


'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__snake_case				 =	datasets.utils.logging.get_logger(__name__)
@dataclass
class   lowercase      (				datasets.BuilderConfig   ):

		"""simple docstring"""

		_a       					=							None
		_a       					=							"utf-8"
		_a       					=							None
		_a       					=							None
		_a       					=							True  # deprecated
		_a       					=							None  # deprecated
		_a       					=							10 << 20  # 10MB
		_a       					=							None



class   lowercase      (				datasets.ArrowBasedBuilder   ):

		"""simple docstring"""

		_a       					=							JsonConfig




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if self.config.block_size is not None:
												logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''				)
												UpperCamelCase__  :Any				   =					self.config.block_size
							if self.config.use_threads is not True:
												logger.warning(
												    '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''				)
							if self.config.newlines_in_values is not None:
												raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''				)
							return datasets.DatasetInfo(features=self.config.features				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							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__  :Optional[Any]				   =					dl_manager.download_and_extract(self.config.data_files				)
							if isinstance(UpperCamelCase_       ,  (str, list, tuple)				):
												UpperCamelCase__  :int				   =					data_files
												if isinstance(UpperCamelCase_       ,  UpperCamelCase_				):
																	UpperCamelCase__  :int				   =					[files]
												UpperCamelCase__  :Any				   =					[dl_manager.iter_files(UpperCamelCase_				) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,  gen_kwargs={'''files''': files}				)]
							UpperCamelCase__  :Optional[Any]				   =					[]
							for split_name, files in data_files.items():
												if isinstance(UpperCamelCase_       ,  UpperCamelCase_				):
																	UpperCamelCase__  :Optional[Any]				   =					[files]
												UpperCamelCase__  :str				   =					[dl_manager.iter_files(UpperCamelCase_				) for file in files]
												splits.append(datasets.SplitGenerator(name=UpperCamelCase_       ,  gen_kwargs={'''files''': files}				)				)
							return splits




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if self.config.features is not None:
												# adding missing columns
												for column_name in set(self.config.features				) - set(pa_table.column_names				):
																	UpperCamelCase__  :List[str]				   =					self.config.features.arrow_schema.field(UpperCamelCase_				).type
																	UpperCamelCase__  :Optional[Any]				   =					pa_table.append_column(UpperCamelCase_       ,  pa.array([None] * len(UpperCamelCase_				)       ,  type=UpperCamelCase_				)				)
												# more expensive cast to support nested structures with keys in a different order
												# allows str <-> int/float or str to Audio for example
												UpperCamelCase__  :Tuple				   =					table_cast(UpperCamelCase_       ,  self.config.features.arrow_schema				)
							return pa_table




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_				)				):
												# If the file is one json object and if we need to look at the list of items in one specific field
												if self.config.field is not None:
																	with open(UpperCamelCase_       ,  encoding=self.config.encoding       ,  errors=self.config.encoding_errors				) as f:
																						UpperCamelCase__  :Any				   =					json.load(UpperCamelCase_				)

																	# We keep only the field we are interested in
																	UpperCamelCase__  :Union[str, Any]				   =					dataset[self.config.field]

																	# We accept two format: a list of dicts or a dict of lists
																	if isinstance(UpperCamelCase_       ,  (list, tuple)				):
																						UpperCamelCase__  :Any				   =					set().union(*[row.keys() for row in dataset]				)
																						UpperCamelCase__  :List[str]				   =					{col: [row.get(UpperCamelCase_				) for row in dataset] for col in keys}
																	else:
																						UpperCamelCase__  :Optional[int]				   =					dataset
																	UpperCamelCase__  :Optional[Any]				   =					pa.Table.from_pydict(UpperCamelCase_				)
																	yield file_idx, self._cast_table(UpperCamelCase_				)

