



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 		lowerCAmelCase__    (					UpperCamelCase_				:       Namespace	)->  Optional[int]:
    return TrainCommand(_lowerCamelCase	)



class     _UpperCAmelCase			(       __snake_case     ):




     @staticmethod
     def 		snake_case_		(				a__):
         A__		 =					parser.add_parser('''train''' ,						help='''CLI tool to train a model on a task.''')

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

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




     def __init__(				self ,						a__):
         A__		 =					logging.get_logger('''transformers-cli/training''')

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

         os.makedirs(args.output ,						exist_ok=_lowercase)
         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 		snake_case_		(				self):

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




     def 		snake_case_		(				self):
         raise NotImplementedError




     def 		snake_case_		(				self):
         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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_lowercase						  =		{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''albert'''




     def __init__(				self ,						a__=3_0_0_0_0 ,						a__=1_2_8 ,						a__=4_0_9_6 ,						a__=1_2 ,						a__=1 ,						a__=6_4 ,						a__=1_6_3_8_4 ,						a__=1 ,						a__="gelu_new" ,						a__=0 ,						a__=0 ,						a__=5_1_2 ,						a__=2 ,						a__=0.0_2 ,						a__=1e-12 ,						a__=0.1 ,						a__="absolute" ,						a__=0 ,						a__=2 ,						a__=3 ,						**a__ ,						):
         super().__init__(pad_token_id=a__ ,						bos_token_id=a__ ,						eos_token_id=a__ ,						**a__)

         A__		 =					vocab_size
         A__		 =					embedding_size
         A__		 =					hidden_size
         A__		 =					num_hidden_layers
         A__		 =					num_hidden_groups
         A__		 =					num_attention_heads
         A__		 =					inner_group_num
         A__		 =					hidden_act
         A__		 =					intermediate_size
         A__		 =					hidden_dropout_prob
         A__		 =					attention_probs_dropout_prob
         A__		 =					max_position_embeddings
         A__		 =					type_vocab_size
         A__		 =					initializer_range
         A__		 =					layer_norm_eps
         A__		 =					classifier_dropout_prob
         A__		 =					position_embedding_type







class     _UpperCAmelCase			(       A__     ):




     @property
     def 		snake_case_		(				self):
         if self.task == "multiple-choice":
             A__		 =					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__		 =					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ])
