



from argparse import ArgumentParser

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


__snake_case						:Optional[int]      		=     logging.get_logger(__name__)  # pylint: disable=invalid-name





def      __snake_case	(			_UpperCAmelCase				):
    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      __snake_case	(			_UpperCAmelCase				):
    __a						       =				pipeline(
        task=args.task   , model=args.model if args.model else None   , config=args.config   , tokenizer=args.tokenizer   , device=args.device   , )
    __a						       =				try_infer_format_from_ext(args.input				) if args.format == '''infer''' else args.format
    __a						       =				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     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Pipeline     ,		__SCREAMING_SNAKE_CASE							:     PipelineDataFormat):


           '''simple docstring'''

           __a						       =				nlp
           __a						       =				reader



       @staticmethod
       def 		_lowerCamelCase			(		__SCREAMING_SNAKE_CASE							:     ArgumentParser):


           '''simple docstring'''

           __a						       =				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 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self._nlp, []

           for entry in self._reader:
               __a						       =				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:
               __a						       =				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 collections import defaultdict





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				first_str.lower().strip()
    __a						       =				second_str.lower().strip()

    # Remove whitespace
    __a						       =				first_str.replace(''' '''   , ''''''				)
    __a						       =				second_str.replace(''' '''   , ''''''				)

    # Strings of different lengths are not anagrams
    if len(_UpperCAmelCase				) != len(_UpperCAmelCase				):
        return False

    # Default values for count should be 0
    __a						       =				defaultdict(_UpperCAmelCase				)

    # For each character in input strings,
    # increment count in the corresponding
    for i in range(len(_UpperCAmelCase				)				):
        count[first_str[i]] += 1
        count[second_str[i]] -= 1

    return all(_count == 0 for _count in count.values()				)


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 __snake_case						:Any      		=     input('''Enter the first string ''').strip()
 __snake_case						:int      		=     input('''Enter the second string ''').strip()

 __snake_case						:int      		=     check_anagrams(input_a, input_b)
 print(f'{input_a} and {input_b} are {"" if status else "not "}anagrams.')





