
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__							:							str									=			False
lowercase__							:							Tuple									=			False




def 	A_ (							snake_case							:   Namespace )      ->    int:



						'''simple docstring'''

						return TrainCommand(_lowerCamelCase )






class        SCREAMING_SNAKE_CASE__    (							__snake_case    ):

							"""simple docstring"""






							@staticmethod
							def     A__		(  SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			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=32     ,	help='''Batch size for training.'''			)
													train_parser.add_argument('''--valid_batch_size'''     ,	type=_lowercase     ,	default=64     ,	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     ,	SCREAMING_SNAKE_CASE_			)->   Union[str, Any]:





													'''simple docstring'''






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

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

													os.makedirs(args.output     ,	exist_ok=_lowercase			)
													__UpperCamelCase       =			args.output

													__UpperCamelCase       =			args.column_label
													__UpperCamelCase       =			args.column_text
													__UpperCamelCase       =			args.column_id

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

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



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






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



							def     A__		(  self			)->   int:





													'''simple docstring'''






													raise NotImplementedError



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





													'''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			)






# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


lowercase__							:							List[str]									=			"3"

print("Python version:", sys.version)

print("OS platform:", platform.platform())
print("OS architecture:", platform.machine())

try:
				import torch

				print("Torch version:", torch.__version__)
				print("Cuda available:", torch.cuda.is_available())
				print("Cuda version:", torch.version.cuda)
				print("CuDNN version:", torch.backends.cudnn.version())
				print("Number of GPUs available:", torch.cuda.device_count())
except ImportError:
				print("Torch version:", None)

try:
				import transformers

				print("transformers version:", transformers.__version__)
except ImportError:
				print("transformers version:", None)




