'''simple docstring'''

from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


lowercase_     		= {
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Optional[int]       =							'ernie_m'
		lowercase   :      Dict[str, str]       =							{"dropout": "classifier_dropout", "num_classes": "num_labels"}






		def __init__(   self		,						__A = 25_0002		,						__A = 768		,						__A = 12		,						__A = 12		,						__A = 3072		,						__A = "gelu"		,						__A = 0.1		,						__A = 0.1		,						__A = 514		,						__A = 0.02		,						__A = 1		,						__A = 1E-05		,						__A=None		,						__A=False		,						__A=0.0		,						**__A		,						) ->     Optional[Any]:
								super().__init__(pad_token_id=__A		,						**__A				)
								_lowerCAmelCase										=vocab_size
								_lowerCAmelCase										=hidden_size
								_lowerCAmelCase										=num_hidden_layers
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=intermediate_size
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=hidden_dropout_prob
								_lowerCAmelCase										=attention_probs_dropout_prob
								_lowerCAmelCase										=max_position_embeddings
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=layer_norm_eps
								_lowerCAmelCase										=classifier_dropout
								_lowerCAmelCase										=is_decoder
								_lowerCAmelCase										=act_dropout






'''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
lowercase_     		= False
lowercase_     		= False



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return TrainCommand(a__      )


class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@staticmethod
		def 		UpperCamelCase__       (   __A				) ->     Tuple:
								_lowerCAmelCase										=parser.add_parser('train'		,						help='CLI tool to train a model on a task.'				)

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

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






		def __init__(   self		,						__A				) ->     List[str]:
								_lowerCAmelCase										=logging.get_logger('transformers-cli/training'				)

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

								os.makedirs(args.output		,						exist_ok=__A				)
								_lowerCAmelCase										=args.output

								_lowerCAmelCase										=args.column_label
								_lowerCAmelCase										=args.column_text
								_lowerCAmelCase										=args.column_id

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

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






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								if self.framework == "tf":
														return self.run_tf()
								return self.run_torch()






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								raise NotImplementedError






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								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				)





