


import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		Dict  ):







	'''simple docstring'''



	if isinstance(__lowerCamelCase  ,			(list, tuple)  ):
		return honor_type(__lowerCamelCase  ,			(move_to_device(__lowerCamelCase  ,			__lowerCamelCase  ) for t in state)  )
	elif isinstance(__lowerCamelCase  ,			__lowerCamelCase  ):
		return type(__lowerCamelCase  )({k: move_to_device(__lowerCamelCase  ,			__lowerCamelCase  ) for k, v in state.items()}  )
	elif isinstance(__lowerCamelCase  ,			torch.Tensor  ):
		return state.to(__lowerCamelCase  )
	return state







class     __lowerCamelCase     (  torch.optim.Optimizer     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase=True						,       UpperCAmelCase=None  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = optimizer
								lowercase_			 = scaler
								lowercase_			 = AcceleratorState()
								lowercase_			 = GradientState()
								lowercase_			 = device_placement
								lowercase_			 = False
								lowercase_			 = None

								# Handle device placement
								if device_placement:
									lowercase_			 = self.optimizer.state_dict()
									if self.accelerator_state.distributed_type == DistributedType.TPU:
										xm.send_cpu_data_to_device(UpperCAmelCase						,       self.accelerator_state.device  )
									else:
										lowercase_			 = move_to_device(UpperCAmelCase						,       self.accelerator_state.device  )
									self.optimizer.load_state_dict(UpperCAmelCase  )




							@property
							def 				A__      (  self  )     ->       Union[str, Any]:






								'''simple docstring'''






								return self.optimizer.state




							@state.setter
							def 				A__      (  self						,       UpperCAmelCase  )     ->       int:






								'''simple docstring'''






								lowercase_			 = state




							@property
							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								return self.optimizer.param_groups




							@param_groups.setter
							def 				A__      (  self						,       UpperCAmelCase  )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = param_groups




							@property
							def 				A__      (  self  )     ->       Any:






								'''simple docstring'''






								return self.optimizer.defaults




							@defaults.setter
							def 				A__      (  self						,       UpperCAmelCase  )     ->       Optional[int]:






								'''simple docstring'''






								lowercase_			 = defaults




							def 				A__      (  self						,       UpperCAmelCase  )     ->       int:






								'''simple docstring'''






								self.optimizer.add_param_group(UpperCAmelCase  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       int:






								'''simple docstring'''






								if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
									xm.send_cpu_data_to_device(UpperCAmelCase						,       self.accelerator_state.device  )
								self.optimizer.load_state_dict(UpperCAmelCase  )




							def 				A__      (  self  )     ->       List[str]:






								'''simple docstring'''






								return self.optimizer.state_dict()




							def 				A__      (  self						,       UpperCAmelCase=None  )     ->       Tuple:






								'''simple docstring'''






								if self.gradient_state.sync_gradients:
									lowercase_			 = "set_to_none" in inspect.signature(self.optimizer.zero_grad  ).parameters
									if accept_arg:
										if set_to_none is None:
											lowercase_			 = False
										self.optimizer.zero_grad(set_to_none=UpperCAmelCase  )
									else:
										if set_to_none is not None:
											raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."  )
										self.optimizer.zero_grad()




							def 				A__      (  self						,       UpperCAmelCase=None  )     ->       Any:






								'''simple docstring'''






								if self.gradient_state.sync_gradients:
									if self.accelerator_state.distributed_type == DistributedType.TPU:
										lowercase_			 = {"closure": closure} if closure is not None else {}
										xm.optimizer_step(self.optimizer						,       optimizer_args=UpperCAmelCase  )
									elif self.scaler is not None:
										lowercase_			 = False
										if self._last_scale is None:
											# `get_scale` is an async operation requiring full synchronization
											# on CPU and GPUs before finishing. As a result, we store away
											# the prior one to reduce the call overhead
											lowercase_			 = self.scaler.get_scale()
											lowercase_			 = True
										self.scaler.step(self.optimizer						,       UpperCAmelCase  )
										self.scaler.update()
										lowercase_			 = self.scaler.get_scale()
										if not new_scale:
											# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
											lowercase_			 = scale_after < self._last_scale
										lowercase_			 = scale_after

									else:
										self.optimizer.step(UpperCAmelCase  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Union[str, Any]:






								'''simple docstring'''






								for param_group in self.optimizer.param_groups:
									lowercase_			 = [parameters_map.get(UpperCAmelCase						,       UpperCAmelCase  ) for p in param_group["params"]]




							@property
							def 				A__      (  self  )     ->       Dict:






								'''simple docstring'''






								warnings.warn(
								    "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
								    "`optimizer.step_was_skipped` instead."						,       UpperCAmelCase						,       )
								return self._is_overflow




							@property
							def 				A__      (  self  )     ->       List[str]:






								'''simple docstring'''






								return self._is_overflow




							def __getstate__(  self  )     ->       Optional[int]:






								'''simple docstring'''






								return self.__dict__.copy()




							def __setstate__(  self						,       UpperCAmelCase  )     ->       Union[str, Any]:






								'''simple docstring'''






								self.__dict__.update(UpperCAmelCase  )





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__":
							SCREAMING_SNAKE_CASE__       			=				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)

							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				parser.parse_known_args()

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

							# Set up logging
							SCREAMING_SNAKE_CASE__       			=				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
							SCREAMING_SNAKE_CASE__       			=				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							SCREAMING_SNAKE_CASE__       			=				AutoTokenizer.from_pretrained(args.model_name_or_path)

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

							# Preprocess train dataset
							SCREAMING_SNAKE_CASE__       			=				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"""])

							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"""])).batch(
							    args.per_device_train_batch_size
							)

							# Preprocess test dataset
							SCREAMING_SNAKE_CASE__       			=				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"""])

							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"""])).batch(
							    args.per_device_eval_batch_size
							)

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

							SCREAMING_SNAKE_CASE__       			=				time.time()
							SCREAMING_SNAKE_CASE__       			=				model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							SCREAMING_SNAKE_CASE__       			=				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}""")

