






"""simple docstring"""



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						(							UpperCAmelCase__    :   str )						->List[str]:
						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        _lowerCAmelCase						(							UpperCAmelCase__    :   Dict )						->Optional[Any]:
						A__  :					int											=					pipeline(
						    task=args.task,  model=args.model if args.model else None,  config=args.config,  tokenizer=args.tokenizer,  device=args.device,  )
						A__  :					int											=					try_infer_format_from_ext(args.input ) if args.format == """infer""" else args.format
						A__  :					Dict											=					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 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):


							def __init__(     self :  Any     ,      snake_case :  Pipeline     ,      snake_case :  PipelineDataFormat							):
													'''simple docstring'''





													A__  :					Union[str, Any]											=					nlp
													A__  :					Optional[int]											=					reader


							@staticmethod
							def   _UpperCamelCase    (     snake_case :  ArgumentParser							):
													'''simple docstring'''





													A__  :					Optional[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   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													A__					,					A__  :					List[str]											=					self._nlp, []

													for entry in self._reader:
																			A__  :					Tuple											=					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:
																			A__  :					List[Any]											=					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							)














"""simple docstring"""



import os
from distutils.util import strtobool




def        _lowerCAmelCase						(							UpperCAmelCase__    :   List[Any],  UpperCAmelCase__    :   Optional[Any] )						->List[str]:
						for e in env_keys:
												A__  :					List[Any]											=					int(os.environ.get(UpperCAmelCase__,  -1 ) )
												if val >= 0:
																		return val
						return default




def        _lowerCAmelCase						(							UpperCAmelCase__    :   Tuple,  UpperCAmelCase__    :   str=False )						->List[str]:
						A__  :					List[Any]											=					os.environ.get(UpperCAmelCase__,  str(UpperCAmelCase__ ) )
						return strtobool(UpperCAmelCase__ ) == 1  # As its name indicates `strtobool` actually returns an int...




def        _lowerCAmelCase						(							UpperCAmelCase__    :   Tuple,  UpperCAmelCase__    :   List[Any]="no" )						->int:
						A__  :					str											=					os.environ.get(UpperCAmelCase__,  str(UpperCAmelCase__ ) )
						return value






