






"""simple docstring"""



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 			_snake_case    (					lowerCamelCase__     :   int=None  )    ->     Union[str, Any]:
							if subparsers is not None:
														lowerCamelCase_	:							List[Any]									=subparsers.add_parser("test"  )
							else:
														lowerCamelCase_	:							List[str]									=argparse.ArgumentParser("Accelerate test command"  )

							parser.add_argument(
							    "--config_file"    ,						default=lowerCamelCase__    ,						help=(
							        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
							        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
							        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
							        "with 'huggingface'."
							    )    ,						)

							if subparsers is not None:
														parser.set_defaults(func=lowerCamelCase__  )
							return parser

def 			_snake_case    (					lowerCamelCase__     :   List[Any]  )    ->     Any:
							lowerCamelCase_	:							Optional[Any]									=os.path.sep.join(__file__.split(os.path.sep  )[:-2] + ["test_utils", "scripts", "test_script.py"]  )

							if args.config_file is None:
														lowerCamelCase_	:							List[Any]									=script_name
							else:
														lowerCamelCase_	:							Union[str, Any]									=F"""--config_file={args.config_file} {script_name}"""

							lowerCamelCase_	:							List[str]									=["accelerate-launch"] + test_args.split()
							lowerCamelCase_	:							Tuple									=execute_subprocess_async(lowerCamelCase__    ,						env=os.environ.copy()  )
							if result.returncode == 0:
														print("Test is a success! You are ready for your distributed training!"  )

def 			_snake_case    (					)    ->     Tuple:
							lowerCamelCase_	:							Any									=test_command_parser()
							lowerCamelCase_	:							List[Any]									=parser.parse_args()
							test_command(lowerCamelCase__  )


if __name__ == "__main__":
						main()











"""simple docstring"""



import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


A__	:    List[str]			 = datasets.utils.logging.get_logger(__name__)







@dataclass
class        lowercase__      (							datasets.BuilderConfig       ):
			_UpperCAmelCase		:int             =			10000
			_UpperCAmelCase		:Optional[List[str]]             =			None
			_UpperCAmelCase		:Optional[datasets.Features]             =			None







class        lowercase__      (							datasets.ArrowBasedBuilder       ):
			_UpperCAmelCase		:Optional[int]             =			ParquetConfig


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										return datasets.DatasetInfo(features=self.config.features )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  List[Any] ):
										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}""" )
										lowerCamelCase_	:							str									=dl_manager.download_and_extract(self.config.data_files )
										if isinstance(snake_case__       ,	(str, list, tuple) ):
																	lowerCamelCase_	:							Dict									=data_files
																	if isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							List[Any]									=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	lowerCamelCase_	:							Any									=[dl_manager.iter_files(snake_case__ ) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={"files": files} )]
										lowerCamelCase_	:							Optional[int]									=[]
										for split_name, files in data_files.items():
																	if isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							Optional[int]									=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	lowerCamelCase_	:							int									=[dl_manager.iter_files(snake_case__ ) for file in files]
																	# Infer features is they are stoed in the arrow schema
																	if self.info.features is None:
																								for file in itertools.chain.from_iterable(snake_case__ ):
																															with open(snake_case__       ,	"rb" ) as f:
																																						lowerCamelCase_	:							List[Any]									=datasets.Features.from_arrow_schema(pq.read_schema(snake_case__ ) )
																															break
																	splits.append(datasets.SplitGenerator(name=snake_case__       ,	gen_kwargs={"files": files} ) )
										return splits


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  pa.Table ):
										if self.info.features is not None:
																	# more expensive cast to support nested features with keys in a different order
																	# allows str <-> int/float or str to Audio for example
																	lowerCamelCase_	:							List[str]									=table_cast(snake_case__       ,	self.info.features.arrow_schema )
										return pa_table


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Optional[Any] ):
										lowerCamelCase_	:							Tuple									=self.info.features.arrow_schema if self.info.features is not None else None
										if self.info.features is not None and self.config.columns is not None:
																	if sorted(field.name for field in schema ) != sorted(self.config.columns ):
																								raise ValueError(
																								    F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
										for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__ ) ):
																	with open(snake_case__       ,	"rb" ) as f:
																								lowerCamelCase_	:							List[str]									=pq.ParquetFile(snake_case__ )
																								try:
																															for batch_idx, record_batch in enumerate(
																															    parquet_file.iter_batches(batch_size=self.config.batch_size       ,	columns=self.config.columns ) ):
																																						lowerCamelCase_	:							Union[str, Any]									=pa.Table.from_batches([record_batch] )
																																						# 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 F"""{file_idx}_{batch_idx}""", self._cast_table(snake_case__ )
																								except ValueError as e:
																															logger.error(F"""Failed to read file '{file}' with error {type(snake_case__ )}: {e}""" )
																															raise



