




"""simple docstring"""





import logging
from pathlib import Path
from typing import Any, List, Tuple

import yaml


# loading package files: https://stackoverflow.com/a/20885799
try:
							import importlib.resources as pkg_resources
except ImportError:
							# Try backported to PY<37 `importlib_resources`.
							import importlib_resources as pkg_resources

from . import resources


_lowercase		 =      """https://github.com/huggingface/datasets/tree/main/src/datasets/utils"""
_lowercase		 =      f"""{BASE_REF_URL}/{__file__}"""
_lowercase		 =      logging.getLogger(__name__)

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Optional[int]							     =   pkg_resources.read_text(_A ,     _A						)
						return yaml.safe_load(_A						), f'''{BASE_REF_URL}/resources/{resource}'''


_lowercase ,_lowercase		 =      load_yaml_resource("""readme_structure.yaml""")

_lowercase		 =      [
    """[Needs More Information]""",
    """[More Information Needed]""",
    """(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)""",
]

# Dictionary representation of section/readme, error_list, warning_list
_lowercase		 =      Tuple[dict, List[str], List[str]]

class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       List[str] ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       List[str] = None ,SCREAMING_SNAKE_CASE_				:       Optional[Any] = False ):
										snake_case__    :   Dict							     =   name
										snake_case__    :   Optional[int]							     =   level
										snake_case__    :   List[str]							     =   lines
										snake_case__    :   str							     =   ''''''
										snake_case__    :   List[Any]							     =   True
										snake_case__    :   int							     =   {}
										snake_case__    :   Union[str, Any]							     =   []
										snake_case__    :   Any							     =   []
										if self.lines is not None:
																self.parse(suppress_parsing_errors=__lowerCAmelCase )

				def  _lowerCamelCase   (				self				:       int ,SCREAMING_SNAKE_CASE_				:       Optional[Any] = False ):
										snake_case__    :   List[Any]							     =   ''''''
										snake_case__    :   Optional[Any]							     =   []
										snake_case__    :   int							     =   False
										for line in self.lines:
																if line.strip(''' \n''' ) == "":
																						continue
																elif line.strip(''' \n''' )[:3] == "```":
																						snake_case__    :   Dict							     =   not code_start
																elif line.split()[0] == self.level + "#" and not code_start:
																						if current_sub_level != "":
																												snake_case__    :   str							     =   Section(__lowerCAmelCase ,self.level + '''#''' ,__lowerCAmelCase )
																												snake_case__    :   Optional[Any]							     =   []
																						else:
																												if current_lines != []:
																																		self.text += "".join(__lowerCAmelCase ).strip()
																																		if self.text != "" and self.text not in FILLER_TEXT:
																																								snake_case__    :   Any							     =   False
																																		snake_case__    :   Optional[Any]							     =   []

																						snake_case__    :   Any							     =   ''' '''.join(line.split()[1:] ).strip(''' \n''' )
																else:
																						current_lines.append(__lowerCAmelCase )
										else:
																if current_sub_level != "":
																						if current_sub_level in self.content:
																												self.parsing_error_list.append(
																												    F'''Multiple sections with the same heading `{current_sub_level}` have been found. Please keep only one of these sections.''' )
																						snake_case__    :   List[str]							     =   Section(__lowerCAmelCase ,self.level + '''#''' ,__lowerCAmelCase )
																else:
																						if current_lines != []:
																												self.text += "".join(__lowerCAmelCase ).strip()
																												if self.text != "" and self.text not in FILLER_TEXT:
																																		snake_case__    :   Tuple							     =   False

										if self.level == "" and not suppress_parsing_errors:
																if self.parsing_error_list != [] or self.parsing_warning_list != []:
																						snake_case__    :   Tuple							     =   '''\n'''.join('''-\t''' + x for x in self.parsing_error_list + self.parsing_warning_list )
																						snake_case__    :   Tuple							     =   F'''The following issues were found while parsing the README at `{self.name}`:\n''' + errors
																						raise ValueError(__lowerCAmelCase )

