'''simple docstring'''




from argparse import ArgumentParser

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


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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    Dict:


       """simple docstring"""







       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(_UpperCAmelCase    ):
                     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 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    Tuple:


       """simple docstring"""







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


class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				str						,					A						:				Pipeline						,					A						:				PipelineDataFormat       ):
             _UpperCAmelCase     :  Any							      =    nlp
             _UpperCAmelCase     :  List[Any]							      =    reader




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





      def      _A	(       self						:				int       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    self._nlp, []

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

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








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A									=				logging.get_logger(__name__)

__A									=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A									=				{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A									=				{
    "yjernite/retribert-base-uncased": 512,
}


__A									=				{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			PRETRAINED_INIT_CONFIGURATION
 lowercase_		=			RetriBertTokenizer
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   int       , UpperCAmelCase_				:   List[str]=None       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   Union[str, Any]="[UNK]"       , UpperCAmelCase_				:   Any="[SEP]"       , UpperCAmelCase_				:   List[str]="[PAD]"       , UpperCAmelCase_				:   Optional[Any]="[CLS]"       , UpperCAmelCase_				:   Optional[Any]="[MASK]"       , UpperCAmelCase_				:   Dict=True       , UpperCAmelCase_				:   str=None       , **UpperCAmelCase_				:   str       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(
       UpperCAmelCase_       , tokenizer_file=UpperCAmelCase_       , do_lower_case=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , tokenize_chinese_chars=UpperCAmelCase_       , strip_accents=UpperCAmelCase_       , **UpperCAmelCase_       , )

   lowerCamelCase__:      List[Any]			  =json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase"       , UpperCAmelCase_) != do_lower_case
       or normalizer_state.get("strip_accents"       , UpperCAmelCase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars"       , UpperCAmelCase_) != tokenize_chinese_chars
   ):
     lowerCamelCase__:      Dict			  =getattr(UpperCAmelCase_       , normalizer_state.pop("type"))
     lowerCamelCase__:      int			  =do_lower_case
     lowerCamelCase__:      int			  =strip_accents
     lowerCamelCase__:      List[str]			  =tokenize_chinese_chars
     lowerCamelCase__:      Tuple			  =normalizer_class(**UpperCAmelCase_)

   lowerCamelCase__:      Any			  =do_lower_case

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Optional[Any]=None)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =[self.cls_token_id] + token_ids_a + [self.sep_token_id]

   if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

   return output

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =[self.sep_token_id]
   lowerCamelCase__:      Optional[int]			  =[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 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self._tokenizer.model.save(UpperCAmelCase_       , name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)

