



'''simple docstring'''





import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
  )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer


class 							_lowerCAmelCase	:


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=13					,    _lowerCamelCase=7					,    _lowerCamelCase=True					,    _lowerCamelCase=True					,    _lowerCamelCase=True					,    _lowerCamelCase=True					,    _lowerCamelCase=99					,    _lowerCamelCase=32					,    _lowerCamelCase=5					,    _lowerCamelCase=4					,    _lowerCamelCase=37					,    _lowerCamelCase="gelu"					,    _lowerCamelCase=0.1					,    _lowerCamelCase=0.1					,    _lowerCamelCase=512					,    _lowerCamelCase=16					,    _lowerCamelCase=2					,    _lowerCamelCase=0.02					,    _lowerCamelCase=3					,    _lowerCamelCase=None					,    _lowerCamelCase=20					,    _lowerCamelCase=30					,    _lowerCamelCase=2					,    _lowerCamelCase=2					,    _lowerCamelCase=10					,    )    ->    Union[str, Any]:
    A_  :		Any						=					parent
    A_  :		Union[str, Any]						=					batch_size
    A_  :		str						=					seq_length
    A_  :		Optional[Any]						=					is_training
    A_  :		Any						=					use_input_mask
    A_  :		Tuple						=					use_token_type_ids
    A_  :		Optional[int]						=					use_labels
    A_  :		List[Any]						=					vocab_size
    A_  :		Any						=					hidden_size
    A_  :		Optional[Any]						=					num_hidden_layers
    A_  :		Union[str, Any]						=					num_attention_heads
    A_  :		Union[str, Any]						=					intermediate_size
    A_  :		List[str]						=					hidden_act
    A_  :		List[str]						=					hidden_dropout_prob
    A_  :		List[Any]						=					attention_probs_dropout_prob
    A_  :		Any						=					max_position_embeddings
    A_  :		Dict						=					type_vocab_size
    A_  :		Union[str, Any]						=					type_sequence_label_size
    A_  :		List[Any]						=					initializer_range
    A_  :		Dict						=					num_labels
    A_  :		Optional[int]						=					scope
    A_  :		Optional[Any]						=					max_xpath_tag_unit_embeddings
    A_  :		Dict						=					max_xpath_subs_unit_embeddings
    A_  :		Any						=					tag_pad_id
    A_  :		Optional[int]						=					subs_pad_id
    A_  :		List[Any]						=					max_depth







 def       UpperCAmelCase_       (      self  )    ->    Any:
    A_  :		List[Any]						=					ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size  )

    A_  :		List[str]						=					ids_tensor(
        [self.batch_size, self.seq_length, self.max_depth]					,    self.max_xpath_tag_unit_embeddings  )

    A_  :		str						=					ids_tensor(
        [self.batch_size, self.seq_length, self.max_depth]					,    self.max_xpath_subs_unit_embeddings  )

    A_  :		Optional[int]						=					None
    if self.use_input_mask:
       A_  :		List[Any]						=					ids_tensor([self.batch_size, self.seq_length]					,    vocab_size=2  )

    A_  :		str						=					None
    if self.use_token_type_ids:
       A_  :		str						=					ids_tensor([self.batch_size, self.seq_length]					,    self.type_vocab_size  )

    A_  :		int						=					None
    A_  :		List[Any]						=					None
    if self.use_labels:
       A_  :		Union[str, Any]						=					ids_tensor([self.batch_size]					,    self.type_sequence_label_size  )
       A_  :		int						=					ids_tensor([self.batch_size, self.seq_length]					,    self.num_labels  )

    A_  :		str						=					self.get_config()

