


'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   :   str												=   {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _a   :   str												=   [
           "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LiltForQuestionAnswering",
           "LiltForSequenceClassification",
           "LiltForTokenClassification",
           "LiltModel",
           "LiltPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       _a   :   Optional[int]												=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








from argparse import ArgumentParser

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


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


def     lowercase	(    a ):



		'''simple docstring'''




		if not path:
				return "pipe"

		for ext in PipelineDataFormat.SUPPORTED_FORMATS:
				if path.endswith(a ):
						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     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						pipeline(
		    task=args.task     , model=args.model if args.model else None     , config=args.config     , tokenizer=args.tokenizer     , device=args.device     , )
		SCREAMING_SNAKE_CASE_				:Any				     =						try_infer_format_from_ext(args.input ) if args.format == "infer" else args.format
		SCREAMING_SNAKE_CASE_				:str				     =						PipelineDataFormat.from_str(
		    format=a     , output_path=args.output     , input_path=args.input     , column=args.column if args.column else nlp.default_input_names     , overwrite=args.overwrite     , )
		return RunCommand(a     , a )



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     List[Any]   ,  UpperCAmelCase				:     Pipeline   ,  UpperCAmelCase				:     PipelineDataFormat):
				SCREAMING_SNAKE_CASE_				:int				     =						nlp
				SCREAMING_SNAKE_CASE_				:Any				     =						reader






		@staticmethod
		def _snake_case		(						UpperCAmelCase				:     ArgumentParser):
				SCREAMING_SNAKE_CASE_				: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 _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self._nlp, []

				for entry in self._reader:
						SCREAMING_SNAKE_CASE_				:int				     =						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_				:Dict				     =						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)






