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      )



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_lowerCamelCase					:   Tuple							=		{
    '''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 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							"""ernie_m"""
							lowercase__						:       Dict[str, str]								=							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					int = 250_002	,							_UpperCamelCase   :					int = 768	,							_UpperCamelCase   :					int = 12	,							_UpperCamelCase   :					int = 12	,							_UpperCamelCase   :					int = 3_072	,							_UpperCamelCase   :					str = "gelu"	,							_UpperCamelCase   :					float = 0.1	,							_UpperCamelCase   :					float = 0.1	,							_UpperCamelCase   :					int = 514	,							_UpperCamelCase   :					float = 0.0_2	,							_UpperCamelCase   :					int = 1	,							_UpperCamelCase   :					float = 1e-05	,							_UpperCamelCase   :					int=None	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					int=0.0	,							**_UpperCamelCase   :					Union[str, Any]	,							)					->				Optional[Any]:



												'''simple docstring'''

												super().__init__(pad_token_id=_UpperCamelCase	,							**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       vocab_size
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       max_position_embeddings
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       layer_norm_eps
												SCREAMING_SNAKE_CASE    			=       classifier_dropout
												SCREAMING_SNAKE_CASE    			=       is_decoder
												SCREAMING_SNAKE_CASE    			=       act_dropout




