


"""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


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

__snake_case         =					["""names""", """prefix"""]
__snake_case         =					["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
__snake_case         =					["""encoding_errors""", """on_bad_lines"""]
__snake_case         =					["""date_format"""]







@dataclass
class _lowerCAmelCase       (   datasets.BuilderConfig			):
   __UpperCAmelCase				: str													=						","
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: Optional[Union[int, List[int], str]]													=						"infer"
   __UpperCAmelCase				: Optional[List[str]]													=						None
   __UpperCAmelCase				: Optional[List[str]]													=						None
   __UpperCAmelCase				: Optional[Union[int, str, List[int], List[str]]]													=						None
   __UpperCAmelCase				: Optional[Union[List[int], List[str]]]													=						None
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: Optional[Literal["c", "python", "pyarrow"]]													=						None
   __UpperCAmelCase				: Dict[Union[int, str], Callable[[Any], Any]]													=						None
   __UpperCAmelCase				: Optional[list]													=						None
   __UpperCAmelCase				: Optional[list]													=						None
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: Optional[Union[int, List[int]]]													=						None
   __UpperCAmelCase				: Optional[int]													=						None
   __UpperCAmelCase				: Optional[Union[str, List[str]]]													=						None
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: str													=						"."
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: str													=						'"'
   __UpperCAmelCase				: int													=						0
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: int													=						0
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: int													=						10000
   __UpperCAmelCase				: Optional[datasets.Features]													=						None
   __UpperCAmelCase				: Optional[str]													=						"strict"
   __UpperCAmelCase				: Literal["error", "warn", "skip"]													=						"error"
   __UpperCAmelCase				: Optional[str]													=						None







   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       if self.delimiter is not None:
           snake_case					:   Optional[Any]         =					self.delimiter
       if self.column_names is not None:
           snake_case					:   Dict         =					self.column_names







   @property
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					{
           "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 _lowerCAmelCase       (   datasets.ArrowBasedBuilder			):
   __UpperCAmelCase				: Union[str, Any]													=						CsvConfig







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       return datasets.DatasetInfo(features=self.config.features		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[int]:

       '''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}'		)
       snake_case					:   Optional[int]         =					dl_manager.download_and_extract(self.config.data_files		)
       if isinstance(UpperCamelCase__		,			(str, list, tuple)		):
           snake_case					:   List[str]         =					data_files
           if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
               snake_case					:   Dict         =					[files]
           snake_case					:   List[Any]         =					[dl_manager.iter_files(UpperCamelCase__		) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,			gen_kwargs={"files": files}		)]
       snake_case					:   Optional[int]         =					[]
       for split_name, files in data_files.items():
           if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
               snake_case					:   Any         =					[files]
           snake_case					:   Optional[int]         =					[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__		)			->	pa.Table:

       '''simple docstring'''




       if self.config.features is not None:
           snake_case					:   Any         =					self.config.features.arrow_schema
           if all(not require_storage_cast(UpperCamelCase__		) for feature in self.config.features.values()		):
               # cheaper cast
               snake_case					:   Any         =					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
               snake_case					:   Union[str, Any]         =					table_cast(UpperCamelCase__		,			UpperCamelCase__		)
       return pa_table







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					self.config.features.arrow_schema if self.config.features else None
       # dtype allows reading an int column as str
       snake_case					:   Tuple         =					(
           {
               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__		)		):
           snake_case					:   Optional[Any]         =					pd.read_csv(UpperCamelCase__		,			iterator=UpperCamelCase__		,			dtype=UpperCamelCase__		,			**self.config.pd_read_csv_kwargs		)
           try:
               for batch_idx, df in enumerate(UpperCamelCase__		):
                   snake_case					:   List[str]         =					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









"""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 experiment tracking 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
#
########################################################################

__snake_case         =					16
__snake_case         =					32







def 						__lowerCAmelCase		(		lowercase						:						Accelerator		,							lowercase						:						int = 16				)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   int         =					AutoTokenizer.from_pretrained("bert-base-cased"				)
    snake_case					:   str         =					load_dataset("glue"		,							"mrpc"				)

    def tokenize_function(lowercase						:						Tuple				):
        # max_length=None => use the model max length (it's actually the default)
        snake_case					:   List[str]         =					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
    # starting with the main process first:
    with accelerator.main_process_first():
        snake_case					:   Any         =					datasets.map(
            lowercase		,							batched=lowercase		,							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					:   Tuple         =					tokenized_datasets.rename_column("label"		,							"labels"				)

