
"""simple docstring"""






import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[Any]   	=np.inf

 def set_batch_size(__lowercase	) -> None:
  nonlocal batch_size
  if isinstance(lowercase_						,	lowercase_	):
   UpperCamelCase_     :					Optional[int]   	=min(lowercase_						,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS	)
  elif isinstance(lowercase_						,	lowercase_	):
   UpperCamelCase_     :					Dict   	=min(lowercase_						,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS	)
  elif isinstance(lowercase_						,	lowercase_	) and feature.dtype == "binary":
   UpperCamelCase_     :					List[Any]   	=min(lowercase_						,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS	)

 _visit(lowercase_						,	lowercase_	)

 return None if batch_size is np.inf else batch_size





class 				a__					(						_UpperCamelCase       ):



     def __init__(							self :Union[str, Any]      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :List[str] = None      ,  _lowerCamelCase :Optional[Any] = None      ,  _lowerCamelCase :List[Any] = None      ,  _lowerCamelCase :List[str] = False      ,  _lowerCamelCase :List[str] = False      ,  _lowerCamelCase :int = None      ,  **_lowerCamelCase :List[str]      ,  ):







      '''simple docstring'''



      super().__init__(
          lowercase__      ,  split=lowercase__      ,  features=lowercase__      ,  cache_dir=lowercase__      ,  keep_in_memory=lowercase__      ,  streaming=lowercase__      ,  num_proc=lowercase__      ,  **lowercase__      ,  )
      UpperCamelCase_     :					int   	=path_or_paths if isinstance(lowercase__      ,  lowercase__   ) else {self.split: path_or_paths}
      UpperCamelCase_     :					Optional[int]   	=_PACKAGED_DATASETS_MODULES['''parquet'''][1]
      UpperCamelCase_     :					Optional[Any]   	=Parquet(
          cache_dir=lowercase__      ,  data_files=lowercase__      ,  features=lowercase__      ,  hash=lowercase__      ,  **lowercase__      ,  )




     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      if self.streaming:
       UpperCamelCase_     :					int   	=self.builder.as_streaming_dataset(split=self.split   )
      # Build regular (map-style) dataset
      else:
       UpperCamelCase_     :					Optional[int]   	=None
       UpperCamelCase_     :					List[Any]   	=None
       UpperCamelCase_     :					Tuple   	=None
       UpperCamelCase_     :					Union[str, Any]   	=None

       self.builder.download_and_prepare(
           download_config=lowercase__      ,  download_mode=lowercase__      ,  verification_mode=lowercase__      ,  base_path=lowercase__      ,  num_proc=self.num_proc      ,  )
       UpperCamelCase_     :					Optional[Any]   	=self.builder.as_dataset(
           split=self.split      ,  verification_mode=lowercase__      ,  in_memory=self.keep_in_memory   )
      return dataset





class 				a__					:



     def __init__(							self :Optional[int]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[int]      ,  _lowerCamelCase :Tuple = None      ,  **_lowerCamelCase :Union[str, Any]      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=dataset
      UpperCamelCase_     :					str   	=path_or_buf
      UpperCamelCase_     :					int   	=batch_size or get_writer_batch_size(dataset.features   )
      UpperCamelCase_     :					List[str]   	=parquet_writer_kwargs



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

      if isinstance(self.path_or_buf      ,  (str, bytes, os.PathLike)   ):
       with open(self.path_or_buf      ,  'wb+'   ) as buffer:
        UpperCamelCase_     :					int   	=self._write(file_obj=lowercase__      ,  batch_size=lowercase__      ,  **self.parquet_writer_kwargs   )
      else:
       UpperCamelCase_     :					str   	=self._write(file_obj=self.path_or_buf      ,  batch_size=lowercase__      ,  **self.parquet_writer_kwargs   )
      return written




     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :Any      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=0
      UpperCamelCase_     :					str   	=parquet_writer_kwargs.pop('path_or_buf'      ,  lowercase__   )
      UpperCamelCase_     :					Tuple   	=self.dataset.features.arrow_schema

      UpperCamelCase_     :					Optional[Any]   	=pq.ParquetWriter(lowercase__      ,  schema=lowercase__      ,  **lowercase__   )

      for offset in logging.tqdm(
          range(0      ,  len(self.dataset   )      ,  lowercase__   )      ,  unit='ba'      ,  disable=not logging.is_progress_bar_enabled()      ,  desc='Creating parquet from Arrow format'      ,  ):
       UpperCamelCase_     :					Optional[Any]   	=query_table(
           table=self.dataset._data      ,  key=slice(lowercase__      ,  offset + batch_size   )      ,  indices=self.dataset._indices if self.dataset._indices is not None else None      ,  )
       writer.write_table(lowercase__   )
       written += batch.nbytes
      writer.close()
      return written




