





'''simple docstring'''






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowerCamelCase						: Tuple		=   16
__lowerCamelCase						: Optional[int]		=   32


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = 16  ,      __SCREAMING_SNAKE_CASE = "bert-base-cased"   ):


		"""simple docstring"""

		_UpperCamelCase       			=AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )
		_UpperCamelCase       			=load_dataset('''glue'''  ,      '''mrpc'''   )

		def tokenize_function(__SCREAMING_SNAKE_CASE   ):
				# max_length=None => use the model max length (it's actually the default)
				_UpperCamelCase       			=tokenizer(examples['''sentence1''']  ,      examples['''sentence2''']  ,      truncation=__SCREAMING_SNAKE_CASE  ,      max_length=__SCREAMING_SNAKE_CASE   )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		_UpperCamelCase       			=datasets.map(
		    __SCREAMING_SNAKE_CASE  ,      batched=__SCREAMING_SNAKE_CASE  ,      remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,      load_from_cache_file=__SCREAMING_SNAKE_CASE   )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		_UpperCamelCase       			=tokenized_datasets.rename_column('''label'''  ,      '''labels'''   )

		def collate_fn(__SCREAMING_SNAKE_CASE   ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(__SCREAMING_SNAKE_CASE  ,      padding='''max_length'''  ,      max_length=128  ,      return_tensors='''pt'''   )
				return tokenizer.pad(__SCREAMING_SNAKE_CASE  ,      padding='''longest'''  ,      return_tensors='''pt'''   )

		# Instantiate dataloaders.
		_UpperCamelCase       			=DataLoader(
		    tokenized_datasets['''train''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE   )
		_UpperCamelCase       			=DataLoader(
		    tokenized_datasets['''validation''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE   )

		return train_dataloader, eval_dataloader


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		model.eval()
		_UpperCamelCase       			=0
		for step, batch in enumerate(__SCREAMING_SNAKE_CASE   ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device   )
				with torch.no_grad():
						_UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   )
				_UpperCamelCase       			=outputs.logits.argmax(dim=-1   )
				# It is slightly faster to call this once, than multiple times
				_UpperCamelCase			,     _UpperCamelCase       			=accelerator.gather(
				    (predictions, batch['''labels'''])   )  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(__SCREAMING_SNAKE_CASE   ) - 1:
								_UpperCamelCase       			=predictions[: len(eval_dataloader.dataset   ) - samples_seen]
								_UpperCamelCase       			=references[: len(eval_dataloader.dataset   ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=__SCREAMING_SNAKE_CASE  ,      references=__SCREAMING_SNAKE_CASE  ,      )

		_UpperCamelCase       			=metric.compute()
		return eval_metric["accuracy"]


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		_UpperCamelCase       			=Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_UpperCamelCase       			=config['''lr''']
		_UpperCamelCase       			=int(config['''num_epochs''']   )
		_UpperCamelCase       			=int(config['''seed''']   )
		_UpperCamelCase       			=int(config['''batch_size''']   )
		_UpperCamelCase       			=args.model_name_or_path

		set_seed(__SCREAMING_SNAKE_CASE   )
		_UpperCamelCase			,     _UpperCamelCase       			=get_dataloaders(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_UpperCamelCase       			=AutoModelForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,      return_dict=__SCREAMING_SNAKE_CASE   )

		# Instantiate optimizer
		_UpperCamelCase       			=(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		_UpperCamelCase       			=optimizer_cls(params=model.parameters()  ,      lr=__SCREAMING_SNAKE_CASE   )

		if accelerator.state.deepspeed_plugin is not None:
				_UpperCamelCase       			=accelerator.state.deepspeed_plugin.deepspeed_config[
				    '''gradient_accumulation_steps'''
				]
		else:
				_UpperCamelCase       			=1
		_UpperCamelCase       			=(len(__SCREAMING_SNAKE_CASE   ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				_UpperCamelCase       			=get_linear_schedule_with_warmup(
				    optimizer=__SCREAMING_SNAKE_CASE  ,      num_warmup_steps=0  ,      num_training_steps=__SCREAMING_SNAKE_CASE  ,      )
		else:
				_UpperCamelCase       			=DummyScheduler(__SCREAMING_SNAKE_CASE  ,      total_num_steps=__SCREAMING_SNAKE_CASE  ,      warmup_num_steps=0   )

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=accelerator.prepare(
		    __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