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       int ):
										snake_case__    :   Dict							     =   []
										snake_case__    :   List[str]							     =   []
										if structure["allow_empty"] is False:
																# If content is expected
																if self.is_empty_text and self.content == {}:
																						# If no content is found, mention it in the error_list
																						error_list.append(F'''Expected some content in section `{self.name}` but it is empty.''' )

										if structure["allow_empty_text"] is False:
																# If some text is expected
																if self.is_empty_text:
																						# If no text is found, mention it in the error_list
																						error_list.append(
																						    F'''Expected some text in section `{self.name}` but it is empty (text in subsections are ignored).''' )
        # Subsections Validation
										if structure["subsections"] is not None:
																# If subsections are expected
																if self.content == {}:
																						# If no subsections are present
																						snake_case__    :   Any							     =   [subsection['''name'''] for subsection in structure['''subsections''']]
																						# Mention the expected values in the error_list
																						error_list.append(
																						    F'''Section `{self.name}` expected the following subsections: {', '.join(['`'+x+'`' for x in values] )}. Found \'None\'.''' )
																else:
																						# If some subsections are present
																						snake_case__    :   Tuple							     =   [subsection['''name'''] for subsection in structure['''subsections''']]
																						snake_case__    :   Union[str, Any]							     =   False
																						for idx, name in enumerate(__lowerCAmelCase ):
																												if name not in self.content:
																																		# If the expected subsection is not present
																																		error_list.append(F'''Section `{self.name}` is missing subsection: `{name}`.''' )
																																		snake_case__    :   Dict							     =   True
																												else:
																																		# If the subsection is present, validate subsection, return the result
																																		# and concat the errors from subsection to section error_list

																																		# Skip sublevel validation if current level is `###`
																																		if self.level == "###":
																																								continue
																																		else:
																																								snake_case__     ,		snake_case__     ,		snake_case__    :   str							     =   self.content[name].validate(
																																								    structure['''subsections'''][idx] )
																																		error_list += subsec_error_list
																																		warning_list += subsec_warning_list

																						if has_missing_subsections:  # we only allow to have extra subsections if all the other ones are here
																												for name in self.content:
																																		if name not in structure_names:
																																								# If an extra subsection is present
																																								warning_list.append(
																																								    F'''`{self.name}` has an extra subsection: `{name}`. Skipping further validation checks for this subsection as expected structure is unknown.''' )
										if error_list:
																# If there are errors, do not return the dictionary as it is invalid
																return {}, error_list, warning_list
										else:
																return self.to_dict(), error_list, warning_list





				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										return {
										    "name": self.name,
										    "text": self.text,
										    "is_empty_text": self.is_empty_text,
										    "subsections": [value.to_dict() for value in self.content.values()],
										}







class  __lowercase    (     lowerCAmelCase__		):  # Level 0


				'''simple docstring'''


				def __init__(				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Dict = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = False ):
										super().__init__(name=__lowerCAmelCase ,level='''''' )  # Not using lines here as we need to use a child class parse
										snake_case__    :   Optional[int]							     =   structure
										snake_case__    :   Union[str, Any]							     =   -2
										snake_case__    :   Union[str, Any]							     =   0
										snake_case__    :   int							     =   lines
										if self.lines is not None:
																self.parse(suppress_parsing_errors=__lowerCAmelCase )

				def  _lowerCamelCase   (				self				:       int ):


										if self.structure is None:
																snake_case__     ,		snake_case__     ,		snake_case__    :   List[str]							     =   self._validate(__lowerCAmelCase )
										else:
																snake_case__     ,		snake_case__     ,		snake_case__    :   Any							     =   self._validate(self.structure )
										if error_list != [] or warning_list != []:
																snake_case__    :   List[str]							     =   '''\n'''.join(['''-\t''' + x for x in error_list + warning_list] )
																snake_case__    :   Optional[int]							     =   F'''The following issues were found for the README at `{self.name}`:\n''' + errors
																raise ValueError(__lowerCAmelCase )

				@classmethod
				def  _lowerCamelCase   (				cls				:       List[str] ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       Tuple = None ,SCREAMING_SNAKE_CASE_				:       str = False ):


										with open(__lowerCAmelCase ,encoding='''utf-8''' ) as f:
																snake_case__    :   List[Any]							     =   f.readlines()
										return cls(__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,suppress_parsing_errors=__lowerCAmelCase )

				@classmethod
				def  _lowerCamelCase   (				cls				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       List[Any] = None ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] = "root" ,SCREAMING_SNAKE_CASE_				:       Dict = False ):
										snake_case__    :   int							     =   string.split('''\n''' )
										return cls(__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,suppress_parsing_errors=__lowerCAmelCase )

