
"""simple docstring"""




import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


_A     :			Tuple  						=			pytest.mark.integration


@pytest.mark.parametrize("path"	,					["paws", "csv"]    )
def     __magic_name__      ( __snake_case							:			Any	,					__snake_case							:			Any    )					->							List[str]:
       inspect_dataset(__snake_case	,					__snake_case    )
       lowercase       :							Tuple									=				path + ".py"
       assert script_name in os.listdir(__snake_case    )
       assert "__pycache__" not in os.listdir(__snake_case    )


@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"    )
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"    )
@pytest.mark.parametrize("path"	,					["accuracy"]    )
def     __magic_name__      ( __snake_case							:			List[Any]	,					__snake_case							:			Optional[Any]    )					->							int:
       inspect_metric(__snake_case	,					__snake_case    )
       lowercase       :							str									=				path + ".py"
       assert script_name in os.listdir(__snake_case    )
       assert "__pycache__" not in os.listdir(__snake_case    )


@pytest.mark.parametrize(
    "path, config_name, expected_splits"	,					[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]	,					)
def     __magic_name__      ( __snake_case							:			List[str]	,					__snake_case							:			int	,					__snake_case							:			Union[str, Any]    )					->							int:
       lowercase       :							List[str]									=				get_dataset_config_info(__snake_case	,					config_name=__snake_case    )
       assert info.config_name == config_name
       assert list(info.splits.keys()    ) == expected_splits


@pytest.mark.parametrize(
    "path, config_name, expected_exception"	,					[
        ("paws", None, ValueError),
    ]	,					)
def     __magic_name__      ( __snake_case							:			Union[str, Any]	,					__snake_case							:			Tuple	,					__snake_case							:			Tuple    )					->							Dict:
       with pytest.raises(__snake_case    ):
              get_dataset_config_info(__snake_case	,					config_name=__snake_case    )


@pytest.mark.parametrize(
    "path, expected"	,					[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]	,					)
def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			Optional[Any]    )					->							Dict:
       lowercase       :							List[str]									=				get_dataset_config_names(__snake_case    )
       assert expected in config_names


@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"	,					[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]	,					)
def     __magic_name__      ( __snake_case							:			List[Any]	,					__snake_case							:			Dict	,					__snake_case							:			Tuple    )					->							Optional[Any]:
       lowercase       :							int									=				get_dataset_infos(__snake_case    )
       assert list(infos.keys()    ) == expected_configs
       lowercase       :							Any									=				expected_configs[0]
       assert expected_config in infos
       lowercase       :							Any									=				infos[expected_config]
       assert info.config_name == expected_config
       assert list(info.splits.keys()    ) == expected_splits_in_first_config


@pytest.mark.parametrize(
    "path, expected_config, expected_splits"	,					[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]	,					)
def     __magic_name__      ( __snake_case							:			Dict	,					__snake_case							:			int	,					__snake_case							:			str    )					->							Dict:
       lowercase       :							List[Any]									=				get_dataset_infos(__snake_case    )
       assert expected_config in infos
       lowercase       :							List[Any]									=				infos[expected_config]
       assert info.config_name == expected_config
       assert list(info.splits.keys()    ) == expected_splits


@pytest.mark.parametrize(
    "path, config_name, expected_exception"	,					[
        ("paws", None, ValueError),
    ]	,					)
def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			Optional[int]	,					__snake_case							:			Optional[int]    )					->							Any:
       with pytest.raises(__snake_case    ):
              get_dataset_split_names(__snake_case	,					config_name=__snake_case    )




"""simple docstring"""




from argparse import ArgumentParser

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


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


def     __magic_name__      ( __snake_case							:			str    )					->							List[str]:
       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     __magic_name__      ( __snake_case							:			str    )					->							Optional[int]:
       lowercase       :							int									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       lowercase       :							Optional[Any]									=				try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       lowercase       :							Optional[int]									=				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 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				nlp
          lowercase       :							Optional[Any]									=				reader
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							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=_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      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self._nlp, []

          for entry in self._reader:
                 lowercase       :							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:
                 lowercase       :							Dict									=				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							)


