



"""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


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

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




@dataclass
class 			snake_case_						(						datasets.BuilderConfig				):
							__lowerCAmelCase    					=					","
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					"infer"
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					"."
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					"\""
							__lowerCAmelCase    					=					0
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					0
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					1_0_0_0_0
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					"strict"
							__lowerCAmelCase    					=					"error"
							__lowerCAmelCase    					=					None






							def snake_case_  (   self      ):



									if self.delimiter is not None:
											a_     :      int 						=     self.delimiter
									if self.column_names is not None:
											a_     :      Dict 						=     self.column_names






							@property
							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     {
									    """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()			,     a_      ):
													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 			snake_case_						(						datasets.ArrowBasedBuilder				):
							__lowerCAmelCase    					=					CsvConfig






							def snake_case_  (   self      ):
									return datasets.DatasetInfo(features=self.config.features      )






							def snake_case_  (   self			,     a_      ):



									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}"""      )
									a_     :      Optional[Any] 						=     dl_manager.download_and_extract(self.config.data_files      )
									if isinstance(a_			,     (str, list, tuple)      ):
											a_     :      Any 						=     data_files
											if isinstance(a_			,     a_      ):
													a_     :      Optional[int] 						=     [files]
											a_     :      Optional[Any] 						=     [dl_manager.iter_files(a_      ) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,     gen_kwargs={"files": files}      )]
									a_     :      List[Any] 						=     []
									for split_name, files in data_files.items():
											if isinstance(a_			,     a_      ):
													a_     :      Tuple 						=     [files]
											a_     :      List[Any] 						=     [dl_manager.iter_files(a_      ) for file in files]
											splits.append(datasets.SplitGenerator(name=a_			,     gen_kwargs={"files": files}      )      )
									return splits






							def snake_case_  (   self			,     a_      ):



									if self.config.features is not None:
											a_     :      Dict 						=     self.config.features.arrow_schema
											if all(not require_storage_cast(a_      ) for feature in self.config.features.values()      ):
													# cheaper cast
													a_     :      Dict 						=     pa.Table.from_arrays([pa_table[field.name] for field in schema]			,     schema=a_      )
											else:
													# more expensive cast; allows str <-> int/float or str to Audio for example
													a_     :      Optional[int] 						=     table_cast(a_			,     a_      )
									return pa_table






							def snake_case_  (   self			,     a_      ):
									a_     :      List[Any] 						=     self.config.features.arrow_schema if self.config.features else None
									# dtype allows reading an int column as str
									a_     :      Any 						=     (
									    {
									        name: dtype.to_pandas_dtype() if not require_storage_cast(a_      ) 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(a_      )      ):
											a_     :      Any 						=     pd.read_csv(a_			,     iterator=a_			,     dtype=a_			,     **self.config.pd_read_csv_kwargs      )
											try:
													for batch_idx, df in enumerate(a_      ):
															a_     :      Optional[int] 						=     pa.Table.from_pandas(a_      )
															# 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(a_      )
											except ValueError as e:
													logger.error(F"""Failed to read file '{file}' with error {type(a_      )}: {e}"""      )
													raise




"""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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE_				= """Create a default config file for Accelerate with only a few flags set."""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__="no", SCREAMING_SNAKE_CASE__ = default_json_config_file, SCREAMING_SNAKE_CASE__ = False	)			->      Tuple:
		a_     :      Union[str, Any] 						=     Path(SCREAMING_SNAKE_CASE__	)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__, exist_ok=SCREAMING_SNAKE_CASE__	)
		if path.exists():
				print(
				    F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""	)
				return False
		a_     :      Optional[int] 						=     mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""	)
		a_     :      str 						=     {
		    "compute_environment": "LOCAL_MACHINE",
		    "mixed_precision": mixed_precision,
		}
		if torch.cuda.is_available():
				a_     :      int 						=     torch.cuda.device_count()
				a_     :      Optional[Any] 						=     num_gpus
				a_     :      int 						=     False
				if num_gpus > 1:
						a_     :      Any 						=     "MULTI_GPU"
				else:
						a_     :      int 						=     "NO"
		elif is_xpu_available() and use_xpu:
				a_     :      int 						=     torch.xpu.device_count()
				a_     :      str 						=     num_xpus
				a_     :      Tuple 						=     False
				if num_xpus > 1:
						a_     :      int 						=     "MULTI_XPU"
				else:
						a_     :      List[str] 						=     "NO"
		elif is_npu_available():
				a_     :      List[Any] 						=     torch.npu.device_count()
				a_     :      int 						=     num_npus
				a_     :      List[Any] 						=     False
				if num_npus > 1:
						a_     :      str 						=     "MULTI_NPU"
				else:
						a_     :      Union[str, Any] 						=     "NO"
		else:
				a_     :      Optional[Any] 						=     0
				a_     :      Optional[Any] 						=     True
				a_     :      Tuple 						=     1
				a_     :      Optional[int] 						=     "NO"
		a_     :      str 						=     ClusterConfig(**SCREAMING_SNAKE_CASE__	)
		config.to_json_file(SCREAMING_SNAKE_CASE__	)
		return path



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_     :      Dict 						=     parser.add_parser("default", parents=SCREAMING_SNAKE_CASE__, help=SCREAMING_SNAKE_CASE__, formatter_class=SCREAMING_SNAKE_CASE__	)
		parser.add_argument(
		    "--config_file", default=SCREAMING_SNAKE_CASE__, 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'."
		    ), dest="save_location", )

		parser.add_argument(
		    "--mixed_precision", choices=["no", "fp16", "bf16"], type=SCREAMING_SNAKE_CASE__, help="Whether or not to use mixed precision training. "
		    "Choose between FP16 and BF16 (bfloat16) training. "
		    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", default="no", )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
		return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Any 						=     write_basic_config(args.mixed_precision, args.save_location	)
		if config_file:
				print(F"""accelerate configuration saved at {config_file}"""	)