




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = False					,  UpperCamelCase__ = False					,  UpperCamelCase__ = None					,  **UpperCamelCase__					,  )					->				Any:
           super().__init__(
               UpperCamelCase__					,  split=UpperCamelCase__					,  features=UpperCamelCase__					,  cache_dir=UpperCamelCase__					,  keep_in_memory=UpperCamelCase__					,  streaming=UpperCamelCase__					,  num_proc=UpperCamelCase__					,  **UpperCamelCase__					,  )
           lowerCamelCase       :				Tuple      			= path_or_paths if isinstance(UpperCamelCase__					,  UpperCamelCase__					) else {self.split: path_or_paths}
           lowerCamelCase       :				Tuple      			= Csv(
               cache_dir=UpperCamelCase__					,  data_files=UpperCamelCase__					,  features=UpperCamelCase__					,  **UpperCamelCase__					,  )







    def    _lowercase					(   self					)					->				Any:
           # Build iterable dataset
           if self.streaming:
                  lowerCamelCase       :				int      			= self.builder.as_streaming_dataset(split=self.split					)
           # Build regular (map-style) dataset
           else:
                  lowerCamelCase       :				str      			= None
                  lowerCamelCase       :				Optional[Any]      			= None
                  lowerCamelCase       :				Optional[int]      			= None
                  lowerCamelCase       :				List[str]      			= None

                  self.builder.download_and_prepare(
                      download_config=UpperCamelCase__					,  download_mode=UpperCamelCase__					,  verification_mode=UpperCamelCase__					,  base_path=UpperCamelCase__					,  num_proc=self.num_proc					,  )
                  lowerCamelCase       :				int      			= self.builder.as_dataset(
                      split=self.split					,  verification_mode=UpperCamelCase__					,  in_memory=self.keep_in_memory					)
           return dataset




class 		UpperCamelCase__     :

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  **UpperCamelCase__					,  )					->				Union[str, Any]:
           if num_proc is not None and num_proc <= 0:
                  raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''					)

           lowerCamelCase       :				int      			= dataset
           lowerCamelCase       :				Union[str, Any]      			= path_or_buf
           lowerCamelCase       :				Dict      			= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           lowerCamelCase       :				Any      			= num_proc
           lowerCamelCase       :				str      			= "utf-8"
           lowerCamelCase       :				Union[str, Any]      			= to_csv_kwargs







    def    _lowercase					(   self					)					->				int:
           lowerCamelCase       :				str      			= self.to_csv_kwargs.pop("path_or_buf"					,  UpperCamelCase__					)
           lowerCamelCase       :				str      			= self.to_csv_kwargs.pop("header"					,  UpperCamelCase__					)
           lowerCamelCase       :				Dict      			= self.to_csv_kwargs.pop("index"					,  UpperCamelCase__					)

           if isinstance(self.path_or_buf					,  (str, bytes, os.PathLike)					):
                  with open(self.path_or_buf					,  "wb+"					) as buffer:
                         lowerCamelCase       :				Optional[Any]      			= self._write(file_obj=UpperCamelCase__					,  header=UpperCamelCase__					,  index=UpperCamelCase__					,  **self.to_csv_kwargs					)
           else:
                  lowerCamelCase       :				Union[str, Any]      			= self._write(file_obj=self.path_or_buf					,  header=UpperCamelCase__					,  index=UpperCamelCase__					,  **self.to_csv_kwargs					)
           return written







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Optional[Any]:
           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= args

           lowerCamelCase       :				Optional[int]      			= query_table(
               table=self.dataset.data					,  key=slice(UpperCamelCase__					,  offset + self.batch_size					)					,  indices=self.dataset._indices					,  )
           lowerCamelCase       :				Dict      			= batch.to_pandas().to_csv(
               path_or_buf=UpperCamelCase__					,  header=header if (offset == 0) else False					,  index=UpperCamelCase__					,  **UpperCamelCase__					)
           return csv_str.encode(self.encoding					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__					)					->				int:
           lowerCamelCase       :				Dict      			= 0

           if self.num_proc is None or self.num_proc == 1:
                  for offset in logging.tqdm(
                      range(0					,  len(self.dataset					)					,  self.batch_size					)					,  unit="ba"					,  disable=not logging.is_progress_bar_enabled()					,  desc="Creating CSV from Arrow format"					,  ):
                         lowerCamelCase       :				str      			= self._batch_csv((offset, header, index, to_csv_kwargs)					)
                         written += file_obj.write(UpperCamelCase__					)

           else:
                  lowerCamelCase			,				lowerCamelCase       :				List[Any]      			= len(self.dataset					), self.batch_size
                  with multiprocessing.Pool(self.num_proc					) as pool:
                         for csv_str in logging.tqdm(
                             pool.imap(
                                 self._batch_csv					,  [(offset, header, index, to_csv_kwargs) for offset in range(0					,  UpperCamelCase__					,  UpperCamelCase__					)]					,  )					,  total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size					,  unit="ba"					,  disable=not logging.is_progress_bar_enabled()					,  desc="Creating CSV from Arrow format"					,  ):
                                written += file_obj.write(UpperCamelCase__					)

           return written












import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 					A				(				*_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[str]      			= torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''   )
       with patch_environment(
           world_size=_SCREAMING_SNAKE_CASE			,master_addr="127.0.01"			,master_port="29500"			,mixed_precision=args[1].mixed_precision   ):
              lowerCamelCase       :				Union[str, Any]      			= PrepareForLaunch(_SCREAMING_SNAKE_CASE			,distributed_type="MULTI_GPU"   )
              torch.multiprocessing.start_processes(_SCREAMING_SNAKE_CASE			,args=_SCREAMING_SNAKE_CASE			,nprocs=_SCREAMING_SNAKE_CASE			,start_method="spawn"   )


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
       # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

       # on-demand GPU
       # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
       SCREAMING_SNAKE_CASE__	:				Tuple  =       rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
       gpu.up_if_not()

       # on-prem GPU
       # gpu = rh.cluster(
       #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
       #       )

       # Set up remote function
       SCREAMING_SNAKE_CASE__	:				int  =       [
           'pip:./',
           'transformers',
           'datasets',
           'evaluate',
           'tqdm',
           'scipy',
           'scikit-learn',
           'tensorboard',
           'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
       ]
       SCREAMING_SNAKE_CASE__	:				Dict  =       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

       # Define train args/config, run train function
       SCREAMING_SNAKE_CASE__	:				int  =       argparse.Namespace(cpu=False, mixed_precision='fp16')
       SCREAMING_SNAKE_CASE__	:				int  =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       launch_train_gpu(config, train_args, stream_logs=True)

       # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
       # gpu.install_packages(reqs)
       # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])






