


"""simple docstring"""







from argparse import ArgumentParser

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


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




def        UpperCAmelCase__	(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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    pipeline(
					    task=args.task  ,						model=args.model if args.model else None  ,						config=args.config  ,						tokenizer=args.tokenizer  ,						device=args.device  ,						)
					__SCREAMING_SNAKE_CASE						       =    try_infer_format_from_ext(args.input       ) if args.format == "infer" else args.format
					__SCREAMING_SNAKE_CASE						       =    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_     (			UpperCamelCase):

	"""simple docstring"""





	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    Pipeline      ,      UpperCAmelCase__      :    PipelineDataFormat       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    nlp
						__SCREAMING_SNAKE_CASE						       =    reader




	@staticmethod
	def   UpperCAmelCase_				(			UpperCAmelCase__      :    ArgumentParser       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self._nlp, []

						for entry in self._reader:
											__SCREAMING_SNAKE_CASE						       =    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:
											__SCREAMING_SNAKE_CASE						       =    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"""







def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    [0 for i in range(r + 1       )]
					# nc0 = 1
					__SCREAMING_SNAKE_CASE						       =    1
					for i in range(1  ,						n + 1       ):
										# to compute current row from previous row.
										__SCREAMING_SNAKE_CASE						       =    min(lowerCAmelCase_  ,						lowerCAmelCase_       )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]



print(binomial_coefficient(n=1_0, r=5))
