"""simple docstring"""


from argparse import ArgumentParser

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


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







def 					__a   (		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 					__a   (		A      ):






     '''simple docstring'''



     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=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    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							nlp
        lowercase__				 =							reader


   @staticmethod
   def 	snake_case__	(     _UpperCAmelCase  ):
        '''simple docstring'''



        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 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						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  )





"""simple docstring"""


import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY






@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class    a__    (   unittest.TestCase	):
   snake_case_							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							hf_hub_download(
            repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							VideoClassificationPipeline(model=_UpperCAmelCase,				image_processor=_UpperCAmelCase,				top_k=2  )
        lowercase__				 =							[
            example_video_filepath,
            "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
        ]
        return video_classifier, examples


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        for example in examples:
             lowercase__				 =							video_classifier(_UpperCAmelCase  )

             self.assertEqual(
                 _UpperCAmelCase,				[
                     {"score": ANY(_UpperCAmelCase  ), "label": ANY(_UpperCAmelCase  )},
                     {"score": ANY(_UpperCAmelCase  ), "label": ANY(_UpperCAmelCase  )},
                 ],				)


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
        lowercase__				 =							VideoMAEFeatureExtractor(
            size={"shortest_edge": 10},				crop_size={"height": 10, "width": 10}  )
        lowercase__				 =							pipeline(
            "video-classification",				model=_UpperCAmelCase,				feature_extractor=_UpperCAmelCase,				frame_sampling_rate=4  )

        lowercase__				 =							hf_hub_download(repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							video_classifier(_UpperCAmelCase,				top_k=2  )
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],				)

        lowercase__				 =							video_classifier(
            [
                video_file_path,
                video_file_path,
            ],				top_k=2,				)
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
            ],				)


   @require_tf
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass




