



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 			__a	(       lowerCAmelCase_  :   Optional[Any]							) ->					List[str]:






						'''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 			__a	(       lowerCAmelCase_  :   Tuple							) ->					List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=  pipeline(
						    task=args.task     ,model=args.model if args.model else None     ,config=args.config     ,tokenizer=args.tokenizer     ,device=args.device     ,)
						UpperCAmelCase_=  try_infer_format_from_ext(args.input							) if args.format == """infer""" else args.format
						UpperCAmelCase_=  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 						lowercase		(					snake_case_):






	"""simple docstring"""


	def __init__(      self      :		int		,    __UpperCAmelCase      :		Optional[int]		,    __UpperCAmelCase      :		str	)     ->					Dict:
							UpperCAmelCase_=  nlp
							UpperCAmelCase_=  reader




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




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[str]	)     ->					Tuple:
							UpperCAmelCase_,  UpperCAmelCase_=  self._nlp, []

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

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












from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=snake_case_							)
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					field(default='''language-modeling'''			,   metadata={'''include_in_asdict_even_if_is_default''': True}							)

	lowerCAmelCase						=					Features({'''text''': Value('''string'''							)}							)
	lowerCAmelCase						=					Features({}							)
	lowerCAmelCase						=					"text"




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict[str, str]:
								return {self.text_column: "text"}





