



import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		FunnelConfig.from_json_file(__snake_case    )
      print(F"""Building PyTorch model from configuration: {config}"""    )
      UpperCAmelCase_  :	str				      =		FunnelBaseModel(__snake_case    ) if base_model else FunnelModel(__snake_case    )

      # Load weights from tf checkpoint
      load_tf_weights_in_funnel(__snake_case					,  __snake_case					,  __snake_case    )

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""    )
      torch.save(model.state_dict()					,  __snake_case    )


if __name__ == "__main__":
   __lowerCamelCase         =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
   )
   __lowerCamelCase         =			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
   )





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    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      return TrainCommand(__snake_case    )





class   snake_case_ (lowercase__  ):




       """simple docstring"""






       @staticmethod
       def  A_   (     lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		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     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		logging.get_logger("transformers-cli/training")

             UpperCAmelCase_  :	List[Any]				      =		"tf" if is_tf_available() else "torch"

             os.makedirs(args.output     ,exist_ok=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		args.output

             UpperCAmelCase_  :	Optional[int]				      =		args.column_label
             UpperCAmelCase_  :	Dict				      =		args.column_text
             UpperCAmelCase_  :	Optional[Any]				      =		args.column_id

             self.logger.info(F"""Loading {args.task} pipeline for {args.model}""")
             if args.task == "text_classification":
                   UpperCAmelCase_  :	List[Any]				      =		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_  :	str				      =		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_  :	str				      =		None
             if args.validation_data:
                   self.logger.info(F"""Loading validation dataset from {args.validation_data}""")
                   UpperCAmelCase_  :	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     ,)

             UpperCAmelCase_  :	List[str]				      =		args.validation_split
             UpperCAmelCase_  :	Tuple				      =		args.train_batch_size
             UpperCAmelCase_  :	Optional[int]				      =		args.valid_batch_size
             UpperCAmelCase_  :	Dict				      =		args.learning_rate
             UpperCAmelCase_  :	List[str]				      =		args.adam_epsilon



       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             raise NotImplementedError



       def  A_   (     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)
