





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_A					:				Tuple				=			logging.get_logger(__name__)
_A					:				Optional[int]				=			{"""vocab_file""": """spiece.model"""}

_A					:				List[Any]				=			{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

_A					:				Tuple				=			{
    """albert-base-v1""": 5_12,
    """albert-large-v1""": 5_12,
    """albert-xlarge-v1""": 5_12,
    """albert-xxlarge-v1""": 5_12,
    """albert-base-v2""": 5_12,
    """albert-large-v2""": 5_12,
    """albert-xlarge-v2""": 5_12,
    """albert-xxlarge-v2""": 5_12,
}

_A					:				Optional[Any]				=			"""▁"""



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	VOCAB_FILES_NAMES
    lowerCamelCase__    :		Optional[Any]				=	PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase__    :		List[Any]				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



    def __init__(       self   ,				A_   ,				A_=True   ,				A_=True   ,				A_=False   ,				A_="[CLS]"   ,				A_="[SEP]"   ,				A_="<unk>"   ,				A_="[SEP]"   ,				A_="<pad>"   ,				A_="[CLS]"   ,				A_="[MASK]"   ,				A_ = None   ,				**A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			(
            AddedToken(A_   ,				lstrip=A_   ,				rstrip=A_   ,				normalized=A_     )
            if isinstance(A_   ,				A_     )
            else mask_token
        )

        SCREAMING_SNAKE_CASE__											=			{} if sp_model_kwargs is None else sp_model_kwargs

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

        SCREAMING_SNAKE_CASE__											=			do_lower_case
        SCREAMING_SNAKE_CASE__											=			remove_space
        SCREAMING_SNAKE_CASE__											=			keep_accents
        SCREAMING_SNAKE_CASE__											=			vocab_file

        SCREAMING_SNAKE_CASE__											=			spm.SentencePieceProcessor(**self.sp_model_kwargs     )
        self.sp_model.Load(A_     )



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return len(self.sp_model     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			{self.convert_ids_to_tokens(A_     ): i for i in range(self.vocab_size     )}
        vocab.update(self.added_tokens_encoder     )
        return vocab



    def __getstate__(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.__dict__.copy()
        SCREAMING_SNAKE_CASE__											=			None
        return state



    def __setstate__(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			d

        # for backward compatibility
        if not hasattr(self   ,				'''sp_model_kwargs'''     ):
            SCREAMING_SNAKE_CASE__											=			{}

        SCREAMING_SNAKE_CASE__											=			spm.SentencePieceProcessor(**self.sp_model_kwargs     )
        self.sp_model.Load(self.vocab_file     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        if self.remove_space:
            SCREAMING_SNAKE_CASE__											=			''' '''.join(inputs.strip().split()     )
        else:
            SCREAMING_SNAKE_CASE__											=			inputs
        SCREAMING_SNAKE_CASE__											=			outputs.replace('''``'''   ,				'''"'''     ).replace('''\'\''''   ,				'''"'''     )

        if not self.keep_accents:
            SCREAMING_SNAKE_CASE__											=			unicodedata.normalize('''NFKD'''   ,				A_     )
            SCREAMING_SNAKE_CASE__											=			''''''.join([c for c in outputs if not unicodedata.combining(A_     )]     )
        if self.do_lower_case:
            SCREAMING_SNAKE_CASE__											=			outputs.lower()

        return outputs



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.preprocess_text(A_     )
        SCREAMING_SNAKE_CASE__											=			self.sp_model.encode(A_   ,				out_type=A_     )
        SCREAMING_SNAKE_CASE__											=			[]
        for piece in pieces:
            if len(A_     ) > 1 and piece[-1] == str(''','''     ) and piece[-2].isdigit():
                SCREAMING_SNAKE_CASE__											=			self.sp_model.EncodeAsPieces(piece[:-1].replace(A_   ,				''''''     )     )
                if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                    if len(cur_pieces[0]     ) == 1:
                        SCREAMING_SNAKE_CASE__											=			cur_pieces[1:]
                    else:
                        SCREAMING_SNAKE_CASE__											=			cur_pieces[0][1:]
                cur_pieces.append(piece[-1]     )
                new_pieces.extend(A_     )
            else:
                new_pieces.append(A_     )

