





from argparse import ArgumentParser

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


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






def lowerCamelCase__   (							snake_case_     :  str	)				->    Tuple:
   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 lowerCamelCase__   (							snake_case_     :  Tuple	)				->    List[str]:
   __snake_case								=       pipeline(
       task=args.task	,				model=args.model if args.model else None	,				config=args.config	,				tokenizer=args.tokenizer	,				device=args.device	,				)
   __snake_case								=       try_infer_format_from_ext(args.input	) if args.format == "infer" else args.format
   __snake_case								=       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 		SCREAMING_SNAKE_CASE__    (	SCREAMING_SNAKE_CASE__  ):


      def __init__(self	:				int    ,      a__	:				str    ,      a__	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       nlp
         __snake_case								=       reader


      @staticmethod
      def        a			(a__	:				List[str]				):





         """simple docstring"""



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


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       self._nlp, []

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

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








from __future__ import annotations

import collections
import pprint
from pathlib import Path






def lowerCamelCase__   (							snake_case_     :  str	)				->    str:
   return "".join(sorted(snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  str	)				->    list[str]:
   return word_by_signature[signature(snake_case_	)]


snake_case_           =      Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
snake_case_           =      sorted({word.strip().lower() for word in data.splitlines()})

snake_case_           =      collections.defaultdict(list)
for word in word_list:
    word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
    snake_case_           =      {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

    with open('anagrams.txt', 'w') as file:
        file.write('all_anagrams = \n ')
        file.write(pprint.pformat(all_anagrams))

