code: 
  
  
  '''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=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)
  
  	__lowercase			:		Any =			parser.parse_known_args()
  
  	# overwrite batch size until we have tf_glue.py
  	__lowercase			:		Optional[Any] =			16
  	__lowercase			:		int =			16
  
  	# Set up logging
  	__lowercase			:		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
  	__lowercase			:		List[Any] =			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  	__lowercase			:		Dict =			AutoTokenizer.from_pretrained(args.model_name_or_path)
  
  	# Load dataset
  	__lowercase			:		List[str] =			load_dataset('imdb', split=['train', 'test'])
  	__lowercase			:		int =			train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
  	__lowercase			:		Union[str, Any] =			test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500
  
  	# Preprocess train dataset
  	__lowercase			:		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			:		List[str] =			{
  	    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
  	    for x in ['input_ids', 'attention_mask']
  	}
  	__lowercase			:		Any =			tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
  	    args.per_device_train_batch_size
  	)
  
  	# Preprocess test dataset
  	__lowercase			:		Optional[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'])
  
  	__lowercase			:		Any =			{
  	    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
  	    for x in ['input_ids', 'attention_mask']
  	}
  	__lowercase			:		str =			tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
  	    args.per_device_eval_batch_size
  	)
  
  	# fine optimizer and loss
  	__lowercase			:		str =			tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  	__lowercase			:		str =			tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  	__lowercase			:		str =			[tf.keras.metrics.SparseCategoricalAccuracy()]
  	model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  
  	__lowercase			:		Union[str, Any] =			time.time()
  	__lowercase			:		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}''')
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	A_			    =					(KDPMaAncestralDiscreteScheduler,)
  	A_			    =					10
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						{
  						    'num_train_timesteps': 1100,
  						    'beta_start': 0.0001,
  						    'beta_end': 0.02,
  						    'beta_schedule': 'linear',
  						}
  
  						config.update(**__a							)
  						return config
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for timesteps in [10, 50, 100, 1000]:
  											self.check_over_configs(num_train_timesteps=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,       [0.0002, 0.002, 0.02]							):
  											self.check_over_configs(beta_start=__a		,       beta_end=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for schedule in ["linear", "scaled_linear"]:
  											self.check_over_configs(beta_schedule=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : Dict      					=						self.scheduler_classes[0]
  						__a    : Tuple      					=						self.get_scheduler_config()
  						__a    : Optional[int]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps							)
  
  						__a    : int      					=						torch.manual_seed(0							)
  
  						__a    : Dict      					=						self.dummy_model()
  						__a    : List[Any]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
  						__a    : int      					=						sample.to(__a							)
  
  						for i, t in enumerate(scheduler.timesteps							):
  											__a    : Any      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : int      					=						model(__a		,       __a							)
  
  											__a    : Dict      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : int      					=						output.prev_sample
  
  						__a    : Tuple      					=						torch.sum(torch.abs(__a							)							)
  						__a    : List[str]      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 13849.3877							) < 1E-2
  						assert abs(result_mean.item() - 18.0331							) < 5E-3
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for prediction_type in ["epsilon", "v_prediction"]:
  											self.check_over_configs(prediction_type=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : Dict      					=						self.scheduler_classes[0]
  						__a    : Dict      					=						self.get_scheduler_config(prediction_type='v_prediction'							)
  						__a    : List[str]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps							)
  
  						__a    : Dict      					=						self.dummy_model()
  						__a    : List[str]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
  						__a    : List[str]      					=						sample.to(__a							)
  
  						__a    : Tuple      					=						torch.manual_seed(0							)
  
  						for i, t in enumerate(scheduler.timesteps							):
  											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : Optional[int]      					=						model(__a		,       __a							)
  
  											__a    : Union[str, Any]      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : Union[str, Any]      					=						output.prev_sample
  
  						__a    : Any      					=						torch.sum(torch.abs(__a							)							)
  						__a    : Optional[int]      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 328.9970							) < 1E-2
  						assert abs(result_mean.item() - 0.4284							) < 1E-3
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : List[str]      					=						self.scheduler_classes[0]
  						__a    : Union[str, Any]      					=						self.get_scheduler_config()
  						__a    : List[Any]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps		,       device=__a							)
  						__a    : Union[str, Any]      					=						torch.manual_seed(0							)
  
  						__a    : List[str]      					=						self.dummy_model()
  						__a    : Any      					=						self.dummy_sample_deter.to(__a							) * scheduler.init_noise_sigma
  
  						for t in scheduler.timesteps:
  											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : str      					=						model(__a		,       __a							)
  
  											__a    : str      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : Any      					=						output.prev_sample
  
  						__a    : Union[str, Any]      					=						torch.sum(torch.abs(__a							)							)
  						__a    : str      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 13849.3818							) < 1E-1
  						assert abs(result_mean.item() - 18.0331							) < 1E-3
  
  
  
  