255 * (second_largest_component + match_value)	)
				elif hue_section > 3 and hue_section <= 4:
								lowerCAmelCase_	: Optional[int]      			=			round(255 * (match_value)	)
								lowerCAmelCase_	: Any      			=			round(255 * (second_largest_component + match_value)	)
								lowerCAmelCase_	: int      			=			round(255 * (chroma + match_value)	)
				elif hue_section > 4 and hue_section <= 5:
								lowerCAmelCase_	: int      			=			round(255 * (second_largest_component + match_value)	)
								lowerCAmelCase_	: Dict      			=			round(255 * (match_value)	)
								lowerCAmelCase_	: List[Any]      			=			round(255 * (chroma + match_value)	)
				else:
								lowerCAmelCase_	: Optional[Any]      			=			round(255 * (chroma + match_value)	)
								lowerCAmelCase_	: Tuple      			=			round(255 * (match_value)	)
								lowerCAmelCase_	: Union[str, Any]      			=			round(255 * (second_largest_component + match_value)	)

				return [red, green, blue]



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	list[float]:
				if red < 0 or red > 255:
								raise Exception('''red should be between 0 and 255'''	)

				if green < 0 or green > 255:
								raise Exception('''green should be between 0 and 255'''	)

				if blue < 0 or blue > 255:
								raise Exception('''blue should be between 0 and 255'''	)

				lowerCAmelCase_	: List[Any]      			=			red / 255
				lowerCAmelCase_	: int      			=			green / 255
				lowerCAmelCase_	: Tuple      			=			blue / 255
				lowerCAmelCase_	: List[Any]      			=			max(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)
				lowerCAmelCase_	: List[Any]      			=			value - min(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)
				lowerCAmelCase_	: Union[str, Any]      			=			0 if value == 0 else chroma / value

				if chroma == 0:
								lowerCAmelCase_	: Optional[Any]      			=			0.0
				elif value == float_red:
								lowerCAmelCase_	: List[Any]      			=			60 * (0 + (float_green - float_blue) / chroma)
				elif value == float_green:
								lowerCAmelCase_	: Tuple      			=			60 * (2 + (float_blue - float_red) / chroma)
				else:
								lowerCAmelCase_	: Any      			=			60 * (4 + (float_red - float_green) / chroma)

				lowerCAmelCase_	: str      			=			(hue + 360) % 360

				return [hue, saturation, value]




def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	bool:
				lowerCAmelCase_	: Tuple      			=			abs(hsv_a[0] - hsv_a[0]	) < 0.2
				lowerCAmelCase_	: List[str]      			=			abs(hsv_a[1] - hsv_a[1]	) < 0.002
				lowerCAmelCase_	: Tuple      			=			abs(hsv_a[2] - hsv_a[2]	) < 0.002

				return check_hue and check_saturation and check_value



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__":
						_UpperCAmelCase     :					Optional[int]       =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=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)

						_UpperCAmelCase       ,   _UpperCAmelCase     :					List[str]       =parser.parse_known_args()

						# overwrite batch size until we have tf_glue.py
						_UpperCAmelCase     :					Optional[Any]       =16
						_UpperCAmelCase     :					List[str]       =16

						# Set up logging
						_UpperCAmelCase     :					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""",
						)

						# Load model and tokenizer
						_UpperCAmelCase     :					List[str]       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_UpperCAmelCase     :					Any       =AutoTokenizer.from_pretrained(args.model_name_or_path)

						# Load dataset
						_UpperCAmelCase       ,   _UpperCAmelCase     :					Union[str, Any]       =load_dataset("""imdb""", split=["""train""", """test"""])
						_UpperCAmelCase     :					int       =train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
						_UpperCAmelCase     :					str       =test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

						# Preprocess train dataset
						_UpperCAmelCase     :					str       =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"""])

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

						# Preprocess test dataset
						_UpperCAmelCase     :					List[Any]       =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"""])

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

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

						_UpperCAmelCase     :					Tuple       =time.time()
						_UpperCAmelCase     :					List[str]       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						_UpperCAmelCase     :					Optional[int]       =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}""")