



"""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__":
			A    =	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)

			A  ,  A    =	parser.parse_known_args()

			# overwrite batch size until we have tf_glue.py
			A    =	1_6
			A    =	1_6

			# 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',
			)

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

			# Load dataset
			A  ,  A    =	load_dataset('imdb', split=['train', 'test'])
			A    =	train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
			A    =	test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

			# Preprocess train dataset
			A    =	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'])

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

			# Preprocess test dataset
			A    =	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'])

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

			A    =	time.time()
			A    =	model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
			A    =	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}''')








"""simple docstring"""







import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       int    ,		lowerCamelCase_:       bool = False   ):
						"""simple docstring"""






						if device_specific:
												seed += AcceleratorState().process_index
						random.seed(lowerCamelCase_   )
						np.random.seed(lowerCamelCase_   )
						torch.manual_seed(lowerCamelCase_   )
						if is_xpu_available():
												torch.xpu.manual_seed_all(lowerCamelCase_   )
						elif is_npu_available():
												torch.npu.manual_seed_all(lowerCamelCase_   )
						else:
												torch.cuda.manual_seed_all(lowerCamelCase_   )
						# ^^ safe to call this function even if cuda is not available
						if is_tpu_available():
												xm.set_rng_state(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[RNGType] = None    ,		lowerCamelCase_:       Optional[torch.Generator] = None   ):
						"""simple docstring"""






						if rng_type == RNGType.TORCH:
												snake_case      : List[Any]							=   torch.get_rng_state()
						elif rng_type == RNGType.CUDA:
												snake_case      : Union[str, Any]							=   torch.cuda.get_rng_state()
						elif rng_type == RNGType.XLA:
												assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
												snake_case      : Optional[int]							=   torch.tensor(xm.get_rng_state()   )
						elif rng_type == RNGType.NPU:
												assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
												snake_case      : int							=   torch.npu.get_rng_state()
						elif rng_type == RNGType.XPU:
												assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
												snake_case      : int							=   torch.xpu.get_rng_state()
						elif rng_type == RNGType.GENERATOR:
												assert generator is not None, "Need a generator to synchronize its seed."
												snake_case      : List[Any]							=   generator.get_state()

						# Broadcast the rng state from device 0 to other devices
						snake_case      : Dict							=   AcceleratorState()
						if state.distributed_type == DistributedType.TPU:
												snake_case      : List[str]							=   rng_state.to(xm.xla_device()   )
												xm.collective_broadcast([rng_state]   )
												xm.mark_step()
												snake_case      : List[Any]							=   rng_state.cpu()
						elif (
						    state.distributed_type in CUDA_DISTRIBUTED_TYPES
						    or state.distributed_type == DistributedType.MULTI_NPU
						    or state.distributed_type == DistributedType.MULTI_XPU
						):
												snake_case      : str							=   rng_state.to(state.device   )
												torch.distributed.broadcast(lowerCamelCase_    ,		0   )
												snake_case      : Union[str, Any]							=   rng_state.cpu()
						elif state.distributed_type == DistributedType.MULTI_CPU:
												torch.distributed.broadcast(lowerCamelCase_    ,		0   )

						# Set the broadcast rng state
						if rng_type == RNGType.TORCH:
												torch.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.CUDA:
												torch.cuda.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.NPU:
												torch.npu.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.XPU:
												torch.xpu.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.XLA:
												xm.set_rng_state(rng_state.item()   )
						elif rng_type == RNGType.GENERATOR:
												generator.set_state(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       List[Union[str, RNGType]]    ,		lowerCamelCase_:       Optional[torch.Generator] = None   ):
						"""simple docstring"""






						for rng_type in rng_types:
												synchronize_rng_state(RNGType(lowerCamelCase_   )    ,		generator=lowerCamelCase_   )