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


lowerCAmelCase       		=    16
lowerCAmelCase       		=    32




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = 16      ,       lowercase_ = "bert-base-cased"      )		->     Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							List[str]	    =	AutoTokenizer.from_pretrained(lowercase_      )
						__UpperCAmelCase			:							int	    =	load_dataset('''glue'''      ,       '''mrpc'''      )

						def tokenize_function(lowercase_      ):
												# max_length=None => use the model max length (it's actually the default)
												__UpperCAmelCase			:							List[Any]	    =	tokenizer(examples['''sentence1''']      ,       examples['''sentence2''']      ,       truncation=lowercase_      ,       max_length=lowercase_      )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						__UpperCAmelCase			:							Dict	    =	datasets.map(
						    lowercase_      ,       batched=lowercase_      ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']      ,       load_from_cache_file=lowercase_      )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__UpperCAmelCase			:							List[str]	    =	tokenized_datasets.rename_column('''label'''      ,       '''labels'''      )

						def collate_fn(lowercase_      ):
												# 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(lowercase_      ,       padding='''max_length'''      ,       max_length=128      ,       return_tensors='''pt'''      )
												return tokenizer.pad(lowercase_      ,       padding='''longest'''      ,       return_tensors='''pt'''      )

						# Instantiate dataloaders.
						__UpperCAmelCase			:							Optional[Any]	    =	DataLoader(
						    tokenized_datasets['''train''']      ,       shuffle=lowercase_      ,       collate_fn=lowercase_      ,       batch_size=lowercase_      )
						__UpperCAmelCase			:							Any	    =	DataLoader(
						    tokenized_datasets['''validation''']      ,       shuffle=lowercase_      ,       collate_fn=lowercase_      ,       batch_size=lowercase_      )

						return train_dataloader, eval_dataloader




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     str:




						'''simple docstring'''







						model.eval()
						__UpperCAmelCase			:							Optional[int]	    =	0
						for step, batch in enumerate(lowercase_      ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device      )
												with torch.no_grad():
																		__UpperCAmelCase			:							Any	    =	model(**lowercase_      )
												__UpperCAmelCase			:							Optional[int]	    =	outputs.logits.argmax(dim=-1      )
												# It is slightly faster to call this once, than multiple times
												__UpperCAmelCase    ,      __UpperCAmelCase			:							Union[str, Any]	    =	accelerator.gather(
												    (predictions, batch['''labels'''])      )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(lowercase_      ) - 1:
																								__UpperCAmelCase			:							Optional[int]	    =	predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																								__UpperCAmelCase			:							List[Any]	    =	references[: len(eval_dataloader.dataset      ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=lowercase_      ,       references=lowercase_      ,       )

						__UpperCAmelCase			:							Union[str, Any]	    =	metric.compute()
						return eval_metric["accuracy"]




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     Dict:




						'''simple docstring'''







						__UpperCAmelCase			:							List[Any]	    =	Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__UpperCAmelCase			:							Optional[Any]	    =	config['''lr''']
						__UpperCAmelCase			:							List[Any]	    =	int(config['''num_epochs''']      )
						__UpperCAmelCase			:							Dict	    =	int(config['''seed''']      )
						__UpperCAmelCase			:							Tuple	    =	int(config['''batch_size''']      )
						__UpperCAmelCase			:							Any	    =	args.model_name_or_path

						set_seed(lowercase_      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	get_dataloaders(lowercase_      ,       lowercase_      ,       lowercase_      )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCAmelCase			:							List[str]	    =	AutoModelForSequenceClassification.from_pretrained(lowercase_      ,       return_dict=lowercase_      )

						# Instantiate optimizer
						__UpperCAmelCase			:							str	    =	(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						__UpperCAmelCase			:							int	    =	optimizer_cls(params=model.parameters()      ,       lr=lowercase_      )

						if accelerator.state.deepspeed_plugin is not None:
												__UpperCAmelCase			:							int	    =	accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												__UpperCAmelCase			:							List[Any]	    =	1
						__UpperCAmelCase			:							str	    =	(len(lowercase_      ) * 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			:							List[str]	    =	get_linear_schedule_with_warmup(
												    optimizer=lowercase_      ,       num_warmup_steps=0      ,       num_training_steps=lowercase_      ,       )
						else:
												__UpperCAmelCase			:							Union[str, Any]	    =	DummyScheduler(lowercase_      ,       total_num_steps=lowercase_      ,       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			:							int	    =	accelerator.prepare(
						    lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )