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       Tuple = False ):
										snake_case__    :   Optional[int]							     =   0

										for line in self.lines:
																self.yaml_tags_line_count += 1
																if line.strip(''' \n''' ) == "---":
																						self.tag_count += 1
																						if self.tag_count == 2:
																												break
																line_count += 1
										if self.tag_count == 2:
																snake_case__    :   Dict							     =   self.lines[line_count + 1 :]  # Get the last + 1 th item.
										else:
																snake_case__    :   Dict							     =   self.lines[self.tag_count :]
										super().parse(suppress_parsing_errors=__lowerCAmelCase )

				def __str__(				self				:       Tuple ):
										return str(self.to_dict() )





				def  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ):
										snake_case__    :   Union[str, Any]							     =   []
										snake_case__    :   Tuple							     =   []
										if self.yaml_tags_line_count == 0:
																warning_list.append('''Empty YAML markers are present in the README.''' )
										elif self.tag_count == 0:
																warning_list.append('''No YAML markers are present in the README.''' )
										elif self.tag_count == 1:
																warning_list.append('''Only the start of YAML tags present in the README.''' )
										# Check how many first level sections are present.
										snake_case__    :   List[str]							     =   len(self.content.keys() )
										if num_first_level_keys > 1:
																# If more than one, add to the error list, continue
																error_list.append(
																    F'''The README has several first-level headings: {', '.join(['`'+x+'`' for x in list(self.content.keys() )] )}. Only one heading is expected. Skipping further validation for this README.''' )
										elif num_first_level_keys < 1:
																# If less than one, append error.
																error_list.append(
																    '''The README has no first-level headings. One heading is expected. Skipping further validation for this README.''' )

										else:
																# If one exactly
																snake_case__    :   List[Any]							     =   list(self.content.keys() )[0]  # Get the key
																if start_key.startswith('''Dataset Card for''' ):  # Check correct start
																						# If the starting is correct, validate all the sections
																						snake_case__     ,		snake_case__     ,		snake_case__    :   int							     =   self.content[start_key].validate(
																						    readme_structure['''subsections'''][0] )
																						error_list += sec_error_list
																						warning_list += sec_warning_list
																else:
																						# If not found, append error
																						error_list.append(
																						    '''No first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.''' )
										if error_list:
																# If there are errors, do not return the dictionary as it is invalid
																return {}, error_list, warning_list
										else:
																return self.to_dict(), error_list, warning_list


if __name__ == "__main__":
							from argparse import ArgumentParser

							_lowercase		 =      ArgumentParser(usage="""Validate the content (excluding YAML tags) of a README.md file.""")
							ap.add_argument("""readme_filepath""")
							_lowercase		 =      ap.parse_args()
							_lowercase		 =      Path(args.readme_filepath)
							_lowercase		 =      ReadMe.from_readme(readme_filepath)










"""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,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

_lowercase		 =      16
_lowercase		 =      32

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase = 16						):
						'''simple docstring'''


						snake_case__    :   int							     =   AutoTokenizer.from_pretrained('''bert-base-cased'''						)
						snake_case__    :   Dict							     =   load_dataset('''glue''' ,     '''mrpc'''						)

						def tokenize_function(lowerCamelCase						):
												# max_length=None => use the model max length (it's actually the default)
												snake_case__    :   Optional[int]							     =   tokenizer(examples['''sentence1'''] ,     examples['''sentence2'''] ,     truncation=lowerCamelCase ,     max_length=lowerCamelCase						)
												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():
												snake_case__    :   Tuple							     =   datasets.map(
												    lowerCamelCase ,     batched=lowerCamelCase ,     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
						snake_case__    :   str							     =   tokenized_datasets.rename_column('''label''' ,     '''labels'''						)

						def collate_fn(lowerCamelCase						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case__    :   Tuple							     =   128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		snake_case__    :   Optional[Any]							     =   16
												elif accelerator.mixed_precision != "no":
																		snake_case__    :   Dict							     =   8
												else:
																		snake_case__    :   str							     =   None

												return tokenizer.pad(
												    lowerCamelCase ,     padding='''longest''' ,     max_length=lowerCamelCase ,     pad_to_multiple_of=lowerCamelCase ,     return_tensors='''pt''' ,     )

						# Instantiate dataloaders.
						snake_case__    :   List[Any]							     =   DataLoader(
						    tokenized_datasets['''train'''] ,     shuffle=lowerCamelCase ,     collate_fn=lowerCamelCase ,     batch_size=lowerCamelCase						)
						snake_case__    :   Tuple							     =   DataLoader(
						    tokenized_datasets['''validation'''] ,     shuffle=lowerCamelCase ,     collate_fn=lowerCamelCase ,     batch_size=lowerCamelCase						)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							_lowercase		 =      mocked_dataloaders  # noqa: F811

