




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__     :     List[Any]		    =       {
    "configuration_squeezebert": [
        "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SqueezeBertConfig",
        "SqueezeBertOnnxConfig",
    ],
    "tokenization_squeezebert": ["SqueezeBertTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				SCREAMING_SNAKE_CASE__     :     int		    =       ["SqueezeBertTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       [
				    "SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "SqueezeBertForMaskedLM",
				    "SqueezeBertForMultipleChoice",
				    "SqueezeBertForQuestionAnswering",
				    "SqueezeBertForSequenceClassification",
				    "SqueezeBertForTokenClassification",
				    "SqueezeBertModel",
				    "SqueezeBertModule",
				    "SqueezeBertPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_squeezebert import (
				    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SqueezeBertConfig,
				    SqueezeBertOnnxConfig,
				)
				from .tokenization_squeezebert import SqueezeBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_squeezebert import (
								    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SqueezeBertForMaskedLM,
								    SqueezeBertForMultipleChoice,
								    SqueezeBertForQuestionAnswering,
								    SqueezeBertForSequenceClassification,
								    SqueezeBertForTokenClassification,
								    SqueezeBertModel,
								    SqueezeBertModule,
								    SqueezeBertPreTrainedModel,
								)

else:
				import sys

				SCREAMING_SNAKE_CASE__     :     str		    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






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      )
