





"""simple docstring"""
from argparse import ArgumentParser

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


__UpperCamelCase     :						Optional[int]						       =  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(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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, Any]			):
 lowerCAmelCase    				= pipeline(
     task=args.task  ,     model=args.model if args.model else None  ,     config=args.config  ,     tokenizer=args.tokenizer  ,     device=args.device  ,     )
 lowerCAmelCase    				= try_infer_format_from_ext(args.input			) if args.format == 'infer' else args.format
 lowerCAmelCase    				= 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				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= nlp
  lowerCAmelCase    				= reader



 @staticmethod
 def    UpperCamelCase__  (     _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= 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=__lowerCAmelCase				,       help='Path to the file to use for inference'     )
  run_parser.add_argument('--output'				,       type=__lowerCAmelCase				,       help='Path to the file that will be used post to write results.'     )
  run_parser.add_argument('--model'				,       type=__lowerCAmelCase				,       help='Name or path to the model to instantiate.'     )
  run_parser.add_argument('--config'				,       type=__lowerCAmelCase				,       help='Name or path to the model\'s config to instantiate.'     )
  run_parser.add_argument(
      '--tokenizer'				,       type=__lowerCAmelCase				,       help='Name of the tokenizer to use. (default: same as the model name)'     )
  run_parser.add_argument(
      '--column'				,       type=__lowerCAmelCase				,       help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'				,       )
  run_parser.add_argument(
      '--format'				,       type=__lowerCAmelCase				,       default='infer'				,       choices=PipelineDataFormat.SUPPORTED_FORMATS				,       help='Input format to read from'				,       )
  run_parser.add_argument(
      '--device'				,       type=__lowerCAmelCase				,       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=__lowerCAmelCase     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase    				= self._nlp, []

  for entry in self._reader:
   lowerCAmelCase    				= nlp(**__lowerCAmelCase     ) if self._reader.is_multi_columns else nlp(__lowerCAmelCase     )
   if isinstance(__lowerCAmelCase				,       __lowerCAmelCase     ):
    outputs.append(__lowerCAmelCase     )
   else:
    outputs += output

        # Saving data
  if self._nlp.binary_output:
   lowerCAmelCase    				= self._reader.save_binary(__lowerCAmelCase     )
   logger.warning(F'Current pipeline requires output to be in binary format, saving at {binary_path}'     )
  else:
   self._reader.save(__lowerCAmelCase     )












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 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							snake_case  :   List[str]      =							MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														for example in examples:
																					UpperCamelCase__					  =      video_classifier(__lowerCAmelCase	)

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






							@require_torch
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
														UpperCamelCase__					  =      VideoMAEFeatureExtractor(
														    size={"""shortest_edge""": 10}	,			crop_size={"""height""": 10, """width""": 10}	)
														UpperCamelCase__					  =      pipeline(
														    """video-classification"""	,			model=__lowerCAmelCase	,			feature_extractor=__lowerCAmelCase	,			frame_sampling_rate=4	)

														UpperCamelCase__					  =      hf_hub_download(repo_id="""nateraw/video-demo"""	,			filename="""archery.mp4"""	,			repo_type="""dataset"""	)
														UpperCamelCase__					  =      video_classifier(__lowerCAmelCase	,			top_k=2	)
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase	,			decimals=4	)	,			[{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}]	,			)

														UpperCamelCase__					  =      video_classifier(
														    [
														        video_file_path,
														        video_file_path,
														    ]	,			top_k=2	,			)
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase	,			decimals=4	)	,			[
														        [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
														        [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
														    ]	,			)







							@require_tf
							def 					_lowerCamelCase		(    self	):
														pass




