



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
a_      :      Union[str, Any]    =     False
a_      :      str    =     False






def     __a					(		__UpperCAmelCase       ):
 return TrainCommand(__UpperCAmelCase       )

class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 @staticmethod
 def 						_UpperCAmelCase       (	SCREAMING_SNAKE_CASE      ) ->			Dict:
  a__												= 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=3_2		,				help='''Batch size for training.'''      )
  train_parser.add_argument('''--valid_batch_size'''		,				type=SCREAMING_SNAKE_CASE		,				default=6_4		,				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-0_8		,				help='''Epsilon for Adam optimizer.'''      )
  train_parser.set_defaults(func=SCREAMING_SNAKE_CASE      )

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

  a__												= '''tf''' if is_tf_available() else '''torch'''

  os.makedirs(args.output		,				exist_ok=SCREAMING_SNAKE_CASE      )
  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 						_UpperCAmelCase       (	self      ) ->			Tuple:
  if self.framework == "tf":
   return self.run_tf()
  return self.run_torch()

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  raise NotImplementedError



 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  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      )










import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def     __a					(		__UpperCAmelCase       ):
 if "model" in orig_key:
  a__												= orig_key.replace('''model.'''							,  ''''''       )
 if "norm1" in orig_key:
  a__												= orig_key.replace('''norm1'''							,  '''attention.output.LayerNorm'''       )
 if "norm2" in orig_key:
  a__												= orig_key.replace('''norm2'''							,  '''output.LayerNorm'''       )
 if "norm" in orig_key:
  a__												= orig_key.replace('''norm'''							,  '''LayerNorm'''       )
 if "transformer" in orig_key:
  a__												= orig_key.split('''.'''       )[0].split('''_'''       )[-1]
  a__												= orig_key.replace(f"transformer_{layer_num}"							,  f"encoder.layer.{layer_num}"       )
 if "mha.attn" in orig_key:
  a__												= orig_key.replace('''mha.attn'''							,  '''attention.self'''       )
 if "mha" in orig_key:
  a__												= orig_key.replace('''mha'''							,  '''attention'''       )
 if "W_q" in orig_key:
  a__												= orig_key.replace('''W_q'''							,  '''self.query'''       )
 if "W_k" in orig_key:
  a__												= orig_key.replace('''W_k'''							,  '''self.key'''       )
 if "W_v" in orig_key:
  a__												= orig_key.replace('''W_v'''							,  '''self.value'''       )
 if "ff1" in orig_key:
  a__												= orig_key.replace('''ff1'''							,  '''intermediate.dense'''       )
 if "ff2" in orig_key:
  a__												= orig_key.replace('''ff2'''							,  '''output.dense'''       )
 if "ff" in orig_key:
  a__												= orig_key.replace('''ff'''							,  '''output.dense'''       )
 if "mlm_class" in orig_key:
  a__												= orig_key.replace('''mlm.mlm_class'''							,  '''cls.predictions.decoder'''       )
 if "mlm" in orig_key:
  a__												= orig_key.replace('''mlm'''							,  '''cls.predictions.transform'''       )
 if "cls" not in orig_key:
  a__												= '''yoso.''' + orig_key

 return orig_key






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 for key in orig_state_dict.copy().keys():
  a__												= orig_state_dict.pop(__UpperCAmelCase       )

  if ("pooler" in key) or ("sen_class" in key):
   continue
  else:
   a__												= val

 a__												= orig_state_dict['''cls.predictions.decoder.bias''']
 a__												= torch.arange(__UpperCAmelCase       ).expand((1, -1)       ) + 2

 return orig_state_dict






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= torch.load(__UpperCAmelCase							,  map_location='''cpu'''       )['''model_state_dict''']
 a__												= YosoConfig.from_json_file(__UpperCAmelCase       )
 a__												= YosoForMaskedLM(__UpperCAmelCase       )

 a__												= convert_checkpoint_helper(config.max_position_embeddings							,  __UpperCAmelCase       )

 print(model.load_state_dict(__UpperCAmelCase       )       )
 model.eval()
 model.save_pretrained(__UpperCAmelCase       )

 print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"       )


if __name__ == "__main__":
     a_      :      int    =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for YOSO model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_      :      Any    =     parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





