


'''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        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
					"""simple docstring"""




					return TrainCommand(_UpperCamelCase		)



class  lowerCAmelCase_						(       snake_case__      ):





		"""simple docstring"""



		@staticmethod
		def __a						(					SCREAMING_SNAKE_CASE__     :							Dict						):





							'''simple docstring'''

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

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

							train_parser.add_argument(
							    """--task"""				,							type=SCREAMING_SNAKE_CASE__				,							default="""text_classification"""				,							help="""Task to train the model on."""						)
							train_parser.add_argument(
							    """--model"""				,							type=SCREAMING_SNAKE_CASE__				,							default="""bert-base-uncased"""				,							help="""Model\'s name or path to stored model."""						)
							train_parser.add_argument("""--train_batch_size"""				,							type=SCREAMING_SNAKE_CASE__				,							default=3_2				,							help="""Batch size for training."""						)
							train_parser.add_argument("""--valid_batch_size"""				,							type=SCREAMING_SNAKE_CASE__				,							default=6_4				,							help="""Batch size for validation."""						)
							train_parser.add_argument("""--learning_rate"""				,							type=SCREAMING_SNAKE_CASE__				,							default=3E-5				,							help="""Learning rate."""						)
							train_parser.add_argument("""--adam_epsilon"""				,							type=SCREAMING_SNAKE_CASE__				,							default=1E-08				,							help="""Epsilon for Adam optimizer."""						)
							train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__						)

		def __init__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Dict						):





							'''simple docstring'''

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

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

							os.makedirs(args.output				,							exist_ok=SCREAMING_SNAKE_CASE__						)
							__a       =   args.output

							__a       =   args.column_label
							__a       =   args.column_text
							__a       =   args.column_id

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

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

		def __a						(					self     :							List[Any]						):





							'''simple docstring'''

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

		def __a						(					self     :							List[Any]						):





							'''simple docstring'''

							raise NotImplementedError


		def __a						(					self     :							List[str]						):





							'''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						)










'''simple docstring'''







import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE = True				,  __SCREAMING_SNAKE_CASE = math.inf				,  __SCREAMING_SNAKE_CASE = -math.inf				,  __SCREAMING_SNAKE_CASE = math.inf				,  __SCREAMING_SNAKE_CASE = -math.inf				,  __SCREAMING_SNAKE_CASE = False				,  __SCREAMING_SNAKE_CASE = 100				,  __SCREAMING_SNAKE_CASE = 0.01				,  __SCREAMING_SNAKE_CASE = 1				,  )				->					Any:
     """simple docstring"""




     __a       =   False
     __a       =   search_prob
     __a       =   start_temperate
     __a       =   []
     __a       =   0
     __a       =   None

     while not search_end:
          __a       =   current_state.score()
          if best_state is None or current_score > best_state.score():
               __a       =   current_state
          scores.append(__SCREAMING_SNAKE_CASE		)
          iterations += 1
          __a       =   None
          __a       =   current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               __a       =   random.randint(0				,  len(__SCREAMING_SNAKE_CASE		) - 1		)  # picking a random neighbor
               __a       =   neighbors.pop(__SCREAMING_SNAKE_CASE		)
               __a       =   picked_neighbor.score() - current_score

               if (
                   picked_neighbor.x > max_x
                   or picked_neighbor.x < min_x
                   or picked_neighbor.y > max_y
                   or picked_neighbor.y < min_y
               ):
                    continue  # neighbor outside our bounds

               if not find_max:
                    __a       =   change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __a       =   picked_neighbor
               else:
                    __a       =   (math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __a       =   picked_neighbor
          __a       =   current_temp - (current_temp * rate_of_decrease)

          if current_temp < threshold_temp or next_state is None:
               # temperature below threshold, or could not find a suitable neighbor
               __a       =   True
          else:
               __a       =   next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(__SCREAMING_SNAKE_CASE		)				,  __SCREAMING_SNAKE_CASE		)
          plt.xlabel("""Iterations"""		)
          plt.ylabel("""Function values"""		)
          plt.show()
     return best_state


if __name__ == "__main__":




 def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
      """simple docstring"""




      return (x**2) + (y**2)

 # starting the problem with initial coordinates (12, 47)
 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			simulated_annealing(
     prob, find_max=False, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
     f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )

 # starting the problem with initial coordinates (12, 47)
 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			simulated_annealing(
     prob, find_max=True, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
     f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )




 def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
      """simple docstring"""




      return (3 * x**2) - (6 * y)

 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			simulated_annealing(prob, find_max=False, visualization=True)
 print(
     'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
     f"""{local_min.score()}"""
 )

 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			simulated_annealing(prob, find_max=True, visualization=True)



 print(
     'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
     f"""{local_min.score()}"""
 )