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

        return tokenizer.pad(
            lowercase		,							padding="longest"		,							max_length=lowercase		,							pad_to_multiple_of=lowercase		,							return_tensors="pt"		,							)

    # Instantiate dataloaders.
    snake_case					:   Any         =					DataLoader(
        tokenized_datasets["train"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)
    snake_case					:   List[str]         =					DataLoader(
        tokenized_datasets["validation"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)

    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

  __snake_case         =					mocked_dataloaders  # noqa: F811







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						int				)			->		Tuple:


    """simple docstring"""





    if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							lowercase				) == "1":
        snake_case					:   Union[str, Any]         =					2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        snake_case					:   str         =					Accelerator(
            cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with="all"		,							project_dir=args.project_dir				)
    else:
        snake_case					:   Optional[int]         =					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					:   str         =					config["lr"]
    snake_case					:   Dict         =					int(config["num_epochs"]				)
    snake_case					:   int         =					int(config["seed"]				)
    snake_case					:   Tuple         =					int(config["batch_size"]				)
    set_seed(lowercase				)

    snake_case      ,snake_case					:   List[Any]         =					get_dataloaders(lowercase		,							lowercase				)
    snake_case					:   Optional[Any]         =					evaluate.load("glue"		,							"mrpc"				)

    # If the batch size is too big we use gradient accumulation
    snake_case					:   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					:   int         =					MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case					:   List[Any]         =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,							return_dict=lowercase				)

    # 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					:   str         =					model.to(accelerator.device				)

    # Instantiate optimizer
    snake_case					:   Tuple         =					AdamW(params=model.parameters()		,							lr=lowercase				)

    # Instantiate scheduler
    snake_case					:   Optional[Any]         =					get_linear_schedule_with_warmup(
        optimizer=lowercase		,							num_warmup_steps=100		,							num_training_steps=(len(lowercase				) * 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					:   Union[str, Any]         =					accelerator.prepare(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        snake_case					:   str         =					os.path.split(lowercase				)[-1].split("."				)[0]
        accelerator.init_trackers(lowercase		,							lowercase				)

    # Now we train the model
    for epoch in range(lowercase				):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            snake_case					:   Any         =					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				)
            snake_case					:   Dict         =					model(**lowercase				)
            snake_case					:   int         =					outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            snake_case					:   Union[str, Any]         =					loss / gradient_accumulation_steps
            accelerator.backward(lowercase				)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        for step, batch in enumerate(lowercase				):
            # 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					:   Optional[int]         =					model(**lowercase				)
            snake_case					:   Tuple         =					outputs.logits.argmax(dim=-1				)
            snake_case      ,snake_case					:   List[str]         =					accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=lowercase		,							references=lowercase		,							)

        snake_case					:   Optional[Any]         =					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'		,							lowercase				)

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": eval_metric["accuracy"],
                    "f1": eval_metric["f1"],
                    "train_loss": total_loss.item() / len(lowercase				),
                    "epoch": epoch,
                }		,							step=lowercase		,							)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()







def 						__lowerCAmelCase		(		)			->		str:


    """simple docstring"""





    snake_case					:   Union[str, Any]         =					argparse.ArgumentParser(description="Simple example of training script."				)
    parser.add_argument(
        "--mixed_precision"		,							type=lowercase		,							default=lowercase		,							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(
        "--with_tracking"		,							action="store_true"		,							help="Whether to load in all available experiment trackers from the environment and use them for logging."		,							)
    parser.add_argument(
        "--project_dir"		,							type=lowercase		,							default="logs"		,							help="Location on where to store experiment tracking logs` and relevent project information"		,							)
    snake_case					:   int         =					parser.parse_args()
    snake_case					:   List[str]         =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowercase		,							lowercase				)


if __name__ == "__main__":
  main()





