



import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
	raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__A       =						False
__A       =						False



def 	__a					(   lowerCAmelCase_       : Namespace    )				->			int:






						'''simple docstring'''



						return TrainCommand(lowerCAmelCase_    )






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						ArgumentParser					)      ->			Optional[Any]:
							UpperCAmelCase_=       parser.add_parser("""train"""						,  help="""CLI tool to train a model on a task."""					)

							train_parser.add_argument(
							    """--train_data"""						,  type=__UpperCAmelCase						,  required=__UpperCAmelCase						,  help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""						,  )
							train_parser.add_argument(
							    """--column_label"""						,  type=__UpperCAmelCase						,  default=0						,  help="""Column of the dataset csv file with example labels."""					)
							train_parser.add_argument(
							    """--column_text"""						,  type=__UpperCAmelCase						,  default=1						,  help="""Column of the dataset csv file with example texts."""					)
							train_parser.add_argument(
							    """--column_id"""						,  type=__UpperCAmelCase						,  default=2						,  help="""Column of the dataset csv file with example ids."""					)
							train_parser.add_argument(
							    """--skip_first_row"""						,  action="""store_true"""						,  help="""Skip the first row of the csv file (headers)."""					)

							train_parser.add_argument("""--validation_data"""						,  type=__UpperCAmelCase						,  default=""""""						,  help="""path to validation dataset."""					)
							train_parser.add_argument(
							    """--validation_split"""						,  type=__UpperCAmelCase						,  default=0.1						,  help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""						,  )

							train_parser.add_argument("""--output"""						,  type=__UpperCAmelCase						,  default="""./"""						,  help="""path to saved the trained model."""					)

							train_parser.add_argument(
							    """--task"""						,  type=__UpperCAmelCase						,  default="""text_classification"""						,  help="""Task to train the model on."""					)
							train_parser.add_argument(
							    """--model"""						,  type=__UpperCAmelCase						,  default="""bert-base-uncased"""						,  help="""Model's name or path to stored model."""					)
							train_parser.add_argument("""--train_batch_size"""						,  type=__UpperCAmelCase						,  default=32						,  help="""Batch size for training."""					)
							train_parser.add_argument("""--valid_batch_size"""						,  type=__UpperCAmelCase						,  default=64						,  help="""Batch size for validation."""					)
							train_parser.add_argument("""--learning_rate"""						,  type=__UpperCAmelCase						,  default=3E-5						,  help="""Learning rate."""					)
							train_parser.add_argument("""--adam_epsilon"""						,  type=__UpperCAmelCase						,  default=1E-08						,  help="""Epsilon for Adam optimizer."""					)
							train_parser.set_defaults(func=__UpperCAmelCase					)




	def __init__(     self     :						str						,  __UpperCAmelCase     :						Namespace					)      ->			Optional[int]:
							UpperCAmelCase_=       logging.get_logger("""transformers-cli/training"""					)

							UpperCAmelCase_=       """tf""" if is_tf_available() else """torch"""

							os.makedirs(args.output						,  exist_ok=__UpperCAmelCase					)
							UpperCAmelCase_=       args.output

							UpperCAmelCase_=       args.column_label
							UpperCAmelCase_=       args.column_text
							UpperCAmelCase_=       args.column_id

							self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""					)
							if args.task == "text_classification":
													UpperCAmelCase_=       TextClassificationPipeline.from_pretrained(args.model					)
							elif args.task == "token_classification":
													raise NotImplementedError
							elif args.task == "question_answering":
													raise NotImplementedError

							self.logger.info(F"""Loading dataset from {args.train_data}"""					)
							UpperCAmelCase_=       Processor.create_from_csv(
							    args.train_data						,  column_label=args.column_label						,  column_text=args.column_text						,  column_id=args.column_id						,  skip_first_row=args.skip_first_row						,  )
							UpperCAmelCase_=       None
							if args.validation_data:
													self.logger.info(F"""Loading validation dataset from {args.validation_data}"""					)
													UpperCAmelCase_=       Processor.create_from_csv(
													    args.validation_data						,  column_label=args.column_label						,  column_text=args.column_text						,  column_id=args.column_id						,  skip_first_row=args.skip_first_row						,  )

