





from argparse import ArgumentParser

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


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





def   lowerCAmelCase_				( __a	)  ->	Any:







  """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   lowerCAmelCase_				( __a	)  ->	Tuple:







  """simple docstring"""





  lowerCamelCase__:   List[str]    						=pipeline(
      task=args.task			,					model=args.model if args.model else None			,					config=args.config			,					tokenizer=args.tokenizer			,					device=args.device			,					)
  lowerCamelCase__:   Union[str, Any]    						=try_infer_format_from_ext(args.input	) if args.format == "infer" else args.format
  lowerCamelCase__:   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      _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE__					):







 '''simple docstring'''

 def __init__(self       :     str ,				UpperCAmelCase_       :     List[Any] ,				UpperCAmelCase_       :     Optional[Any]) ->int:



   '''simple docstring'''



   lowerCamelCase__:   Any    						=nlp
   lowerCamelCase__:   Union[str, Any]    						=reader

 @staticmethod
 def 							SCREAMING_SNAKE_CASE_      (UpperCAmelCase_       :     Union[str, Any]) ->int:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, 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=snake_case__ ,				help="Path to the file to use for inference")
   run_parser.add_argument("--output" ,				type=snake_case__ ,				help="Path to the file that will be used post to write results.")
   run_parser.add_argument("--model" ,				type=snake_case__ ,				help="Name or path to the model to instantiate.")
   run_parser.add_argument("--config" ,				type=snake_case__ ,				help="Name or path to the model's config to instantiate.")
   run_parser.add_argument(
       "--tokenizer" ,				type=snake_case__ ,				help="Name of the tokenizer to use. (default: same as the model name)")
   run_parser.add_argument(
       "--column" ,				type=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=snake_case__ ,				default="infer" ,				choices=PipelineDataFormat.SUPPORTED_FORMATS ,				help="Input format to read from" ,				)
   run_parser.add_argument(
       "--device" ,				type=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=snake_case__)







 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->str:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=self._nlp, []

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

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








from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

__A             =       (3, 9, -11, 0, 7, 5, 1, -1)
__A             =       (4, 6, 2, 0, 8, 10, 3, -2)



@dataclass
class      _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''
 lowercase_	=    42
 lowercase_	=    42



class      _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __init__(self       :     Dict ,				UpperCAmelCase_       :     Iterable[int]) ->None:



   '''simple docstring'''



   lowerCamelCase__:   Node | None    						=None
   for i in sorted(UpperCAmelCase_ ,				reverse=UpperCAmelCase_):
     lowerCamelCase__:   Any    						=Node(UpperCAmelCase_ ,				self.head)

 def __iter__(self       :     Optional[int]) ->Iterator[int]:



   '''simple docstring'''



   lowerCamelCase__:   int    						=self.head
   while node:
     yield node.data
     lowerCamelCase__:   List[str]    						=node.next_node

 def __len__(self       :     int) ->int:



   '''simple docstring'''



   return sum(1 for _ in self)







 def __str__(self       :     Union[str, Any]) ->str:



   '''simple docstring'''



   return " -> ".join([str(UpperCAmelCase_) for node in self])





def   lowerCAmelCase_				( __a			,					__a	)  ->	SortedLinkedList:







  """simple docstring"""





  return SortedLinkedList(list(__a	) + list(__a	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     __A             =       SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

