



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


__A       =						16
__A       =						32



def 	__a					(   lowerCAmelCase_       : Accelerator      ,lowerCAmelCase_       : int = 16      ,lowerCAmelCase_       : str = "bert-base-cased"    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       AutoTokenizer.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       load_dataset("""glue"""      ,"""mrpc"""    )

						def tokenize_function(lowerCAmelCase_       : List[Any]    ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase_=       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
						UpperCAmelCase_=       datasets.map(
						    lowerCAmelCase_      ,batched=lowerCAmelCase_      ,remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,load_from_cache_file=lowerCAmelCase_    )

						# 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(lowerCAmelCase_       : List[Any]    ):
												# 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(lowerCAmelCase_      ,padding="""max_length"""      ,max_length=1_28      ,return_tensors="""pt"""    )
												return tokenizer.pad(lowerCAmelCase_      ,padding="""longest"""      ,return_tensors="""pt"""    )

						# Instantiate dataloaders.
						UpperCAmelCase_=       DataLoader(
						    tokenized_datasets["""train"""]      ,shuffle=lowerCAmelCase_      ,collate_fn=lowerCAmelCase_      ,batch_size=lowerCAmelCase_    )
						UpperCAmelCase_=       DataLoader(
						    tokenized_datasets["""validation"""]      ,shuffle=lowerCAmelCase_      ,collate_fn=lowerCAmelCase_      ,batch_size=lowerCAmelCase_    )

						return train_dataloader, eval_dataloader



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : List[str]    )				->			int:






						'''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(lowerCAmelCase_    )
						UpperCAmelCase_,       UpperCAmelCase_=       get_dataloaders(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

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

						# 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=lowerCAmelCase_    )

						if accelerator.state.deepspeed_plugin is not None:
												UpperCAmelCase_=       accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												UpperCAmelCase_=       1
						UpperCAmelCase_=       (len(lowerCAmelCase_    ) * 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=lowerCAmelCase_      ,num_warmup_steps=0      ,num_training_steps=lowerCAmelCase_      ,)
						else:
												UpperCAmelCase_=       DummyScheduler(lowerCAmelCase_      ,total_num_steps=lowerCAmelCase_      ,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(
						    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						# 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

						# Now we train the model
						UpperCAmelCase_=       evaluate.load("""glue"""      ,"""mrpc"""    )
						UpperCAmelCase_=       0
						UpperCAmelCase_=       {}
						for epoch in range(lowerCAmelCase_      ,lowerCAmelCase_    ):
												model.train()
												for step, batch in enumerate(lowerCAmelCase_    ):
																		UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		UpperCAmelCase_=       outputs.loss
																		UpperCAmelCase_=       loss / gradient_accumulation_steps
																		accelerator.backward(lowerCAmelCase_    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												UpperCAmelCase_=       0
												for step, batch in enumerate(lowerCAmelCase_    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		with torch.no_grad():
																								UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		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(lowerCAmelCase_    ) - 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=lowerCAmelCase_      ,references=lowerCAmelCase_      ,)

												UpperCAmelCase_=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""      ,lowerCAmelCase_    )
												UpperCAmelCase_=       eval_metric["""accuracy"""]

												if best_performance < eval_metric["accuracy"]:
																		UpperCAmelCase_=       eval_metric["""accuracy"""]

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
												with open(os.path.join(args.output_dir      ,"""all_results.json"""    )      ,"""w"""    ) as f:
																		json.dump(lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
						parser.add_argument(
						    """--model_name_or_path"""      ,type=lowerCAmelCase_      ,default="""bert-base-cased"""      ,help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,required=lowerCAmelCase_      ,)
						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(
						    """--performance_lower_bound"""      ,type=lowerCAmelCase_      ,default=lowerCAmelCase_      ,help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""      ,)
						parser.add_argument(
						    """--num_epochs"""      ,type=lowerCAmelCase_      ,default=3      ,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(lowerCAmelCase_      ,lowerCAmelCase_    )


if __name__ == "__main__":
	main()









import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__A       =						datasets.utils.logging.get_logger(__name__)

__A       =						['''names''', '''prefix''']
__A       =						['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__A       =						['''encoding_errors''', '''on_bad_lines''']
__A       =						['''date_format''']






@dataclass
class 		lowercase						(			datasets.BuilderConfig):






	"""simple docstring"""


	a__				: str		       =							","
	a__				: Optional[str]		       =							None
	a__				: Optional[Union[int, List[int], str]]		       =							"infer"
	a__				: Optional[List[str]]		       =							None
	a__				: Optional[List[str]]		       =							None
	a__				: Optional[Union[int, str, List[int], List[str]]]		       =							None
	a__				: Optional[Union[List[int], List[str]]]		       =							None
	a__				: Optional[str]		       =							None
	a__				: bool		       =							True
	a__				: Optional[Literal["c", "python", "pyarrow"]]		       =							None
	a__				: Dict[Union[int, str], Callable[[Any], Any]]		       =							None
	a__				: Optional[list]		       =							None
	a__				: Optional[list]		       =							None
	a__				: bool		       =							False
	a__				: Optional[Union[int, List[int]]]		       =							None
	a__				: Optional[int]		       =							None
	a__				: Optional[Union[str, List[str]]]		       =							None
	a__				: bool		       =							True
	a__				: bool		       =							True
	a__				: bool		       =							False
	a__				: bool		       =							True
	a__				: Optional[str]		       =							None
	a__				: str		       =							"."
	a__				: Optional[str]		       =							None
	a__				: str		       =							'"'
	a__				: int		       =							0
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: bool		       =							True
	a__				: bool		       =							True
	a__				: int		       =							0
	a__				: bool		       =							True
	a__				: bool		       =							False
	a__				: Optional[str]		       =							None
	a__				: int		       =							1_0000
	a__				: Optional[datasets.Features]		       =							None
	a__				: Optional[str]		       =							"strict"
	a__				: Literal["error", "warn", "skip"]		       =							"error"
	a__				: Optional[str]		       =							None




