





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			       =      False
_lowerCamelCase			       =      False




def    __UpperCAmelCase(      lowercase_      ):
       return TrainCommand(lowercase_      )







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		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__):



             """simple docstring"""





             _lowerCamelCase							:     int	=		logging.get_logger('''transformers-cli/training''')

             _lowerCamelCase							:     Tuple	=		'''tf''' if is_tf_available() else '''torch'''

             os.makedirs(args.output      ,							exist_ok=a__)
             _lowerCamelCase							:     List[Any]	=		args.output

             _lowerCamelCase							:     List[str]	=		args.column_label
             _lowerCamelCase							:     Optional[int]	=		args.column_text
             _lowerCamelCase							:     Optional[Any]	=		args.column_id

             self.logger.info(F"""Loading {args.task} pipeline for {args.model}""")
             if args.task == "text_classification":
                    _lowerCamelCase							:     str	=		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							:     Union[str, Any]	=		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							:     str	=		None
             if args.validation_data:
                    self.logger.info(F"""Loading validation dataset from {args.validation_data}""")
                    _lowerCamelCase							:     Union[str, Any]	=		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							:     List[str]	=		args.validation_split
             _lowerCamelCase							:     Optional[Any]	=		args.train_batch_size
             _lowerCamelCase							:     int	=		args.valid_batch_size
             _lowerCamelCase							:     List[str]	=		args.learning_rate
             _lowerCamelCase							:     Any	=		args.adam_epsilon


      def 				__snake_case			(		self):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             raise NotImplementedError



      def 				__snake_case			(		self):



             """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)








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_lowerCamelCase			       =      'Create a default config file for Accelerate with only a few flags set.'




def    __UpperCAmelCase(      lowercase_="no" ,    lowercase_ = default_json_config_file ,    lowercase_ = False      ):
       _lowerCamelCase							:     List[str]	=		Path(lowercase_      )
       path.parent.mkdir(parents=lowercase_ ,    exist_ok=lowercase_      )
       if path.exists():
              print(
                  F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""      )
              return False
       _lowerCamelCase							:     List[Any]	=		mixed_precision.lower()
       if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
              raise ValueError(
                  F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""      )
       _lowerCamelCase							:     Optional[int]	=		{
           '''compute_environment''': '''LOCAL_MACHINE''',
           '''mixed_precision''': mixed_precision,
       }
       if torch.cuda.is_available():
              _lowerCamelCase							:     Any	=		torch.cuda.device_count()
              _lowerCamelCase							:     str	=		num_gpus
              _lowerCamelCase							:     Union[str, Any]	=		False
              if num_gpus > 1:
                     _lowerCamelCase							:     str	=		'''MULTI_GPU'''
              else:
                     _lowerCamelCase							:     List[str]	=		'''NO'''
       elif is_xpu_available() and use_xpu:
              _lowerCamelCase							:     Union[str, Any]	=		torch.xpu.device_count()
              _lowerCamelCase							:     int	=		num_xpus
              _lowerCamelCase							:     Dict	=		False
              if num_xpus > 1:
                     _lowerCamelCase							:     List[str]	=		'''MULTI_XPU'''
              else:
                     _lowerCamelCase							:     Any	=		'''NO'''
       elif is_npu_available():
              _lowerCamelCase							:     List[str]	=		torch.npu.device_count()
              _lowerCamelCase							:     Optional[int]	=		num_npus
              _lowerCamelCase							:     str	=		False
              if num_npus > 1:
                     _lowerCamelCase							:     List[Any]	=		'''MULTI_NPU'''
              else:
                     _lowerCamelCase							:     str	=		'''NO'''
       else:
              _lowerCamelCase							:     Tuple	=		0
              _lowerCamelCase							:     Optional[Any]	=		True
              _lowerCamelCase							:     List[str]	=		1
              _lowerCamelCase							:     Union[str, Any]	=		'''NO'''
       _lowerCamelCase							:     Any	=		ClusterConfig(**lowercase_      )
       config.to_json_file(lowercase_      )
       return path




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[str]	=		parser.add_parser('''default''' ,    parents=lowercase_ ,    help=lowercase_ ,    formatter_class=lowercase_      )
       parser.add_argument(
           '''--config_file''' ,    default=lowercase_ ,    help=(
               '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
               '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
               '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
               '''with \'huggingface\'.'''
           ) ,    dest='''save_location''' ,    )

       parser.add_argument(
           '''--mixed_precision''' ,    choices=['''no''', '''fp16''', '''bf16'''] ,    type=lowercase_ ,    help='''Whether or not to use mixed precision training. '''
           '''Choose between FP16 and BF16 (bfloat16) training. '''
           '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.''' ,    default='''no''' ,    )
       parser.set_defaults(func=lowercase_      )
       return parser




def    __UpperCAmelCase(      lowercase_      ):
       _lowerCamelCase							:     str	=		write_basic_config(args.mixed_precision ,    args.save_location      )
       if config_file:
              print(F"""accelerate configuration saved at {config_file}"""      )