    return (
        config,
        input_ids,
        xpath_tags_seq,
        xpath_subs_seq,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
    )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    return MarkupLMConfig(
        vocab_size=self.vocab_size					,    hidden_size=self.hidden_size					,    num_hidden_layers=self.num_hidden_layers					,    num_attention_heads=self.num_attention_heads					,    intermediate_size=self.intermediate_size					,    hidden_act=self.hidden_act					,    hidden_dropout_prob=self.hidden_dropout_prob					,    attention_probs_dropout_prob=self.attention_probs_dropout_prob					,    max_position_embeddings=self.max_position_embeddings					,    type_vocab_size=self.type_vocab_size					,    initializer_range=self.initializer_range					,    max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings					,    max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings					,    tag_pad_id=self.tag_pad_id					,    subs_pad_id=self.subs_pad_id					,    max_depth=self.max_depth					,    )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    )    ->    Tuple:
    A_  :		List[str]						=					MarkupLMModel(config=_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    print("""Configs:"""					,    model.config.tag_pad_id					,    model.config.subs_pad_id  )
    A_  :		Any						=					model(_lowerCamelCase					,    attention_mask=_lowerCamelCase					,    token_type_ids=_lowerCamelCase  )
    A_  :		List[str]						=					model(_lowerCamelCase					,    token_type_ids=_lowerCamelCase  )
    A_  :		Tuple						=					model(_lowerCamelCase  )
    self.parent.assertEqual(result.last_hidden_state.shape					,    (self.batch_size, self.seq_length, self.hidden_size)  )
    self.parent.assertEqual(result.pooler_output.shape					,    (self.batch_size, self.hidden_size)  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    )    ->    str:
    A_  :		Optional[int]						=					self.num_labels
    A_  :		Tuple						=					MarkupLMForSequenceClassification(_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		List[str]						=					model(
        _lowerCamelCase					,    xpath_tags_seq=_lowerCamelCase					,    xpath_subs_seq=_lowerCamelCase					,    attention_mask=_lowerCamelCase					,    token_type_ids=_lowerCamelCase					,    labels=_lowerCamelCase					,    )
    self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.num_labels)  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    )    ->    Any:
    A_  :		List[Any]						=					self.num_labels
    A_  :		Union[str, Any]						=					MarkupLMForTokenClassification(config=_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		Optional[Any]						=					model(
        _lowerCamelCase					,    xpath_tags_seq=_lowerCamelCase					,    xpath_subs_seq=_lowerCamelCase					,    attention_mask=_lowerCamelCase					,    token_type_ids=_lowerCamelCase					,    labels=_lowerCamelCase					,    )
    self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.seq_length, self.num_labels)  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    )    ->    Dict:
    A_  :		Optional[Any]						=					MarkupLMForQuestionAnswering(config=_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		Optional[int]						=					model(
        _lowerCamelCase					,    xpath_tags_seq=_lowerCamelCase					,    xpath_subs_seq=_lowerCamelCase					,    attention_mask=_lowerCamelCase					,    token_type_ids=_lowerCamelCase					,    start_positions=_lowerCamelCase					,    end_positions=_lowerCamelCase					,    )
    self.parent.assertEqual(result.start_logits.shape					,    (self.batch_size, self.seq_length)  )
    self.parent.assertEqual(result.end_logits.shape					,    (self.batch_size, self.seq_length)  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_  :		str						=					self.prepare_config_and_inputs()
    (
        (
        A_
    )						,  (
        A_
    )						,  (
        A_
    )						,  (
        A_
    )						,  (
        A_
    )						,  (
        A_
    )						,  (
        A_
    )						,  (
        A_
    )						,  
    )  :		Optional[int]						=					config_and_inputs
    A_  :		List[str]						=					{
        """input_ids""": input_ids,
        """xpath_tags_seq""": xpath_tags_seq,
        """xpath_subs_seq""": xpath_subs_seq,
        """token_type_ids""": token_type_ids,
        """attention_mask""": input_mask,
    }
    return config, inputs_dict


@require_torch
class 							_lowerCAmelCase	(  __A,							__A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     (
     (
         MarkupLMModel,
         MarkupLMForSequenceClassification,
         MarkupLMForTokenClassification,
         MarkupLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 lowerCamelCase									=     (
     {
         '''feature-extraction''': MarkupLMModel,
         '''question-answering''': MarkupLMForQuestionAnswering,
         '''text-classification''': MarkupLMForSequenceClassification,
         '''token-classification''': MarkupLMForTokenClassification,
         '''zero-shot''': MarkupLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    Optional[Any]:
    # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
    # (batch of pretokenized examples).
    return True







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    A_  :		List[str]						=					MarkupLMModelTester(self  )
    A_  :		Dict						=					ConfigTester(self					,    config_class=_lowerCamelCase					,    hidden_size=37  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    self.config_tester.run_common_tests()







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		List[Any]						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		List[Any]						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Any:
    A_  :		List[Any]						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    A_  :		Union[str, Any]						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase  )



def     UpperCAmelCase     (    )   ->       Optional[int]:



   """simple docstring"""






   A_  :		Dict						=					"""
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    """

   return html_string







@require_torch
class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 @cached_property
 def       UpperCAmelCase_       (      self  )    ->    int:
    # TODO use from_pretrained here
    A_  :		Tuple						=					MarkupLMFeatureExtractor()
    A_  :		Tuple						=					MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""  )

    return MarkupLMProcessor(_lowerCamelCase					,    _lowerCamelCase  )







