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
_lowerCamelCase					:   Dict							=		False
_lowerCamelCase					:   Any							=		False





def 	__lowerCamelCase							(UpperCAmelCase__     :							Namespace  ):
					return TrainCommand(UpperCAmelCase__  )


class 						lowercase    (     a     ):




							@staticmethod
							def 	__snake_case(						_UpperCamelCase   :					ArgumentParser      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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   :					Union[str, Any]	,							_UpperCamelCase   :					Namespace      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       logging.get_logger("transformers-cli/training"      )

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

												os.makedirs(args.output	,							exist_ok=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       args.output

												SCREAMING_SNAKE_CASE    			=       args.column_label
												SCREAMING_SNAKE_CASE    			=       args.column_text
												SCREAMING_SNAKE_CASE    			=       args.column_id

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

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




							def 	__snake_case(						self   :					Dict      )					->				Any:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

												raise NotImplementedError





							def 	__snake_case(						self   :					List[Any]      )					->				Dict:



												'''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      )





import numpy as np





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							float = 1e-12      , UpperCAmelCase__     :							int = 1_0_0      , ):
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[1]
					# Ensure proper dimensionality.
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(UpperCAmelCase__  ) == np.iscomplexobj(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       np.iscomplexobj(UpperCAmelCase__  )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(UpperCAmelCase__      , input_matrix.conj().T  )

					# Set convergence to False. Will define convergence when we exceed max_iterations
					# or when we have small changes from one iteration to next.

					SCREAMING_SNAKE_CASE    			=       False
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       1e12

					while not convergence:
										# Multiple matrix by the vector.
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , UpperCAmelCase__  )
										# Normalize the resulting output vector.
										SCREAMING_SNAKE_CASE    			=       w / np.linalg.norm(UpperCAmelCase__  )
										# Find rayleigh quotient
										# (faster than usual b/c we know vector is normalized already)
										SCREAMING_SNAKE_CASE    			=       vector.conj().T if is_complex else vector.T
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , np.dot(UpperCAmelCase__      , UpperCAmelCase__  )  )

										# Check convergence.
										SCREAMING_SNAKE_CASE    			=       np.abs(lambda_ - lambda_previous  ) / lambda_
										iterations += 1

										if error <= error_tol or iterations >= max_iterations:
															SCREAMING_SNAKE_CASE    			=       True

										SCREAMING_SNAKE_CASE    			=       lambda_

					if is_complex:
										SCREAMING_SNAKE_CASE    			=       np.real(lambda_  )

					return lambda_, vector





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]  )
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  )
					SCREAMING_SNAKE_CASE    			=       real_input_matrix.astype(np.complexaaa  )
					SCREAMING_SNAKE_CASE    			=       np.triu(1j * complex_input_matrix      , 1  )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  ).astype(np.complexaaa  )

					for problem_type in ["real", "complex"]:
										if problem_type == "real":
															SCREAMING_SNAKE_CASE    			=       real_input_matrix
															SCREAMING_SNAKE_CASE    			=       real_vector
										elif problem_type == "complex":
															SCREAMING_SNAKE_CASE    			=       complex_input_matrix
															SCREAMING_SNAKE_CASE    			=       complex_vector

										# Our implementation.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       power_iteration(UpperCAmelCase__      , UpperCAmelCase__  )

										# Numpy implementation.

										# Get eigenvalues and eigenvectors using built-in numpy
										# eigh (eigh used for symmetric or hermetian matrices).
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       np.linalg.eigh(UpperCAmelCase__  )
										# Last eigenvalue is the maximum one.
										SCREAMING_SNAKE_CASE    			=       eigen_values[-1]
										# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
										SCREAMING_SNAKE_CASE    			=       eigen_vectors[:, -1]

										# Check our implementation and numpy gives close answers.
										assert np.abs(eigen_value - eigen_value_max  ) <= 1e-6
										# Take absolute values element wise of each eigenvector.
										# as they are only unique to a minus sign.
										assert np.linalg.norm(np.abs(UpperCAmelCase__  ) - np.abs(UpperCAmelCase__  )  ) <= 1e-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




