






'''simple docstring'''







from argparse import ArgumentParser

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


_A			:    List[str]					  =logging.get_logger(__name__)  # pylint: disable=invalid-name





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							if not path:
														return "pipe"

							for ext in PipelineDataFormat.SUPPORTED_FORMATS:
														if path.endswith(UpperCamelCase   ):
																					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_ (UpperCamelCase   )   -> Union[str, Any]:
							lowerCamelCase__      :			Dict 						= pipeline(
							    task=args.task       ,    model=args.model if args.model else None       ,    config=args.config       ,    tokenizer=args.tokenizer       ,    device=args.device       ,    )
							lowerCamelCase__      :			Any 						= try_infer_format_from_ext(args.input   ) if args.format == """infer""" else args.format
							lowerCamelCase__      :			List[str] 						= PipelineDataFormat.from_str(
							    format=UpperCamelCase       ,    output_path=args.output       ,    input_path=args.input       ,    column=args.column if args.column else nlp.default_input_names       ,    overwrite=args.overwrite       ,    )
							return RunCommand(UpperCamelCase       ,    UpperCamelCase   )



class _lowercase			(       _lowercase	):




							def __init__(							self:       int    ,							UpperCamelCase__:       Pipeline    ,							UpperCamelCase__:       PipelineDataFormat  ):
														lowerCamelCase__      :			int 						= nlp
														lowerCamelCase__      :			str 						= reader




							@staticmethod
							def 	lowerCamelCase_       (							UpperCamelCase__:       ArgumentParser  ):
														lowerCamelCase__      :			Optional[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 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__						,	lowerCamelCase__      :			List[str] 						= self._nlp, []

														for entry in self._reader:
																					lowerCamelCase__      :			Tuple 						= 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:
																					lowerCamelCase__      :			str 						= 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'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

