



from argparse import ArgumentParser

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


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






def  UpperCAmelCase__    (    lowercase__   ) ->      Optional[int]:
						if not path:
												return "pipe"

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







class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			def __init__(				self	:       Dict			,     lowercase	:       Pipeline			,     lowercase	:       PipelineDataFormat   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				nlp
									__lowercase					=				reader





			@staticmethod
			def 		snake_case__			(				lowercase	:       ArgumentParser   )						->					Union[str, Any]:
									"""simple docstring"""







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







			def 		snake_case__			(				self	:       str   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase	,			__lowercase					=				self._nlp, []

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

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










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

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase			)
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  str      =			field(default="""summarization"""    ,					metadata={"""include_in_asdict_even_if_is_default""": True}			)
			lowercase__      :  ClassVar[Features]      =			Features({"""text""": Value("""string"""			)}			)
			lowercase__      :  ClassVar[Features]      =			Features({"""summary""": Value("""string"""			)}			)
			lowercase__      :  str      =			"text"
			lowercase__      :  str      =			"summary"





			@property
			def 		snake_case__			(				self	:       List[Any]   )						->					Dict[str, str]:
									"""simple docstring"""







									return {self.text_column: "text", self.summary_column: "summary"}