 @slow
 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		Optional[int]						=					MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""  ).to(_lowerCamelCase  )

    A_  :		int						=					self.default_processor

    A_  :		List[str]						=					processor(prepare_html_string()					,    return_tensors="""pt"""  )
    A_  :		str						=					inputs.to(_lowerCamelCase  )

    # forward pass
    with torch.no_grad():
       A_  :		Dict						=					model(**_lowerCamelCase  )

    # verify the last hidden states
    A_  :		int						=					torch.Size([1, 14, 768]  )
    self.assertEqual(outputs.last_hidden_state.shape					,    _lowerCamelCase  )

    A_  :		Optional[Any]						=					torch.tensor(
        [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]  ).to(_lowerCamelCase  )

    self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]					,    _lowerCamelCase					,    atol=1e-4  )  )





'''simple docstring'''





import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__    :     Union[str, Any]			  =   16
UpperCamelCase__    :     Optional[int]			  =   32



def     UpperCAmelCase     (    a_      ,		a_		)   ->       List[Any]:



   """simple docstring"""






   if args.with_tracking:
      A_  :		Union[str, Any]						=					Accelerator(
          cpu=args.cpu      ,		mixed_precision=args.mixed_precision      ,		log_with="""all"""      ,		project_dir=args.project_dir		)
   else:
      A_  :		Optional[Any]						=					Accelerator(cpu=args.cpu      ,		mixed_precision=args.mixed_precision		)

   if hasattr(args.checkpointing_steps      ,		"""isdigit"""		):
      if args.checkpointing_steps == "epoch":
         A_  :		Tuple						=					args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         A_  :		List[str]						=					int(args.checkpointing_steps		)
      else:
         raise ValueError(
             F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."		)
   else:
      A_  :		Optional[Any]						=					None
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A_  :		str						=					config["""lr"""]
   A_  :		List[str]						=					int(config["""num_epochs"""]		)
   A_  :		Dict						=					int(config["""seed"""]		)
   A_  :		str						=					int(config["""batch_size"""]		)

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      A_  :		str						=					os.path.split(a_		)[-1].split("""."""		)[0]
      accelerator.init_trackers(a_      ,		a_		)

   A_  :		Union[str, Any]						=					AutoTokenizer.from_pretrained("""bert-base-cased"""		)
   A_  :		Optional[int]						=					load_dataset("""glue"""      ,		"""mrpc"""		)
   A_  :		List[str]						=					evaluate.load("""glue"""      ,		"""mrpc"""		)

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

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      A_  :		Dict						=					datasets.map(
          a_      ,		batched=a_      ,		remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,		)

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

   # If the batch size is too big we use gradient accumulation
   A_  :		Optional[Any]						=					1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      A_  :		List[str]						=					batch_size // MAX_GPU_BATCH_SIZE
      A_  :		List[str]						=					MAX_GPU_BATCH_SIZE

   def collate_fn(a_		):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A_  :		List[Any]						=					1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         A_  :		Any						=					1_6
      elif accelerator.mixed_precision != "no":
         A_  :		Optional[int]						=					8
      else:
         A_  :		Optional[Any]						=					None

      return tokenizer.pad(
          a_      ,		padding="""longest"""      ,		max_length=a_      ,		pad_to_multiple_of=a_      ,		return_tensors="""pt"""      ,		)

   # Instantiate dataloaders.
   A_  :		str						=					DataLoader(
       tokenized_datasets["""train"""]      ,		shuffle=a_      ,		collate_fn=a_      ,		batch_size=a_		)
   A_  :		Any						=					DataLoader(
       tokenized_datasets["""validation"""]      ,		shuffle=a_      ,		collate_fn=a_      ,		batch_size=a_		)

   set_seed(a_		)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   A_  :		List[Any]						=					AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,		return_dict=a_		)

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   A_  :		Union[str, Any]						=					model.to(accelerator.device		)

   # Instantiate optimizer
   A_  :		Any						=					AdamW(params=model.parameters()      ,		lr=a_		)

   # Instantiate scheduler
   A_  :		Dict						=					get_linear_schedule_with_warmup(
       optimizer=a_      ,		num_warmup_steps=1_0_0      ,		num_training_steps=(len(a_		) * num_epochs) // gradient_accumulation_steps      ,		)

