
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   =	False
lowercase   =	False



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Namespace							)	->     Tuple:
							return TrainCommand(__snake_case							)





class    __A(					a_							):

					@staticmethod
					def  lowercase__   (						__UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       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=3_2 ,       help="""Batch size for training.""" )
												train_parser.add_argument("""--valid_batch_size""" ,       type=_a ,       default=6_4 ,       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       :  Tuple ,       __UpperCamelCase       :  Tuple ):
												lowerCamelCase_					=       logging.get_logger("""transformers-cli/training""" )

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

												os.makedirs(args.output ,       exist_ok=_a )
												lowerCamelCase_					=       args.output

												lowerCamelCase_					=       args.column_label
												lowerCamelCase_					=       args.column_text
												lowerCamelCase_					=       args.column_id

												self.logger.info(F'''Loading {args.task} pipeline for {args.model}''' )
												if args.task == "text_classification":
																			lowerCamelCase_					=       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_					=       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_					=       None
												if args.validation_data:
																			self.logger.info(F'''Loading validation dataset from {args.validation_data}''' )
																			lowerCamelCase_					=       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_					=       args.validation_split
												lowerCamelCase_					=       args.train_batch_size
												lowerCamelCase_					=       args.valid_batch_size
												lowerCamelCase_					=       args.learning_rate
												lowerCamelCase_					=       args.adam_epsilon

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

					def  lowercase__   (						self       :  Optional[int] ):
												raise NotImplementedError



					def  lowercase__   (						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 )







"""simple docstring"""




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_A     :			Tuple  						=			logging.get_logger(__name__)

_A     :			Dict  						=			{"""vocab_file""": """vocab.txt"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

_A     :			Optional[Any]  						=			{
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}


def     __magic_name__      ( __snake_case							:			Tuple    )					->							str:
       with open(__snake_case	,					"r"    ) as f:
              lowercase       :							Dict									=				f.read().splitlines()
              return [l.strip() for l in lines]

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   def __init__(     self      ,       _a      ,       _a="<unk>"      ,       _a="<cls>"      ,       _a="<pad>"      ,       _a="<mask>"      ,       _a="<eos>"      ,       **_a      ,       ):
          super().__init__(**_a							)
          lowercase       :							Union[str, Any]									=				load_vocab_file(_a							)
          lowercase       :							Union[str, Any]									=				dict(enumerate(self.all_tokens							)							)
          lowercase       :							Any									=				{tok: ind for ind, tok in enumerate(self.all_tokens							)}
          lowercase       :							Dict									=				unk_token
          lowercase       :							Optional[int]									=				cls_token
          lowercase       :							Optional[int]									=				pad_token
          lowercase       :							Any									=				mask_token
          lowercase       :							List[str]									=				eos_token
          lowercase       :							Tuple									=				self.all_tokens
          self._create_trie(self.unique_no_split_tokens							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a      ,       **_a							):
          return text.split()
   def      __magic_name__     (     self      ,       _a=False							):
          return len(self._id_to_token							)
   def      __magic_name__     (     self							):
          return {token: i for i, token in enumerate(self.all_tokens							)}
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[int]									=				[self.cls_token_id]
          lowercase       :							Dict									=				[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
                 if self.eos_token_id is None:
                        return cls + token_ids_a
                 else:
                        return cls + token_ids_a + sep
          elif self.eos_token_id is None:
                 raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"							)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = False							):
          if already_has_special_tokens:
                 if token_ids_a is not None:
                        raise ValueError(
                            "You should not supply a second sequence if the provided sequence of "
                            "ids is already formatted with special tokens for the model."							)

                 return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          lowercase       :							Union[str, Any]									=				[1] + ([0] * len(_a							)) + [1]
          if token_ids_a is not None:
                 mask += [0] * len(_a							) + [1]
          return mask
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Optional[Any]									=				os.path.join(_a      ,       (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"							)
          with open(_a      ,       "w"							) as f:
                 f.write("\n".join(self.all_tokens							)							)
          return (vocab_file,)
   @property
   def      __magic_name__     (     self							):
          return self.get_vocab_size(with_added_tokens=_a							)

   def      __magic_name__     (     self      ,       _a      ,       _a = False							):
          return super()._add_tokens(_a      ,       special_tokens=_a							)


