






'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


UpperCamelCase							=logging.get_logger(__name__)

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

UpperCamelCase							={
    "vocab_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-german-cased": (
            "https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
        ),
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase							={
    "distilbert-base-uncased": 512,
    "distilbert-base-uncased-distilled-squad": 512,
    "distilbert-base-cased": 512,
    "distilbert-base-cased-distilled-squad": 512,
    "distilbert-base-german-cased": 512,
    "distilbert-base-multilingual-cased": 512,
}


UpperCamelCase							={
    "distilbert-base-uncased": {"do_lower_case": True},
    "distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
    "distilbert-base-cased": {"do_lower_case": False},
    "distilbert-base-cased-distilled-squad": {"do_lower_case": False},
    "distilbert-base-german-cased": {"do_lower_case": False},
    "distilbert-base-multilingual-cased": {"do_lower_case": False},
}




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						str      =						VOCAB_FILES_NAMES
 __a			:						Union[str, Any]      =						PRETRAINED_VOCAB_FILES_MAP
 __a			:						str      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __a			:						Optional[int]      =						PRETRAINED_INIT_CONFIGURATION
 __a			:						List[str]      =						['''input_ids''', '''attention_mask''']
 __a			:						Any      =						DistilBertTokenizer
 def __init__(							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=True			,     __lowerCAmelCase="[UNK]"			,     __lowerCAmelCase="[SEP]"			,     __lowerCAmelCase="[PAD]"			,     __lowerCAmelCase="[CLS]"			,     __lowerCAmelCase="[MASK]"			,     __lowerCAmelCase=True			,     __lowerCAmelCase=None			,     **__lowerCAmelCase			,     ):
       super().__init__(
           _SCREAMING_SNAKE_CASE			,     tokenizer_file=_SCREAMING_SNAKE_CASE			,     do_lower_case=_SCREAMING_SNAKE_CASE			,     unk_token=_SCREAMING_SNAKE_CASE			,     sep_token=_SCREAMING_SNAKE_CASE			,     pad_token=_SCREAMING_SNAKE_CASE			,     cls_token=_SCREAMING_SNAKE_CASE			,     mask_token=_SCREAMING_SNAKE_CASE			,     tokenize_chinese_chars=_SCREAMING_SNAKE_CASE			,     strip_accents=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )

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

       UpperCamelCase_				:   Dict					    =							do_lower_case
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=None ):
       UpperCamelCase_				:   Tuple					    =							[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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       UpperCamelCase_				:   Any					    =							[self.sep_token_id]
       UpperCamelCase_				:   List[str]					    =							[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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       UpperCamelCase_				:   int					    =							self._tokenizer.model.save(_SCREAMING_SNAKE_CASE			,     name=_SCREAMING_SNAKE_CASE )
       return tuple(_SCREAMING_SNAKE_CASE )








'''simple docstring'''

from argparse import ArgumentParser

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


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



def 							snake_case					(     a_							: str	)       ->				int:





      """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_							: List[Any]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							pipeline(
          task=args.task    ,   model=args.model if args.model else None    ,   config=args.config    ,   tokenizer=args.tokenizer    ,   device=args.device    ,   )
      UpperCamelCase_				:   List[Any]					    =							try_infer_format_from_ext(args.input	) if args.format == """infer""" else args.format
      UpperCamelCase_				:   Tuple					    =							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     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							nlp
       UpperCamelCase_				:   Optional[Any]					    =							reader
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase ):
       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=__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   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self._nlp, []

       for entry in self._reader:
             UpperCamelCase_				:   List[Any]					    =							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:
             UpperCamelCase_				:   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 )






