



'''simple docstring'''



from argparse import ArgumentParser

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


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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							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					):



							"""simple docstring"""



							__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=_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 _UpperCamelCase		(     A		):





				'''simple docstring'''





				def __init__(							self  :  int  ,       _lowerCAmelCase  :  Pipeline  ,       _lowerCAmelCase  :  PipelineDataFormat):







											'''simple docstring'''

											__lowercase       	=nlp
											__lowercase       	=reader

				@staticmethod
				def 				__lowerCamelCase     (							_lowerCAmelCase  :  ArgumentParser):







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

				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											__lowercase	,				__lowercase       	=self._nlp, []

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

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








'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


lowerCamelCase	 =       logging.get_logger(__name__)





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				def __init__(							self  :  Optional[int]  ,       *_lowerCAmelCase  :  Union[str, Any]  ,       **_lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

											warnings.warn(
											    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
											    ' use DeiTImageProcessor instead.'  ,       _lowerCAmelCase  ,       )
											super().__init__(*_lowerCAmelCase  ,       **_lowerCAmelCase)