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,     lowerCamelCase						) == "1":
												snake_case__    :   Any							     =   2
						# Initialize accelerator
						snake_case__    :   List[Any]							     =   Accelerator(cpu=args.cpu ,     mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case__    :   Any							     =   config['''lr''']
						snake_case__    :   int							     =   int(config['''num_epochs''']						)
						snake_case__    :   List[str]							     =   int(config['''seed''']						)
						snake_case__    :   Dict							     =   int(config['''batch_size''']						)

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps ,     '''isdigit'''						):
												if args.checkpointing_steps == "epoch":
																		snake_case__    :   Optional[Any]							     =   args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case__    :   Union[str, Any]							     =   int(args.checkpointing_steps						)
												else:
																		raise ValueError(
																		    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
						else:
												snake_case__    :   Union[str, Any]							     =   None

						set_seed(lowerCamelCase						)

						snake_case__     ,		snake_case__    :   int							     =   get_dataloaders(lowerCamelCase ,     lowerCamelCase						)
						snake_case__    :   Optional[Any]							     =   evaluate.load('''glue''' ,     '''mrpc'''						)

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

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case__    :   Tuple							     =   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,     return_dict=lowerCamelCase						)

						# 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).
						snake_case__    :   Dict							     =   model.to(accelerator.device						)

						# Instantiate optimizer
						snake_case__    :   Optional[int]							     =   AdamW(params=model.parameters() ,     lr=lowerCamelCase						)

						# Instantiate scheduler
						snake_case__    :   Optional[Any]							     =   get_linear_schedule_with_warmup(
						    optimizer=lowerCamelCase ,     num_warmup_steps=100 ,     num_training_steps=(len(lowerCamelCase						) * 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.
						snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__    :   Optional[Any]							     =   accelerator.prepare(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

						# New Code #
						# We need to keep track of how many total steps we have iterated over
						snake_case__    :   Any							     =   0
						# We also need to keep track of the stating epoch so files are named properly
						snake_case__    :   List[str]							     =   0

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						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						)
																		snake_case__    :   Optional[Any]							     =   os.path.basename(args.resume_from_checkpoint						)
												else:
																		# Get the most recent checkpoint
																		snake_case__    :   Any							     =   [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
																		dirs.sort(key=os.path.getctime						)
																		snake_case__    :   str							     =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												snake_case__    :   List[Any]							     =   os.path.splitext(lowerCamelCase						)[0]

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

    # Now we train the model
						for epoch in range(lowerCamelCase ,     lowerCamelCase						):
												model.train()
												# New Code #
												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
																		snake_case__    :   int							     =   accelerator.skip_first_batches(lowerCamelCase ,     lowerCamelCase						)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		snake_case__    :   Tuple							     =   train_dataloader
												for step, batch in enumerate(lowerCamelCase						):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device						)
																		snake_case__    :   Tuple							     =   model(**lowerCamelCase						)
																		snake_case__    :   Optional[int]							     =   outputs.loss
																		snake_case__    :   Union[str, Any]							     =   loss / gradient_accumulation_steps
																		accelerator.backward(lowerCamelCase						)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(lowerCamelCase ,     lowerCamelCase						):
																								snake_case__    :   Tuple							     =   f'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case__    :   Optional[int]							     =   os.path.join(args.output_dir ,     lowerCamelCase						)
																														accelerator.save_state(lowerCamelCase						)

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

												snake_case__    :   str							     =   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:''' ,     lowerCamelCase						)

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		snake_case__    :   str							     =   f'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								snake_case__    :   Optional[int]							     =   os.path.join(args.output_dir ,     lowerCamelCase						)
																		accelerator.save_state(lowerCamelCase						)

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   Tuple							     =   argparse.ArgumentParser(description='''Simple example of training script.'''						)
						parser.add_argument(
						    '''--mixed_precision''' ,     type=lowerCamelCase ,     default=lowerCamelCase ,     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=lowerCamelCase ,     default=lowerCamelCase ,     help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.''' ,     )
						parser.add_argument(
						    '''--output_dir''' ,     type=lowerCamelCase ,     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=lowerCamelCase ,     default=lowerCamelCase ,     help='''If the training should continue from a checkpoint folder.''' ,     )
						snake_case__    :   List[str]							     =   parser.parse_args()
						snake_case__    :   str							     =   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowerCamelCase ,     lowerCamelCase						)


if __name__ == "__main__":
							main()


