






from argparse import ArgumentParser

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


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







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				str  ):
	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 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Optional[Any]  ):
	snake_case__					: List[Any]  =    pipeline(
	    task=args.task	,    model=args.model if args.model else None	,    config=args.config	,    tokenizer=args.tokenizer	,    device=args.device	,    )
	snake_case__					: Union[str, Any]  =    try_infer_format_from_ext(args.input  ) if args.format == "infer" else args.format
	snake_case__					: Any  =    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 				SCREAMING_SNAKE_CASE__		(							_UpperCamelCase		):





					"""simple docstring"""






					def __init__(   self	:    int				,						__A	:    Pipeline				,						__A	:    PipelineDataFormat			):
						snake_case__					: List[Any]  =    nlp
						snake_case__					: str  =    reader



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






					def 			_lowercase	(   self	:    int			):
						snake_case__					: Tuple  =    self._nlp, []

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

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










"""simple docstring"""






__UpperCAmelCase						=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Tuple			)    ->			Dict:



			'''simple docstring'''
			# Return True if there is node that has not iterated.
			a__  :	int		 =			[False] * len(lowerCAmelCase__			)
			a__  :	Optional[Any]		 =			[s]
			a__  :	Optional[int]		 =			True

			while queue:
						a__  :	Any		 =			queue.pop(0			)
						for ind in range(len(graph[u]			)			):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(lowerCAmelCase__			)
												a__  :	Any		 =			True
												a__  :	Union[str, Any]		 =			u

			return visited[t]






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Optional[Any]			)    ->			Any:



			'''simple docstring'''
			a__  :	Any		 =			[-1] * (len(lowerCAmelCase__			))
			a__  :	Any		 =			0
			a__  :	List[str]		 =			[]
			a__  :	Tuple		 =			[i[:] for i in graph]  # Record original cut, copy.
			while bfs(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
						a__  :	Any		 =			float("Inf"			)
						a__  :	Optional[int]		 =			sink

						while s != source:
									# Find the minimum value in select path
									a__  :	Union[str, Any]		 =			min(lowerCAmelCase__							,			graph[parent[s]][s]			)
									a__  :	List[str]		 =			parent[s]

						max_flow += path_flow
						a__  :	List[str]		 =			sink

						while v != source:
									a__  :	int		 =			parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									a__  :	List[str]		 =			parent[v]

			for i in range(len(lowerCAmelCase__			)			):
						for j in range(len(graph[0]			)			):
									if graph[i][j] == 0 and temp[i][j] > 0:
												res.append((i, j)			)

			return res


if __name__ == "__main__":
		print(mincut(test_graph, source=0, sink=5))