		# We need to keep track of how many total steps we have iterated over
		_UpperCamelCase       			=0
		# We also need to keep track of the stating epoch so files are named properly
		_UpperCamelCase       			=0
		_UpperCamelCase       			=evaluate.load('''glue'''  ,      '''mrpc'''   )
		_UpperCamelCase       			=num_epochs

		if args.partial_train_epoch is not None:
				_UpperCamelCase       			=args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint   )
				_UpperCamelCase       			=args.resume_from_checkpoint.split('''epoch_'''   )[1]
				_UpperCamelCase       			=''''''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				_UpperCamelCase       			=int(__SCREAMING_SNAKE_CASE   ) + 1
				_UpperCamelCase       			=evaluation_loop(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
				accelerator.print('''resumed checkpoint performance:'''  ,      __SCREAMING_SNAKE_CASE   )
				accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''  ,      lr_scheduler.get_lr()[0]   )
				accelerator.print('''resumed optimizers\'s lr:'''  ,      optimizer.param_groups[0]['''lr''']   )
				with open(os.path.join(args.output_dir  ,      f'''state_{starting_epoch-1}.json'''   )  ,      '''r'''   ) as f:
						_UpperCamelCase       			=json.load(__SCREAMING_SNAKE_CASE   )
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

				# Now we train the model
		_UpperCamelCase       			={}
		for epoch in range(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):
				model.train()
				for step, batch in enumerate(__SCREAMING_SNAKE_CASE   ):
						_UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   )
						_UpperCamelCase       			=outputs.loss
						_UpperCamelCase       			=loss / gradient_accumulation_steps
						accelerator.backward(__SCREAMING_SNAKE_CASE   )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				_UpperCamelCase       			=f'''epoch_{epoch}'''
				_UpperCamelCase       			=os.path.join(args.output_dir  ,      __SCREAMING_SNAKE_CASE   )
				accelerator.save_state(__SCREAMING_SNAKE_CASE   )
				_UpperCamelCase       			=evaluation_loop(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
				_UpperCamelCase       			=accuracy
				_UpperCamelCase       			=lr_scheduler.get_lr()[0]
				_UpperCamelCase       			=optimizer.param_groups[0]['''lr''']
				_UpperCamelCase       			=epoch
				_UpperCamelCase       			=overall_step
				accelerator.print(f'''epoch {epoch}:'''  ,      __SCREAMING_SNAKE_CASE   )

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir  ,      f'''state_{epoch}.json'''   )  ,      '''w'''   ) as f:
								json.dump(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   ():


		"""simple docstring"""

		_UpperCamelCase       			=argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''   )
		parser.add_argument(
		    '''--model_name_or_path'''  ,      type=__SCREAMING_SNAKE_CASE  ,      default='''bert-base-cased'''  ,      help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,      required=__SCREAMING_SNAKE_CASE  ,      )
		parser.add_argument(
		    '''--output_dir'''  ,      type=__SCREAMING_SNAKE_CASE  ,      default='''.'''  ,      help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,      )
		parser.add_argument(
		    '''--resume_from_checkpoint'''  ,      type=__SCREAMING_SNAKE_CASE  ,      default=__SCREAMING_SNAKE_CASE  ,      help='''If the training should continue from a checkpoint folder.'''  ,      )
		parser.add_argument(
		    '''--partial_train_epoch'''  ,      type=__SCREAMING_SNAKE_CASE  ,      default=__SCREAMING_SNAKE_CASE  ,      help='''If passed, the training will stop after this number of epochs.'''  ,      )
		parser.add_argument(
		    '''--num_epochs'''  ,      type=__SCREAMING_SNAKE_CASE  ,      default=2  ,      help='''Number of train epochs.'''  ,      )
		_UpperCamelCase       			=parser.parse_args()
		_UpperCamelCase       			={'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

		training_function(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
						main()













'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase						: Optional[int]		=   {
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: int		=   [
          'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
          'XmodForCausalLM',
          'XmodForMaskedLM',
          'XmodForMultipleChoice',
          'XmodForQuestionAnswering',
          'XmodForSequenceClassification',
          'XmodForTokenClassification',
          'XmodModel',
          'XmodPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xmod import (
                XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
                XmodForCausalLM,
                XmodForMaskedLM,
                XmodForMultipleChoice,
                XmodForQuestionAnswering,
                XmodForSequenceClassification,
                XmodForTokenClassification,
                XmodModel,
                XmodPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						: Dict		=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






