






'''simple docstring'''

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__											=     False
SCREAMING_SNAKE_CASE__											=     False







def 					lowercase__				(   __UpperCamelCase					)->				str:
      return TrainCommand(__UpperCamelCase					)






class        a_		(    __SCREAMING_SNAKE_CASE       ):

  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      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    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      logging.get_logger("""transformers-cli/training"""   )

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

        os.makedirs(args.output    ,	exist_ok=_a   )
        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   )						->   Any:



        """simple docstring"""






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

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



        """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   )













'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      UpperCamelCase       						=      [
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase	,		__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase						,UpperCamelCase       						=      emb.weight.shape
      UpperCamelCase       						=      nn.Linear(__UpperCamelCase	,		__UpperCamelCase	,		bias=__UpperCamelCase					)
      UpperCamelCase       						=      emb.weight.data
      return lin_layer







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)
      UpperCamelCase       						=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
      UpperCamelCase       						=      mam_aaa["""model"""]
      remove_ignore_keys_(__UpperCamelCase					)
      UpperCamelCase       						=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

      UpperCamelCase       						=      MaMaaaConfig(
          vocab_size=__UpperCamelCase	,		max_position_embeddings=1024	,		encoder_layers=args.encoder_layers	,		decoder_layers=args.decoder_layers	,		encoder_attention_heads=args.encoder_attention_heads	,		decoder_attention_heads=args.decoder_attention_heads	,		encoder_ffn_dim=args.encoder_ffn_embed_dim	,		decoder_ffn_dim=args.decoder_ffn_embed_dim	,		d_model=args.encoder_embed_dim	,		encoder_layerdrop=args.encoder_layerdrop	,		decoder_layerdrop=args.decoder_layerdrop	,		dropout=args.dropout	,		attention_dropout=args.attention_dropout	,		activation_dropout=args.activation_dropout	,		activation_function="""relu"""	,		)

      UpperCamelCase       						=      state_dict["""decoder.embed_tokens.weight"""]
      UpperCamelCase       						=      MaMaaaForConditionalGeneration(__UpperCamelCase					)
      model.model.load_state_dict(__UpperCamelCase	,		strict=__UpperCamelCase					)
      UpperCamelCase       						=      make_linear_from_emb(model.model.shared					)

      return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     SCREAMING_SNAKE_CASE__											=     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)