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			int:
							if self.delimiter is not None:
													UpperCAmelCase_=       self.delimiter
							if self.column_names is not None:
													UpperCAmelCase_=       self.column_names




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Tuple:
							UpperCAmelCase_=       {
							    """sep""": self.sep,
							    """header""": self.header,
							    """names""": self.names,
							    """index_col""": self.index_col,
							    """usecols""": self.usecols,
							    """prefix""": self.prefix,
							    """mangle_dupe_cols""": self.mangle_dupe_cols,
							    """engine""": self.engine,
							    """converters""": self.converters,
							    """true_values""": self.true_values,
							    """false_values""": self.false_values,
							    """skipinitialspace""": self.skipinitialspace,
							    """skiprows""": self.skiprows,
							    """nrows""": self.nrows,
							    """na_values""": self.na_values,
							    """keep_default_na""": self.keep_default_na,
							    """na_filter""": self.na_filter,
							    """verbose""": self.verbose,
							    """skip_blank_lines""": self.skip_blank_lines,
							    """thousands""": self.thousands,
							    """decimal""": self.decimal,
							    """lineterminator""": self.lineterminator,
							    """quotechar""": self.quotechar,
							    """quoting""": self.quoting,
							    """escapechar""": self.escapechar,
							    """comment""": self.comment,
							    """encoding""": self.encoding,
							    """dialect""": self.dialect,
							    """error_bad_lines""": self.error_bad_lines,
							    """warn_bad_lines""": self.warn_bad_lines,
							    """skipfooter""": self.skipfooter,
							    """doublequote""": self.doublequote,
							    """memory_map""": self.memory_map,
							    """float_precision""": self.float_precision,
							    """chunksize""": self.chunksize,
							    """encoding_errors""": self.encoding_errors,
							    """on_bad_lines""": self.on_bad_lines,
							    """date_format""": self.date_format,
							}

							# some kwargs must not be passed if they don't have a default value
							# some others are deprecated and we can also not pass them if they are the default value
							for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
													if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()						,  __UpperCAmelCase					):
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
							if not (datasets.config.PANDAS_VERSION.major >= 2):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
							if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

							return pd_read_csv_kwargs






class 		lowercase						(			datasets.ArrowBasedBuilder):






	"""simple docstring"""


	a__				: int		       =							CsvConfig




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							return datasets.DatasetInfo(features=self.config.features					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Dict					)      ->			Optional[int]:
							if not self.config.data_files:
													raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""					)
							UpperCAmelCase_=       dl_manager.download_and_extract(self.config.data_files					)
							if isinstance(__UpperCAmelCase						,  (str, list, tuple)					):
													UpperCAmelCase_=       data_files
													if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       [files]
													UpperCAmelCase_=       [dl_manager.iter_files(__UpperCAmelCase					) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,  gen_kwargs={"""files""": files}					)]
							UpperCAmelCase_=       []
							for split_name, files in data_files.items():
													if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       [files]
													UpperCAmelCase_=       [dl_manager.iter_files(__UpperCAmelCase					) for file in files]
													splits.append(datasets.SplitGenerator(name=__UpperCAmelCase						,  gen_kwargs={"""files""": files}					)					)
							return splits




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						pa.Table					)      ->			pa.Table:
							if self.config.features is not None:
													UpperCAmelCase_=       self.config.features.arrow_schema
													if all(not require_storage_cast(__UpperCAmelCase					) for feature in self.config.features.values()					):
																			# cheaper cast
																			UpperCAmelCase_=       pa.Table.from_arrays([pa_table[field.name] for field in schema]						,  schema=__UpperCAmelCase					)
													else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			UpperCAmelCase_=       table_cast(__UpperCAmelCase						,  __UpperCAmelCase					)
							return pa_table




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							UpperCAmelCase_=       self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							UpperCAmelCase_=       (
							    {
							        name: dtype.to_pandas_dtype() if not require_storage_cast(__UpperCAmelCase					) else object
							        for name, dtype, feature in zip(schema.names						,  schema.types						,  self.config.features.values()					)
							    }
							    if schema is not None
							    else None
							)
							for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase					)					):
													UpperCAmelCase_=       pd.read_csv(__UpperCAmelCase						,  iterator=__UpperCAmelCase						,  dtype=__UpperCAmelCase						,  **self.config.pd_read_csv_kwargs					)
													try:
																			for batch_idx, df in enumerate(__UpperCAmelCase					):
																									UpperCAmelCase_=       pa.Table.from_pandas(__UpperCAmelCase					)
																									# Uncomment for debugging (will print the Arrow table size and elements)
																									# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																									# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																									yield (file_idx, batch_idx), self._cast_table(__UpperCAmelCase					)
													except ValueError as e:
																			logger.error(F"""Failed to read file '{file}' with error {type(__UpperCAmelCase					)}: {e}"""					)
																			raise




