



from argparse import ArgumentParser

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


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







def     UpperCAmelCase_		(					snake_case__					)	->     Tuple:



				"""simple docstring"""





				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(snake_case__					):
												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_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  pipeline(
				    task=args.task    ,			model=args.model if args.model else None    ,			config=args.config    ,			tokenizer=args.tokenizer    ,			device=args.device    ,			)
				lowerCAmelCase__		  =  try_infer_format_from_ext(args.input					) if args.format == 'infer' else args.format
				lowerCAmelCase__		  =  PipelineDataFormat.from_str(
				    format=snake_case__    ,			output_path=args.output    ,			input_path=args.input    ,			column=args.column if args.column else nlp.default_input_names    ,			overwrite=args.overwrite    ,			)
				return RunCommand(snake_case__    ,			snake_case__					)


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  nlp
						lowerCAmelCase__		  =  reader
		@staticmethod
		def 						SCREAMING_SNAKE_CASE_					(							a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self._nlp, []

						for entry in self._reader:
										lowerCAmelCase__		  =  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:
										lowerCAmelCase__		  =  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_						)





import random
from typing import Any







def     UpperCAmelCase_		(					snake_case__					)	->     list[Any]:



				"""simple docstring"""





				for _ in range(len(snake_case__					)					):
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  data[b], data[a]
				return data


if __name__ == "__main__":
	_lowerCAmelCase	:   int    =   [0, 1, 2, 3, 4, 5, 6, 7]
	_lowerCAmelCase	:   Optional[Any]    =   ["python", "says", "hello", "!"]
	print("Fisher-Yates Shuffle:")
	print("List", integers, strings)
	print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
