


"""simple docstring"""






import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil


@pytest.fixture
def __lowercase     (      lowerCamelCase_  :      str  ):
					SCREAMING_SNAKE_CASE__     							=				tmp_path / "file.csv"
					SCREAMING_SNAKE_CASE__     							=				textwrap.dedent(
					    "\\n        header1,header2\n        1,2\n        10,20\n        "  )
					with open(lowerCamelCase_						,					"w"  ) as f:
										f.write(lowerCamelCase_  )
					return str(lowerCamelCase_  )


@pytest.fixture
def __lowercase     (      lowerCamelCase_  :      Optional[Any]  ):
					SCREAMING_SNAKE_CASE__     							=				tmp_path / "malformed_file.csv"
					SCREAMING_SNAKE_CASE__     							=				textwrap.dedent(
					    "\\n        header1,header2\n        1,2\n        10,20,\n        "  )
					with open(lowerCamelCase_						,					"w"  ) as f:
										f.write(lowerCamelCase_  )
					return str(lowerCamelCase_  )


@pytest.fixture
def __lowercase     (      lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      str  ):
					SCREAMING_SNAKE_CASE__     							=				tmp_path / "csv_with_image.csv"
					SCREAMING_SNAKE_CASE__     							=				textwrap.dedent(
					    F'''\
        image
        {image_file}
        '''  )
					with open(lowerCamelCase_						,					"w"  ) as f:
										f.write(lowerCamelCase_  )
					return str(lowerCamelCase_  )


@pytest.fixture
def __lowercase     (      lowerCamelCase_  :      int  ):
					SCREAMING_SNAKE_CASE__     							=				tmp_path / "csv_with_label.csv"
					SCREAMING_SNAKE_CASE__     							=				textwrap.dedent(
					    "\\n        label\n        good\n        bad\n        good\n        "  )
					with open(lowerCamelCase_						,					"w"  ) as f:
										f.write(lowerCamelCase_  )
					return str(lowerCamelCase_  )


@pytest.fixture
def __lowercase     (      lowerCamelCase_  :      Dict  ):
					SCREAMING_SNAKE_CASE__     							=				tmp_path / "csv_with_int_list.csv"
					SCREAMING_SNAKE_CASE__     							=				textwrap.dedent(
					    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "  )
					with open(lowerCamelCase_						,					"w"  ) as f:
										f.write(lowerCamelCase_  )
					return str(lowerCamelCase_  )


def __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Union[str, Any]						,					lowerCamelCase_  :      Optional[int]  ):
					SCREAMING_SNAKE_CASE__     							=				Csv()
					SCREAMING_SNAKE_CASE__     							=				csv._generate_tables([[csv_file, malformed_csv_file]]  )
					with pytest.raises(lowerCamelCase_						,					match="Error tokenizing data"  ):
										for _ in generator:
															pass
					assert any(
					    record.levelname == "ERROR"
					    and "Failed to read file" in record.message
					    and os.path.basename(lowerCamelCase_  ) in record.message
					    for record in caplog.records  )


@require_pil
def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
					with open(lowerCamelCase_						,					encoding="utf-8"  ) as f:
										SCREAMING_SNAKE_CASE__     							=				f.read().splitlines()[1]
					SCREAMING_SNAKE_CASE__     							=				Csv(encoding="utf-8"						,					features=Features({"image": Image()}  )  )
					SCREAMING_SNAKE_CASE__     							=				csv._generate_tables([[csv_file_with_image]]  )
					SCREAMING_SNAKE_CASE__     							=				pa.concat_tables([table for _, table in generator]  )
					assert pa_table.schema.field("image"  ).type == Image()()
					SCREAMING_SNAKE_CASE__     							=				pa_table.to_pydict()["image"]
					assert generated_content == [{"path": image_file, "bytes": None}]


def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
					with open(lowerCamelCase_						,					encoding="utf-8"  ) as f:
										SCREAMING_SNAKE_CASE__     							=				f.read().splitlines()[1:]
					SCREAMING_SNAKE_CASE__     							=				Csv(encoding="utf-8"						,					features=Features({"label": ClassLabel(names=["good", "bad"]  )}  )  )
					SCREAMING_SNAKE_CASE__     							=				csv._generate_tables([[csv_file_with_label]]  )
					SCREAMING_SNAKE_CASE__     							=				pa.concat_tables([table for _, table in generator]  )
					assert pa_table.schema.field("label"  ).type == ClassLabel(names=["good", "bad"]  )()
					SCREAMING_SNAKE_CASE__     							=				pa_table.to_pydict()["label"]
					assert generated_content == [ClassLabel(names=["good", "bad"]  ).straint(lowerCamelCase_  ) for label in labels]


def __lowercase     (      lowerCamelCase_  :      int  ):
					SCREAMING_SNAKE_CASE__     							=				Csv(encoding="utf-8"						,					sep=","						,					converters={"int_list": lambda lowerCamelCase_  : [int(lowerCamelCase_  ) for i in x.split()]}  )
					SCREAMING_SNAKE_CASE__     							=				csv._generate_tables([[csv_file_with_int_list]]  )
					SCREAMING_SNAKE_CASE__     							=				pa.concat_tables([table for _, table in generator]  )
					assert pa.types.is_list(pa_table.schema.field("int_list"  ).type  )
					SCREAMING_SNAKE_CASE__     							=				pa_table.to_pydict()["int_list"]
					assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]




"""simple docstring"""






import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
	# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
	# setup instructions, if using on-demand hardware

	# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
	# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
	# Throw an error if user passes both BYO and on-demand cluster args
	# Otherwise, use default values
	_lowerCamelCase     =  argparse.ArgumentParser()
	parser.add_argument('--user', type=str, default='ubuntu')
	parser.add_argument('--host', type=str, default='localhost')
	parser.add_argument('--key_path', type=str, default=None)
	parser.add_argument('--instance', type=str, default='V100:1')
	parser.add_argument('--provider', type=str, default='cheapest')
	parser.add_argument('--use_spot', type=bool, default=False)
	parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
	_lowerCamelCase     ,     _lowerCamelCase     =  parser.parse_known_args()
	if args.host != "localhost":
		if args.instance != "V100:1" or args.provider != "cheapest":
			raise ValueError('Cannot specify both BYO and on-demand cluster args')
		_lowerCamelCase     =  rh.cluster(
		    name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
		)
	else:
		_lowerCamelCase     =  rh.cluster(
		    name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
		)
	_lowerCamelCase     =  args.example.rsplit('/', 1)[0]

	# Set up remote environment
	cluster.install_packages(['pip:./'])  # Installs transformers from local source
	# Note transformers is copied into the home directory on the remote machine, so we can install from there
	cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
	cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])

	# Run example. You can bypass the CLI wrapper and paste your own code here.
	cluster.run([f"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])

	# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
	# from my_script... import train
	# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
	# launch_train_gpu = rh.function(fn=train,
	#                                system=gpu,
	#                                reqs=reqs,
	#                                name='train_bert_glue')
	#
	# We can pass in arguments just like we would to a function:
	# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
	#                  stream_logs=True)