						# We need to keep track of how many total steps we have iterated over
						__UpperCAmelCase			:							Tuple	    =	0
						# We also need to keep track of the stating epoch so files are named properly
						__UpperCAmelCase			:							Any	    =	0
						__UpperCAmelCase			:							Tuple	    =	evaluate.load('''glue'''      ,       '''mrpc'''      )
						__UpperCAmelCase			:							Any	    =	num_epochs

						if args.partial_train_epoch is not None:
												__UpperCAmelCase			:							Any	    =	args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint      )
												__UpperCAmelCase			:							List[Any]	    =	args.resume_from_checkpoint.split('''epoch_'''      )[1]
												__UpperCAmelCase			:							Union[str, Any]	    =	''''''
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												__UpperCAmelCase			:							Tuple	    =	int(lowercase_      ) + 1
												__UpperCAmelCase			:							Optional[int]	    =	evaluation_loop(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )
												accelerator.print('''resumed checkpoint performance:'''      ,       lowercase_      )
												accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''      ,       lr_scheduler.get_lr()[0]      )
												accelerator.print('''resumed optimizers\'s lr:'''      ,       optimizer.param_groups[0]['''lr''']      )
												with open(os.path.join(args.output_dir      ,       f"state_{starting_epoch-1}.json"      )      ,       '''r'''      ) as f:
																		__UpperCAmelCase			:							str	    =	json.load(lowercase_      )
																		assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																		), "Scheduler learning rate mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																		), "Optimizer learning rate mismatch, loading from checkpoint failed"
																		assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																		return

    # Now we train the model
						__UpperCAmelCase			:							Tuple	    =	{}
						for epoch in range(lowercase_      ,       lowercase_      ):
												model.train()
												for step, batch in enumerate(lowercase_      ):
																		__UpperCAmelCase			:							Dict	    =	model(**lowercase_      )
																		__UpperCAmelCase			:							Union[str, Any]	    =	outputs.loss
																		__UpperCAmelCase			:							Tuple	    =	loss / gradient_accumulation_steps
																		accelerator.backward(lowercase_      )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												__UpperCAmelCase			:							Optional[Any]	    =	f"epoch_{epoch}"
												__UpperCAmelCase			:							Optional[Any]	    =	os.path.join(args.output_dir      ,       lowercase_      )
												accelerator.save_state(lowercase_      )
												__UpperCAmelCase			:							Any	    =	evaluation_loop(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )
												__UpperCAmelCase			:							List[str]	    =	accuracy
												__UpperCAmelCase			:							Dict	    =	lr_scheduler.get_lr()[0]
												__UpperCAmelCase			:							List[str]	    =	optimizer.param_groups[0]['''lr''']
												__UpperCAmelCase			:							List[Any]	    =	epoch
												__UpperCAmelCase			:							Any	    =	overall_step
												accelerator.print(f"epoch {epoch}:"      ,       lowercase_      )

												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		with open(os.path.join(args.output_dir      ,       f"state_{epoch}.json"      )      ,       '''w'''      ) as f:
																								json.dump(lowercase_      ,       lowercase_      )




def     __SCREAMING_SNAKE_CASE							(       )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Any	    =	argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''      )
						parser.add_argument(
						    '''--model_name_or_path'''      ,       type=lowercase_      ,       default='''bert-base-cased'''      ,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''      ,       required=lowercase_      ,       )
						parser.add_argument(
						    '''--output_dir'''      ,       type=lowercase_      ,       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=lowercase_      ,       default=lowercase_      ,       help='''If the training should continue from a checkpoint folder.'''      ,       )
						parser.add_argument(
						    '''--partial_train_epoch'''      ,       type=lowercase_      ,       default=lowercase_      ,       help='''If passed, the training will stop after this number of epochs.'''      ,       )
						parser.add_argument(
						    '''--num_epochs'''      ,       type=lowercase_      ,       default=2      ,       help='''Number of train epochs.'''      ,       )
						__UpperCAmelCase			:							Optional[int]	    =	parser.parse_args()
						__UpperCAmelCase			:							str	    =	{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

						training_function(lowercase_      ,       lowercase_      )


if __name__ == "__main__":
				main()