        return new_pieces



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        return self.sp_model.PieceToId(A_     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        return self.sp_model.IdToPiece(A_     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[]
        SCREAMING_SNAKE_CASE__											=			''''''
        SCREAMING_SNAKE_CASE__											=			False
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special:
                    out_string += " "
                out_string += self.sp_model.decode(A_     ) + token
                SCREAMING_SNAKE_CASE__											=			True
                SCREAMING_SNAKE_CASE__											=			[]
            else:
                current_sub_tokens.append(A_     )
                SCREAMING_SNAKE_CASE__											=			False
        out_string += self.sp_model.decode(A_     )
        return out_string.strip()



    def  lowercase_					(       self   ,				A_   ,				A_ = None     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[self.sep_token_id]
        SCREAMING_SNAKE_CASE__											=			[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  lowercase_					(       self   ,				A_   ,				A_ = None   ,				A_ = False     ):





        '''simple docstring'''

        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=A_   ,				token_ids_a=A_   ,				already_has_special_tokens=A_     )

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



    def  lowercase_					(       self   ,				A_   ,				A_ = None     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[self.sep_token_id]
        SCREAMING_SNAKE_CASE__											=			[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  lowercase_					(       self   ,				A_   ,				A_ = None     ):





        '''simple docstring'''

        if not os.path.isdir(A_     ):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
            return
        SCREAMING_SNAKE_CASE__											=			os.path.join(
            A_   ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

        if os.path.abspath(self.vocab_file     ) != os.path.abspath(A_     ) and os.path.isfile(self.vocab_file     ):
            copyfile(self.vocab_file   ,				A_     )
        elif not os.path.isfile(self.vocab_file     ):
            with open(A_   ,				'''wb'''     ) as fi:
                SCREAMING_SNAKE_CASE__											=			self.sp_model.serialized_model_proto()
                fi.write(A_     )

        return (out_vocab_file,)




from argparse import ArgumentParser

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


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


def     lowercase	(    a ):



		'''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     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						pipeline(
		    task=args.task     , model=args.model if args.model else None     , config=args.config     , tokenizer=args.tokenizer     , device=args.device     , )
		SCREAMING_SNAKE_CASE_				:Any				     =						try_infer_format_from_ext(args.input ) if args.format == "infer" else args.format
		SCREAMING_SNAKE_CASE_				: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      (     lowercase							):






		def __init__(						self				:     List[Any]   ,  UpperCAmelCase				:     Pipeline   ,  UpperCAmelCase				:     PipelineDataFormat):
				SCREAMING_SNAKE_CASE_				:int				     =						nlp
				SCREAMING_SNAKE_CASE_				:Any				     =						reader






		@staticmethod
		def _snake_case		(						UpperCAmelCase				:     ArgumentParser):
				SCREAMING_SNAKE_CASE_				: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=UpperCAmelCase   ,  help="Path to the file to use for inference")
				run_parser.add_argument("--output"   ,  type=UpperCAmelCase   ,  help="Path to the file that will be used post to write results.")
				run_parser.add_argument("--model"   ,  type=UpperCAmelCase   ,  help="Name or path to the model to instantiate.")
				run_parser.add_argument("--config"   ,  type=UpperCAmelCase   ,  help="Name or path to the model's config to instantiate.")
				run_parser.add_argument(
				    "--tokenizer"   ,  type=UpperCAmelCase   ,  help="Name of the tokenizer to use. (default: same as the model name)")
				run_parser.add_argument(
				    "--column"   ,  type=UpperCAmelCase   ,  help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"   ,  )
				run_parser.add_argument(
				    "--format"   ,  type=UpperCAmelCase   ,  default="infer"   ,  choices=PipelineDataFormat.SUPPORTED_FORMATS   ,  help="Input format to read from"   ,  )
				run_parser.add_argument(
				    "--device"   ,  type=UpperCAmelCase   ,  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=UpperCAmelCase)







		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self._nlp, []

				for entry in self._reader:
						SCREAMING_SNAKE_CASE_				:int				     =						nlp(**UpperCAmelCase) if self._reader.is_multi_columns else nlp(UpperCAmelCase)
						if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
								outputs.append(UpperCAmelCase)
						else:
								outputs += output

        # Saving data
				if self._nlp.binary_output:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self._reader.save_binary(UpperCAmelCase)
						logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}")
				else:
						self._reader.save(UpperCAmelCase)






