



from argparse import ArgumentParser

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


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



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			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=_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 				_snake_case			(					UpperCamelCase_):




				def __init__(						self					:						Optional[Any],					__lowercase					:						Pipeline,					__lowercase					:						PipelineDataFormat						):
							lowercase__					  =				nlp
							lowercase__					  =				reader




				@staticmethod
				def 							A__      (						__lowercase					:						ArgumentParser						):
							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=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 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self._nlp, []

							for entry in self._reader:
										lowercase__					  =				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:
										lowercase__					  =				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__						)









from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def __lowerCAmelCase (     ):
			lowercase__					  =				HfArgumentParser(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			lowercase__					  =				TensorFlowBenchmark(args=SCREAMING_SNAKE_CASE_  )
			try:
						lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			except ValueError as e:
						lowercase__					  =				"Arg --no_{0} is no longer used, please use --no-{0} instead."
						lowercase__					  =				" ".join(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[:-1]  )
						lowercase__					  =				""
						lowercase__					  =				eval(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[-1]  )
						lowercase__					  =				[]
						for arg in depreciated_args:
									# arg[2:] removes '--'
									if arg[2:] in TensorFlowBenchmark.deprecated_args:
												# arg[5:] removes '--no_'
												full_error_msg += arg_error_msg.format(arg[5:]  )
									else:
												wrong_args.append(SCREAMING_SNAKE_CASE_  )
						if len(SCREAMING_SNAKE_CASE_  ) > 0:
									lowercase__					  =				full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE_  )
						raise ValueError(SCREAMING_SNAKE_CASE_  )
			benchmark.run()


if __name__ == "__main__":
							main()