												# If the file has one json object per line
												else:
																	with open(UpperCamelCase_       ,  '''rb'''				) as f:
																						UpperCamelCase__  :int				   =					0
																						# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																						# Set a default minimum value of 16kB if the chunk size is really small
																						UpperCamelCase__  :Tuple				   =					max(self.config.chunksize // 32       ,  16 << 10				)
																						UpperCamelCase__  :int				   =					(
																						    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																						)
																						while True:
																											UpperCamelCase__  :str				   =					f.read(self.config.chunksize				)
																											if not batch:
																																break
																											# Finish current line
																											try:
																																batch += f.readline()
																											except (AttributeError, io.UnsupportedOperation):
																																batch += readline(UpperCamelCase_				)
																											# PyArrow only accepts utf-8 encoded bytes
																											if self.config.encoding != "utf-8":
																																UpperCamelCase__  :List[str]				   =					batch.decode(self.config.encoding       ,  errors=UpperCamelCase_				).encode('''utf-8'''				)
																											try:
																																while True:
																																					try:
																																										UpperCamelCase__  :int				   =					paj.read_json(
																																										    io.BytesIO(UpperCamelCase_				)       ,  read_options=paj.ReadOptions(block_size=UpperCamelCase_				)				)
																																										break
																																					except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																										if (
																																										    isinstance(UpperCamelCase_       ,  pa.ArrowInvalid				)
																																										    and "straddling" not in str(UpperCamelCase_				)
																																										    or block_size > len(UpperCamelCase_				)
																																										):
																																															raise
																																										else:
																																															# Increase the block size in case it was too small.
																																															# The block size will be reset for the next file.
																																															logger.debug(
																																															    F'''Batch of {len(UpperCamelCase_				)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''				)
																																															block_size *= 2
																											except pa.ArrowInvalid as e:
																																try:
																																					with open(
																																					    UpperCamelCase_       ,  encoding=self.config.encoding       ,  errors=self.config.encoding_errors				) as f:
																																										UpperCamelCase__  :int				   =					json.load(UpperCamelCase_				)
																																except json.JSONDecodeError:
																																					logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCamelCase_				)}: {e}'''				)
																																					raise e
																																# If possible, parse the file as a list of json objects and exit the loop
																																if isinstance(UpperCamelCase_       ,  UpperCamelCase_				):  # list is the only sequence type supported in JSON
																																					try:
																																										UpperCamelCase__  :Any				   =					set().union(*[row.keys() for row in dataset]				)
																																										UpperCamelCase__  :Dict				   =					{col: [row.get(UpperCamelCase_				) for row in dataset] for col in keys}
																																										UpperCamelCase__  :Dict				   =					pa.Table.from_pydict(UpperCamelCase_				)
																																					except (pa.ArrowInvalid, AttributeError) as e:
																																										logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCamelCase_				)}: {e}'''				)
																																										raise ValueError(F'''Not able to read records in the JSON file at {file}.'''				) from None
																																					yield file_idx, self._cast_table(UpperCamelCase_				)
																																					break
																																else:
																																					logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCamelCase_				)}: {e}'''				)
																																					raise ValueError(
																																					    F'''Not able to read records in the JSON file at {file}. '''
																																					    F'''You should probably indicate the field of the JSON file containing your records. '''
																																					    F'''This JSON file contain the following fields: {str(list(dataset.keys()				)				)}. '''
																																					    F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''				) from None
                        # 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_				)
																											batch_idx += 1



'''simple docstring'''
import argparse

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)
#
# 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
#
########################################################################


__snake_case				 =	16
__snake_case				 =	32


def      a (     __a					,   __a = 16  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					AutoTokenizer.from_pretrained('''bert-base-cased'''  )
					UpperCamelCase__  :Optional[Any]				   =					load_dataset('''glue'''					,   '''mrpc'''  )

					def tokenize_function(__a  ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase__  :Any				   =					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():
										UpperCamelCase__  :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
					UpperCamelCase__  :Optional[int]				   =					tokenized_datasets.rename_column('''label'''					,   '''labels'''  )

					def collate_fn(__a  ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										UpperCamelCase__  :Union[str, Any]				   =					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":
															UpperCamelCase__  :str				   =					16
										elif accelerator.mixed_precision != "no":
															UpperCamelCase__  :Tuple				   =					8
										else:
															UpperCamelCase__  :int				   =					None

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

					# Instantiate dataloaders.
					UpperCamelCase__  :Any				   =					DataLoader(
					    tokenized_datasets['''train''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a					,   drop_last=__a  )
					UpperCamelCase__  :Any				   =					DataLoader(
					    tokenized_datasets['''validation''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a					,   drop_last=(accelerator.mixed_precision == '''fp8''')					,   )

					return train_dataloader, eval_dataloader


def      a (     __a					,   __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :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
					UpperCamelCase__  :List[Any]				   =					config['''lr''']
					UpperCamelCase__  :List[str]				   =					int(config['''num_epochs''']  )
					UpperCamelCase__  :int				   =					int(config['''seed''']  )
					UpperCamelCase__  :str				   =					int(config['''batch_size''']  )

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

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

					set_seed(__a  )
					UpperCamelCase__   ,							UpperCamelCase__  :Tuple				   =					get_dataloaders(__a					,   __a  )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__  :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).
					UpperCamelCase__  :Optional[int]				   =					model.to(accelerator.device  )
					# Instantiate optimizer
					UpperCamelCase__  :Union[str, Any]				   =					AdamW(params=model.parameters()					,   lr=__a  )

					# Instantiate scheduler
					UpperCamelCase__  :Optional[Any]				   =					get_linear_schedule_with_warmup(
					    optimizer=__a					,   num_warmup_steps=100					,   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.

					UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__  :str				   =					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  )
															UpperCamelCase__  :Dict				   =					model(**__a  )
															UpperCamelCase__  :Union[str, Any]				   =					outputs.loss
															UpperCamelCase__  :Tuple				   =					loss / gradient_accumulation_steps
															accelerator.backward(__a  )
															if step % gradient_accumulation_steps == 0:
																				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():
																				UpperCamelCase__  :Optional[int]				   =					model(**__a  )
															UpperCamelCase__  :int				   =					outputs.logits.argmax(dim=-1  )
															UpperCamelCase__   ,							UpperCamelCase__  :List[Any]				   =					accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
															metric.add_batch(
															    predictions=__a					,   references=__a					,   )

										UpperCamelCase__  :List[str]				   =					metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}:'''					,   __a  )


def      a (     )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :Union[str, 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.'''  )
					UpperCamelCase__  :int				   =					parser.parse_args()
					UpperCamelCase__  :int				   =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__a					,   __a  )


if __name__ == "__main__":
		main()