   # 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.
   A_						,  A_						,  A_						,  A_						,  A_  :		List[str]						=					accelerator.prepare(
       a_      ,		a_      ,		a_      ,		a_      ,		a_		)

   # We need to keep track of how many total steps we have iterated over
   A_  :		Optional[int]						=					0
   # We also need to keep track of the stating epoch so files are named properly
   A_  :		Union[str, Any]						=					0

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"		)
         accelerator.load_state(args.resume_from_checkpoint		)
         A_  :		int						=					os.path.basename(args.resume_from_checkpoint		)
      else:
         # Get the most recent checkpoint
         A_  :		int						=					[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
         dirs.sort(key=os.path.getctime		)
         A_  :		Optional[Any]						=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      A_  :		Optional[Any]						=					os.path.splitext(a_		)[0]

      if "epoch" in training_difference:
         A_  :		List[Any]						=					int(training_difference.replace("""epoch_"""      ,		""""""		)		) + 1
         A_  :		List[str]						=					None
      else:
         A_  :		Optional[int]						=					int(training_difference.replace("""step_"""      ,		""""""		)		)
         A_  :		Optional[int]						=					resume_step // len(a_		)
         resume_step -= starting_epoch * len(a_		)

    # Now we train the model
   for epoch in range(a_      ,		a_		):
      model.train()
      if args.with_tracking:
         A_  :		List[str]						=					0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         A_  :		List[Any]						=					accelerator.skip_first_batches(a_      ,		a_		)
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         A_  :		Tuple						=					train_dataloader
      for step, batch in enumerate(a_		):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device		)
         A_  :		List[Any]						=					model(**a_		)
         A_  :		str						=					outputs.loss
         A_  :		Tuple						=					loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(a_		)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(a_      ,		a_		):
            A_  :		str						=					F"step_{overall_step}"
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  A_  :		Dict						=					os.path.join(args.output_dir      ,		a_		)
               accelerator.save_state(a_		)

      model.eval()
      for step, batch in enumerate(a_		):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device		)
         with torch.no_grad():
            A_  :		str						=					model(**a_		)
         A_  :		Any						=					outputs.logits.argmax(dim=-1		)
         A_						,  A_  :		Dict						=					accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
         metric.add_batch(
             predictions=a_      ,		references=a_      ,		)

      A_  :		Union[str, Any]						=					metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}:"      ,		a_		)
      if args.with_tracking:
         accelerator.log(
             {
                 """accuracy""": eval_metric["""accuracy"""],
                 """f1""": eval_metric["""f1"""],
                 """train_loss""": total_loss.item() / len(a_		),
                 """epoch""": epoch,
             }      ,		step=a_      ,		)

      if checkpointing_steps == "epoch":
         A_  :		str						=					F"epoch_{epoch}"
         if args.output_dir is not None:
            A_  :		Dict						=					os.path.join(args.output_dir      ,		a_		)
         accelerator.save_state(a_		)

   if args.with_tracking:
      accelerator.end_training()



def     UpperCAmelCase     (    )   ->       str:



   """simple docstring"""






   A_  :		List[Any]						=					argparse.ArgumentParser(description="""Simple example of training script."""		)
   parser.add_argument(
       """--mixed_precision"""      ,		type=a_      ,		default=a_      ,		choices=["""no""", """fp16""", """bf16""", """fp8"""]      ,		help="""Whether to use mixed precision. Choose"""
       """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
       """and an Nvidia Ampere GPU."""      ,		)
   parser.add_argument("""--cpu"""      ,		action="""store_true"""      ,		help="""If passed, will train on the CPU."""		)
   parser.add_argument(
       """--checkpointing_steps"""      ,		type=a_      ,		default=a_      ,		help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""      ,		)
   parser.add_argument(
       """--resume_from_checkpoint"""      ,		type=a_      ,		default=a_      ,		help="""If the training should continue from a checkpoint folder."""      ,		)
   parser.add_argument(
       """--with_tracking"""      ,		action="""store_true"""      ,		help="""Whether to load in all available experiment trackers from the environment and use them for logging."""      ,		)
   parser.add_argument(
       """--output_dir"""      ,		type=a_      ,		default="""."""      ,		help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,		)
   parser.add_argument(
       """--project_dir"""      ,		type=a_      ,		default="""logs"""      ,		help="""Location on where to store experiment tracking logs` and relevent project information"""      ,		)
   A_  :		Tuple						=					parser.parse_args()
   A_  :		Union[str, Any]						=					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
   training_function(a_      ,		a_		)


if __name__ == "__main__":
  main()
