




from __future__ import annotations

import collections
import pprint
from pathlib import Path

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			return "".join(sorted(SCREAMING_SNAKE_CASE ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							list[str]:


			'''simple docstring'''







			return word_by_signature[signature(SCREAMING_SNAKE_CASE )]


SCREAMING_SNAKE_CASE__     :     str		    =       Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       sorted({word.strip().lower() for word in data.splitlines()})

SCREAMING_SNAKE_CASE__     :     Tuple		    =       collections.defaultdict(list)
for word in word_list:
				word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

				with open("anagrams.txt", "w") as file:
								file.write("all_anagrams = \n ")
								file.write(pprint.pformat(all_anagrams))






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__     :     Union[str, Any]		    =       False
SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       False

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[Any]:


			'''simple docstring'''







			return TrainCommand(SCREAMING_SNAKE_CASE )



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	@staticmethod
	def 						A_(							SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""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=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=32      ,     help='Batch size for training.'      )
				train_parser.add_argument('--valid_batch_size'      ,     type=SCREAMING_SNAKE_CASE      ,     default=64      ,     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      ,     SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""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=SCREAMING_SNAKE_CASE      )
				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 						A_(							self      )  ->					Dict:




				"""simple docstring"""



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





	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				raise NotImplementedError





	def 						A_(							self      )  ->					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      )