							UpperCAmelCase_=       args.validation_split
							UpperCAmelCase_=       args.train_batch_size
							UpperCAmelCase_=       args.valid_batch_size
							UpperCAmelCase_=       args.learning_rate
							UpperCAmelCase_=       args.adam_epsilon




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[Any]:
							if self.framework == "tf":
													return self.run_tf()
							return self.run_torch()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							raise NotImplementedError




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[str]:
							self.pipeline.fit(
							    self.train_dataset						,  validation_data=self.valid_dataset						,  validation_split=self.validation_split						,  learning_rate=self.learning_rate						,  adam_epsilon=self.adam_epsilon						,  train_batch_size=self.train_batch_size						,  valid_batch_size=self.valid_batch_size						,  )

							# Save trained pipeline
							self.pipeline.save_pretrained(self.output					)









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


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

__A       =						['''names''', '''prefix''']
__A       =						['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__A       =						['''encoding_errors''', '''on_bad_lines''']
__A       =						['''date_format''']






@dataclass
class 		lowercase						(			datasets.BuilderConfig):






	"""simple docstring"""


	a__				: str		       =							","
	a__				: Optional[str]		       =							None
	a__				: Optional[Union[int, List[int], str]]		       =							"infer"
	a__				: Optional[List[str]]		       =							None
	a__				: Optional[List[str]]		       =							None
	a__				: Optional[Union[int, str, List[int], List[str]]]		       =							None
	a__				: Optional[Union[List[int], List[str]]]		       =							None
	a__				: Optional[str]		       =							None
	a__				: bool		       =							True
	a__				: Optional[Literal["c", "python", "pyarrow"]]		       =							None
	a__				: Dict[Union[int, str], Callable[[Any], Any]]		       =							None
	a__				: Optional[list]		       =							None
	a__				: Optional[list]		       =							None
	a__				: bool		       =							False
	a__				: Optional[Union[int, List[int]]]		       =							None
	a__				: Optional[int]		       =							None
	a__				: Optional[Union[str, List[str]]]		       =							None
	a__				: bool		       =							True
	a__				: bool		       =							True
	a__				: bool		       =							False
	a__				: bool		       =							True
	a__				: Optional[str]		       =							None
	a__				: str		       =							"."
	a__				: Optional[str]		       =							None
	a__				: str		       =							'"'
	a__				: int		       =							0
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: bool		       =							True
	a__				: bool		       =							True
	a__				: int		       =							0
	a__				: bool		       =							True
	a__				: bool		       =							False
	a__				: Optional[str]		       =							None
	a__				: int		       =							1_0000
	a__				: Optional[datasets.Features]		       =							None
	a__				: Optional[str]		       =							"strict"
	a__				: Literal["error", "warn", "skip"]		       =							"error"
	a__				: Optional[str]		       =							None




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			int:
							if self.delimiter is not None:
													UpperCAmelCase_=       self.delimiter
							if self.column_names is not None:
													UpperCAmelCase_=       self.column_names




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Tuple:
							UpperCAmelCase_=       {
							    """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 		lowercase						(			datasets.ArrowBasedBuilder):






	"""simple docstring"""


	a__				: int		       =							CsvConfig




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							return datasets.DatasetInfo(features=self.config.features					)




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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						pa.Table					)      ->			pa.Table:
							if self.config.features is not None:
													UpperCAmelCase_=       self.config.features.arrow_schema
													if all(not require_storage_cast(__UpperCAmelCase					) for feature in self.config.features.values()					):
																			# cheaper cast
																			UpperCAmelCase_=       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
																			UpperCAmelCase_=       table_cast(__UpperCAmelCase						,  __UpperCAmelCase					)
							return pa_table




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							UpperCAmelCase_=       self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							UpperCAmelCase_=       (
							    {
							        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					)					):
													UpperCAmelCase_=       pd.read_csv(__UpperCAmelCase						,  iterator=__UpperCAmelCase						,  dtype=__UpperCAmelCase						,  **self.config.pd_read_csv_kwargs					)
													try:
																			for batch_idx, df in enumerate(__UpperCAmelCase					):
																									UpperCAmelCase_=       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




