






'''simple docstring'''







import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


__lowerCAmelCase									=   16
__lowerCAmelCase									=   32





def 		_UpperCAmelCase (				__A				: Accelerator    ,		__A				: int = 16   ):
				a_		:	str									=   AutoTokenizer.from_pretrained('''bert-base-cased'''   )
				a_		:	str									=   load_dataset('''glue'''    ,		'''mrpc'''   )

				def tokenize_function(__A				: List[str]   ):
								# max_length=None => use the model max length (it's actually the default)
								a_		:	int									=   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_		:	List[str]									=   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_		:	Any									=   tokenized_datasets.rename_column('''label'''    ,		'''labels'''   )

				def collate_fn(__A				: List[str]   ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								a_		:	Tuple									=   1_28 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_		:	Dict									=   16
								elif accelerator.mixed_precision != "no":
												a_		:	int									=   8
								else:
												a_		:	Dict									=   None

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

				# Instantiate dataloaders.
				a_		:	Optional[int]									=   DataLoader(
				    tokenized_datasets['''train''']    ,		shuffle=__A    ,		collate_fn=__A    ,		batch_size=__A   )
				a_		:	List[Any]									=   DataLoader(
				    tokenized_datasets['''validation''']    ,		shuffle=__A    ,		collate_fn=__A    ,		batch_size=__A   )

				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

		__lowerCAmelCase									=   mocked_dataloaders  # noqa: F811





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

				a_		:	List[str]									=   evaluate.load('''glue'''    ,		'''mrpc'''   )

				# New Code #
				# We now can define an inner training loop function. It should take a batch size as the only parameter,
				# and build the dataloaders in there.
				# It also gets our decorator
				@find_executable_batch_size(starting_batch_size=__A   )
				def inner_training_loop(__A				: List[Any]   ):
								# And now just move everything below under this function
								# We need to bring in the Accelerator object from earlier
								nonlocal accelerator
								# And reset all of its attributes that could hold onto any memory:
								accelerator.free_memory()

								# Then we can declare the model, optimizer, and everything else:
								set_seed(__A   )

								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								a_		:	int									=   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_		:	Optional[Any]									=   model.to(accelerator.device   )

								# Instantiate optimizer
								a_		:	Optional[Any]									=   AdamW(params=model.parameters()    ,		lr=__A   )
								a_					,      a_		:	str									=   get_dataloaders(__A    ,		__A   )

								# Instantiate scheduler
								a_		:	List[str]									=   get_linear_schedule_with_warmup(
								    optimizer=__A    ,		num_warmup_steps=1_00    ,		num_training_steps=(len(__A   ) * num_epochs)    ,		)

								# 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_		:	Optional[Any]									=   accelerator.prepare(
								    __A    ,		__A    ,		__A    ,		__A    ,		__A   )

								# Now we train the model
								for epoch in range(__A   ):
												model.train()
												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_		:	Optional[Any]									=   model(**__A   )
																a_		:	Any									=   outputs.loss
																accelerator.backward(__A   )
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												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_		:	Dict									=   model(**__A   )
																a_		:	Tuple									=   outputs.logits.argmax(dim=-1   )
																a_					,      a_		:	int									=   accelerator.gather_for_metrics((predictions, batch['''labels'''])   )
																metric.add_batch(
																    predictions=__A    ,		references=__A    ,		)

												a_		:	Dict									=   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'epoch {epoch}:'    ,		__A   )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
				inner_training_loop()





def 		_UpperCAmelCase (				):
				a_		:	List[str]									=   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.'''   )
				a_		:	int									=   parser.parse_args()
				a_		:	Any									=   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__A    ,		__A   )


if __name__ == "__main__":
		main()












'''simple docstring'''







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


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

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





@dataclass
class 						SCREAMING_SNAKE_CASE       (       datasets.BuilderConfig							):
			snake_case__	=					","
			snake_case__	=					None
			snake_case__	=					"infer"
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					True
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					False
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					True
			snake_case__	=					True
			snake_case__	=					False
			snake_case__	=					True
			snake_case__	=					None
			snake_case__	=					"."
			snake_case__	=					None
			snake_case__	=					'"'
			snake_case__	=					0
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					True
			snake_case__	=					True
			snake_case__	=					0
			snake_case__	=					True
			snake_case__	=					False
			snake_case__	=					None
			snake_case__	=					10000
			snake_case__	=					None
			snake_case__	=					"strict"
			snake_case__	=					"error"
			snake_case__	=					None






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







			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Optional[Any]:
							a_		:	List[Any]									=   {
							    '''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()	,    __SCREAMING_SNAKE_CASE       ):
															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 						SCREAMING_SNAKE_CASE       (       datasets.ArrowBasedBuilder							):
			snake_case__	=					CsvConfig






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






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Any:
							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}'       )
							a_		:	Tuple									=   dl_manager.download_and_extract(self.config.data_files       )
							if isinstance(__SCREAMING_SNAKE_CASE	,    (str, list, tuple)       ):
											a_		:	int									=   data_files
											if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
															a_		:	Optional[Any]									=   [files]
											a_		:	Optional[Any]									=   [dl_manager.iter_files(__SCREAMING_SNAKE_CASE       ) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,    gen_kwargs={'''files''': files}       )]
							a_		:	List[str]									=   []
							for split_name, files in data_files.items():
											if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
															a_		:	int									=   [files]
											a_		:	Union[str, Any]									=   [dl_manager.iter_files(__SCREAMING_SNAKE_CASE       ) for file in files]
											splits.append(datasets.SplitGenerator(name=__SCREAMING_SNAKE_CASE	,    gen_kwargs={'''files''': files}       )       )
							return splits






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







			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      Any       )      ->      Optional[int]:
							a_		:	Union[str, Any]									=   self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							a_		:	Optional[int]									=   (
							    {
							        name: dtype.to_pandas_dtype() if not require_storage_cast(__SCREAMING_SNAKE_CASE       ) 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(__SCREAMING_SNAKE_CASE       )       ):
											a_		:	Tuple									=   pd.read_csv(__SCREAMING_SNAKE_CASE	,    iterator=__SCREAMING_SNAKE_CASE	,    dtype=__SCREAMING_SNAKE_CASE	,    **self.config.pd_read_csv_kwargs       )
											try:
															for batch_idx, df in enumerate(__SCREAMING_SNAKE_CASE       ):
																			a_		:	Optional[int]									=   pa.Table.from_pandas(__SCREAMING_SNAKE_CASE       )
																			# 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(__SCREAMING_SNAKE_CASE       )
											except ValueError as e:
															logger.error(f'Failed to read file \'{file}\' with error {type(__SCREAMING_SNAKE_CASE       )}: {e}'       )
															raise




