"""simple docstring"""
from __future__ import annotations

from typing import Any



class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = 0    ):






										"""simple docstring"""

										snake_case					,snake_case								=			row, column
										snake_case								=			[[default_value for c in range(lowerCAmelCase    )] for r in range(lowerCAmelCase    )]

				def __str__(				self    ):






										"""simple docstring"""

										snake_case								=			F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

										# Make string identifier
										snake_case								=			0
										for row_vector in self.array:
																for obj in row_vector:
																						snake_case								=			max(lowerCAmelCase		,    len(str(lowerCAmelCase    )    )    )
										snake_case								=			F"""%{max_element_length}s"""

										# Make string and return
										def single_line(lowerCAmelCase    ) -> str:
																nonlocal string_format_identifier
																snake_case								=			'['
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector    )
																line += "]"
																return line

										s += "\n".join(single_line(lowerCAmelCase    ) for row_vector in self.array    )
										return s

				def __repr__(				self    ):






										"""simple docstring"""

										return str(self    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if not (isinstance(lowerCAmelCase		,    (list, tuple)    ) and len(lowerCAmelCase    ) == 2):
																return False
										elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																return False
										else:
																return True

				def __getitem__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										assert self.validate_indicies(lowerCAmelCase    )
										return self.array[loc[0]][loc[1]]

				def __setitem__(				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										assert self.validate_indicies(lowerCAmelCase    )
										snake_case								=			value

				def __add__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										assert isinstance(lowerCAmelCase		,    lowerCAmelCase    )
										assert self.row == another.row and self.column == another.column

										# Add
										snake_case								=			Matrix(self.row		,    self.column    )
										for r in range(self.row    ):
																for c in range(self.column    ):
																						snake_case								=			self[r, c] + another[r, c]
										return result

				def __neg__(				self    ):






										"""simple docstring"""

										snake_case								=			Matrix(self.row		,    self.column    )
										for r in range(self.row    ):
																for c in range(self.column    ):
																						snake_case								=			-self[r, c]
										return result

				def __sub__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self + (-another)

				def __mul__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if isinstance(lowerCAmelCase		,    (int, float)    ):  # Scalar multiplication
																snake_case								=			Matrix(self.row		,    self.column    )
																for r in range(self.row    ):
																						for c in range(self.column    ):
																												snake_case								=			self[r, c] * another
																return result
										elif isinstance(lowerCAmelCase		,    lowerCAmelCase    ):  # Matrix multiplication
																assert self.column == another.row
																snake_case								=			Matrix(self.row		,    another.column    )
																for r in range(self.row    ):
																						for c in range(another.column    ):
																												for i in range(self.column    ):
																																		result[r, c] += self[r, i] * another[i, c]
																return result
										else:
																snake_case								=			F"""Unsupported type given for another ({type(lowerCAmelCase    )})"""
																raise TypeError(lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			Matrix(self.column		,    self.row    )
										for r in range(self.row    ):
																for c in range(self.column    ):
																						snake_case								=			self[r, c]
										return result

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										assert isinstance(lowerCAmelCase		,    lowerCAmelCase    ) and isinstance(lowerCAmelCase		,    lowerCAmelCase    )
										assert self.row == self.column == u.row == v.row  # u, v should be column vector
										assert u.column == v.column == 1  # u, v should be column vector

										# Calculate
										snake_case								=			v.transpose()
										snake_case								=			(v_t * self * u)[0, 0] + 1
										if numerator_factor == 0:
																return None  # It's not invertable
										return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





					def    lowerCAmelCase__    (			)					->				None:





											"""simple docstring"""






											snake_case								=			Matrix(3 ,			3 ,			0					)
											for i in range(3					):
																	snake_case								=			1
											print(f"""a^(-1) is {ainv}"""					)
											# u, v
											snake_case								=			Matrix(3 ,			1 ,			0					)
											snake_case					,snake_case					,snake_case								=			1, 2, -3
											snake_case								=			Matrix(3 ,			1 ,			0					)
											snake_case					,snake_case					,snake_case								=			4, -2, 5
											print(f"""u is {u}"""					)
											print(f"""v is {v}"""					)
											print(f"""uv^T is {u * v.transpose()}"""					)
											# Sherman Morrison
											print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(_UpperCamelCase ,			_UpperCamelCase					)}"""					)





					def    lowerCAmelCase__    (			)					->				None:





											"""simple docstring"""






											import doctest

											doctest.testmod()



					testa()



"""simple docstring"""
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
SCREAMING_SNAKE_CASE__      						=  False
SCREAMING_SNAKE_CASE__      						=  False





def    lowerCAmelCase__    (			_UpperCamelCase       :				Namespace					)					->				List[str]:





						"""simple docstring"""






						return TrainCommand(_UpperCamelCase					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@staticmethod
				def 		snake_case (				lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			parser.add_parser('train'		,    help='CLI tool to train a model on a task.'    )

										train_parser.add_argument(
										    '--train_data'		,    type=lowerCAmelCase		,    required=lowerCAmelCase		,    help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'		,    )
										train_parser.add_argument(
										    '--column_label'		,    type=lowerCAmelCase		,    default=0		,    help='Column of the dataset csv file with example labels.'    )
										train_parser.add_argument(
										    '--column_text'		,    type=lowerCAmelCase		,    default=1		,    help='Column of the dataset csv file with example texts.'    )
										train_parser.add_argument(
										    '--column_id'		,    type=lowerCAmelCase		,    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=lowerCAmelCase		,    default=''		,    help='path to validation dataset.'    )
										train_parser.add_argument(
										    '--validation_split'		,    type=lowerCAmelCase		,    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=lowerCAmelCase		,    default='./'		,    help='path to saved the trained model.'    )

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

				def __init__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			logging.get_logger('transformers-cli/training'    )

										snake_case								=			'tf' if is_tf_available() else 'torch'

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase    )
										snake_case								=			args.output

										snake_case								=			args.column_label
										snake_case								=			args.column_text
										snake_case								=			args.column_id

										self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""    )
										if args.task == "text_classification":
																snake_case								=			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}"""    )
										snake_case								=			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		,    )
										snake_case								=			None
										if args.validation_data:
																self.logger.info(F"""Loading validation dataset from {args.validation_data}"""    )
																snake_case								=			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		,    )

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										if self.framework == "tf":
																return self.run_tf()
										return self.run_torch()

				def 		snake_case (				self    ):






										"""simple docstring"""

										raise NotImplementedError

				def 		snake_case (				self    ):






										"""simple docstring"""

										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    )


