





from argparse import ArgumentParser

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


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



def 	A_       (  _lowerCAmelCase  )		->				Any:
 if not path:
  return "pipe"

 for ext in PipelineDataFormat.SUPPORTED_FORMATS:
  if path.endswith(_lowerCAmelCase  ):
   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  )		->				Optional[Any]:
 UpperCamelCase			:      Optional[int] 			=     pipeline(
     task=args.task							,     model=args.model if args.model else None							,     config=args.config							,     tokenizer=args.tokenizer							,     device=args.device							,     )
 UpperCamelCase			:      Optional[int] 			=     try_infer_format_from_ext(args.input  ) if args.format == "infer" else args.format
 UpperCamelCase			:      int 			=     PipelineDataFormat.from_str(
     format=_lowerCAmelCase							,     output_path=args.output							,     input_path=args.input							,     column=args.column if args.column else nlp.default_input_names							,     overwrite=args.overwrite							,     )
 return RunCommand(_lowerCAmelCase							,     _lowerCAmelCase  )



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     nlp
       UpperCamelCase			:      Tuple 			=     reader





      @staticmethod
      def  __UpperCamelCase(				A_     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     self._nlp, []

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

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







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	A_       (  )		->				Dict:
 UpperCamelCase			:      Tuple 			=     ArgumentParser(
     description=(
         "PyTorch TPU distributed training launch "
         "helper utility that will spawn up "
         "multiple distributed processes"
     )  )

 # Optional arguments for the launch helper
 parser.add_argument("--num_cores"							,     type=_lowerCAmelCase							,     default=1							,     help="Number of TPU cores to use (1 or 8)."  )

 # positional
 parser.add_argument(
     "training_script"							,     type=_lowerCAmelCase							,     help=(
         "The full path to the single TPU training "
         "program/script to be launched in parallel, "
         "followed by all the arguments for the "
         "training script"
     )							,     )

 # rest from the training program
 parser.add_argument("training_script_args"							,     nargs=_lowerCAmelCase  )

 return parser.parse_args()



def 	A_       (  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     parse_args()

 # Import training_script as a module.
 UpperCamelCase			:      Union[str, Any] 			=     Path(args.training_script  )
 sys.path.append(str(script_fpath.parent.resolve()  )  )
 UpperCamelCase			:      List[Any] 			=     script_fpath.stem
 UpperCamelCase			:      Optional[Any] 			=     importlib.import_module(_lowerCAmelCase  )

 # Patch sys.argv
 UpperCamelCase			:      List[Any] 			=     [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores  )]
 xmp.spawn(mod._mp_fn							,     args=()							,     nprocs=args.num_cores  )


if __name__ == "__main__":
    main()
