
'''simple docstring'''

from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__snake_case:  str								=			logging.get_logger(__name__)  # pylint: disable=invalid-name






def 	_snake_case						(      A_       :    str   ):
		"""simple docstring"""







		if not path:
				return "pipe"

		for ext in PipelineDataFormat.SUPPORTED_FORMATS:
				if path.endswith(A_   ):
						return ext

		raise Exception(
		    f'''Unable to determine file format from file extension {path}. '''
		    f'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'''   )






def 	_snake_case						(      A_       :    int   ):
		"""simple docstring"""







		a_      :	int      =							pipeline(
		    task=args.task     ,		model=args.model if args.model else None     ,		config=args.config     ,		tokenizer=args.tokenizer     ,		device=args.device     ,		)
		a_      :	Dict      =							try_infer_format_from_ext(args.input   ) if args.format == """infer""" else args.format
		a_      :	List[str]      =							PipelineDataFormat.from_str(
		    format=A_     ,		output_path=args.output     ,		input_path=args.input     ,		column=args.column if args.column else nlp.default_input_names     ,		overwrite=args.overwrite     ,		)
		return RunCommand(A_     ,		A_   )




class  _UpperCAmelCase ( lowerCAmelCase__    ):

					"""simple docstring"""

					def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

							'''simple docstring'''

							a_      :	Union[str, Any]      =							nlp
							a_      :	Any      =							reader


					@staticmethod
					def 							_lowerCAmelCase  (      lowerCAmelCase_ ):

							'''simple docstring'''

							a_      :	List[Any]      =							parser.add_parser("""run"""							,		help="""Run a pipeline through the CLI""" )
							run_parser.add_argument("""--task"""							,		choices=get_supported_tasks()							,		help="""Task to run""" )
							run_parser.add_argument("""--input"""							,		type=lowerCAmelCase_							,		help="""Path to the file to use for inference""" )
							run_parser.add_argument("""--output"""							,		type=lowerCAmelCase_							,		help="""Path to the file that will be used post to write results.""" )
							run_parser.add_argument("""--model"""							,		type=lowerCAmelCase_							,		help="""Name or path to the model to instantiate.""" )
							run_parser.add_argument("""--config"""							,		type=lowerCAmelCase_							,		help="""Name or path to the model's config to instantiate.""" )
							run_parser.add_argument(
							    """--tokenizer"""							,		type=lowerCAmelCase_							,		help="""Name of the tokenizer to use. (default: same as the model name)""" )
							run_parser.add_argument(
							    """--column"""							,		type=lowerCAmelCase_							,		help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""							,		)
							run_parser.add_argument(
							    """--format"""							,		type=lowerCAmelCase_							,		default="""infer"""							,		choices=PipelineDataFormat.SUPPORTED_FORMATS							,		help="""Input format to read from"""							,		)
							run_parser.add_argument(
							    """--device"""							,		type=lowerCAmelCase_							,		default=-1							,		help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""							,		)
							run_parser.add_argument("""--overwrite"""							,		action="""store_true"""							,		help="""Allow overwriting the output file.""" )
							run_parser.set_defaults(func=lowerCAmelCase_ )






					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	Optional[Any]      =							self._nlp, []

							for entry in self._reader:
									a_      :	Optional[int]      =							nlp(**lowerCAmelCase_ ) if self._reader.is_multi_columns else nlp(lowerCAmelCase_ )
									if isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ):
											outputs.append(lowerCAmelCase_ )
									else:
											outputs += output

        # Saving data
							if self._nlp.binary_output:
									a_      :	int      =							self._reader.save_binary(lowerCAmelCase_ )
									logger.warning(f'''Current pipeline requires output to be in binary format, saving at {binary_path}''' )
							else:
									self._reader.save(lowerCAmelCase_ )




'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case:  Optional[Any]								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{"vocab_file": "sentencepiece.model"}

__snake_case:  int								=			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

__snake_case:  Union[str, Any]								=			{
    "google/rembert": 2_56,
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					VOCAB_FILES_NAMES
     a_    	=					PRETRAINED_VOCAB_FILES_MAP
     a_    	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_="[CLS]"							,		lowerCAmelCase_="[SEP]"							,		lowerCAmelCase_="[UNK]"							,		lowerCAmelCase_="[SEP]"							,		lowerCAmelCase_="[PAD]"							,		lowerCAmelCase_="[CLS]"							,		lowerCAmelCase_="[MASK]"							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(
           do_lower_case=lowerCAmelCase_							,		remove_space=lowerCAmelCase_							,		keep_accents=lowerCAmelCase_							,		bos_token=lowerCAmelCase_							,		eos_token=lowerCAmelCase_							,		unk_token=lowerCAmelCase_							,		sep_token=lowerCAmelCase_							,		pad_token=lowerCAmelCase_							,		cls_token=lowerCAmelCase_							,		mask_token=lowerCAmelCase_							,		**lowerCAmelCase_							,		)

       a_      :	int      =							do_lower_case
       a_      :	int      =							remove_space
       a_      :	Dict      =							keep_accents
       a_      :	Union[str, Any]      =							vocab_file

       a_      :	List[str]      =							spm.SentencePieceProcessor()
       self.sp_model.Load(lowerCAmelCase_ )


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return len(self.sp_model )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							{self.convert_ids_to_tokens(lowerCAmelCase_ ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab


     def __getstate__(      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.__dict__.copy()
       a_      :	Optional[Any]      =							None
       return state


     def __setstate__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	int      =							d
       a_      :	Optional[int]      =							spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_=False ):

       '''simple docstring'''

       a_      :	Tuple      =							self.sp_model.EncodeAsPieces(lowerCAmelCase_ )
       return pieces


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.sp_model.PieceToId(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.sp_model.IdToPiece(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Tuple      =							self.sp_model.decode_pieces(lowerCAmelCase_ )
       return out_string


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	List[Any]      =							[self.sep_token_id]
       a_      :	Tuple      =							[self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = False ):

       '''simple docstring'''

       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 x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(lowerCAmelCase_ )) + [1] + ([0] * len(lowerCAmelCase_ )) + [1]
       return [1] + ([0] * len(lowerCAmelCase_ )) + [1]


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	Optional[int]      =							[self.sep_token_id]
       a_      :	Optional[Any]      =							[self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep ) * [0]
       return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       if not os.path.isdir(lowerCAmelCase_ ):
         logger.error("""Vocabulary path ({}) should be a directory""".format(lowerCAmelCase_ ) )
         return
       a_      :	List[str]      =							os.path.join(
           lowerCAmelCase_							,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

       if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase_ ):
         copyfile(self.vocab_file							,		lowerCAmelCase_ )

       return (out_vocab_file,)


