





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_   :      str			=   False



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		return TrainCommand(__UpperCAmelCase							)


class 		snake_case_   (							_lowercase						):







	'''simple docstring'''




	@staticmethod
	def    UpperCAmelCase			(						__lowerCamelCase							:     Optional[Any]   )       -> Dict:

			'''simple docstring'''




			__lowercase											=     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-0_8					,      help='Epsilon for Adam optimizer.'   )
			train_parser.set_defaults(func=__lowerCamelCase   )




	def __init__(						self							:     Optional[Any]					,      __lowerCamelCase							:     str   )       -> int:

			'''simple docstring'''




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

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

			os.makedirs(args.output					,      exist_ok=__lowerCamelCase   )
			__lowercase											=     args.output

			__lowercase											=     args.column_label
			__lowercase											=     args.column_text
			__lowercase											=     args.column_id

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

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




	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Tuple:

			'''simple docstring'''




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




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> Optional[int]:

			'''simple docstring'''




			raise NotImplementedError






	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Tuple:

			'''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   )









from __future__ import annotations

import requests






def     __a					(		__UpperCAmelCase       ):
 a__												= f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
 return requests.get(__UpperCAmelCase       ).json()






def     __a					(		__UpperCAmelCase = 10       ):
 a__												= '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
 a__												= requests.get(__UpperCAmelCase       ).json()[:max_stories]
 return [get_hackernews_story(__UpperCAmelCase       ) for story_id in story_ids]






def     __a					(		__UpperCAmelCase = 10       ):
 a__												= hackernews_top_stories(__UpperCAmelCase       )
 return "\n".join('''* [{title}]({url})'''.format(**__UpperCAmelCase       ) for story in stories       )


if __name__ == "__main__":
     print(hackernews_top_stories_as_markdown())





