





'''simple docstring'''






from argparse import ArgumentParser

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


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





def       lowercase_      (		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       lowercase_      (		lowercase__						)   ->Tuple:
						_snake_case:   Union[str, Any]  					=		pipeline(
						    task=args.task				,  model=args.model if args.model else None				,  config=args.config				,  tokenizer=args.tokenizer				,  device=args.device				,  )
						_snake_case:   Tuple  					=		try_infer_format_from_ext(args.input						) if args.format == '''infer''' else args.format
						_snake_case:   List[Any]  					=		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_  ):


					def __init__(					self	:  Optional[Any]       ,     __snake_case	:  Pipeline       ,     __snake_case	:  PipelineDataFormat       ):





											'''simple docstring'''





											_snake_case:   str  					=		nlp
											_snake_case:   List[Any]  					=		reader


					@staticmethod
					def    SCREAMING_SNAKE_CASE_		(					__snake_case	:  ArgumentParser       ):





											'''simple docstring'''





											_snake_case:   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=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	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		self._nlp, []

											for entry in self._reader:
																	_snake_case:   str  					=		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:
																	_snake_case:   Any  					=		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__       )












'''simple docstring'''






import torch





def       lowercase_      (		)   ->Dict:
						if torch.cuda.is_available():
												_snake_case:   Any  					=		torch.cuda.device_count()
						else:
												_snake_case:   Optional[int]  					=		0
						print(F'''Successfully ran on {num_gpus} GPUs'''						)


if __name__ == "__main__":
		main()





