


from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
      import numpy as np
      from decord import VideoReader


if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase_     )
class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					List[Any]		,*lowerCamelCase__		:					str		,**lowerCamelCase__		:					Optional[int] )  ->	Tuple:




           '''simple docstring'''


           super().__init__(*lowerCamelCase__		,**lowerCamelCase__ )
           requires_backends(self		,"""decord""" )
           self.check_model_type(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					int=None		,lowerCamelCase__		:					List[Any]=None		,lowerCamelCase__		:					int=None )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		{}
           if frame_sampling_rate is not None:
                 SCREAMING_SNAKE_CASE	=		frame_sampling_rate
           if num_frames is not None:
                 SCREAMING_SNAKE_CASE	=		num_frames

           SCREAMING_SNAKE_CASE	=		{}
           if top_k is not None:
                 SCREAMING_SNAKE_CASE	=		top_k
           return preprocess_params, {}, postprocess_params






     def __call__(							self		:					List[Any]		,lowerCamelCase__		:					Union[str, List[str]]		,**lowerCamelCase__		:					List[str] )  ->	Optional[int]:




           '''simple docstring'''


           return super().__call__(lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Optional[int]=None		,lowerCamelCase__		:					int=1 )  ->	Optional[int]:




           '''simple docstring'''


           if num_frames is None:
                 SCREAMING_SNAKE_CASE	=		self.model.config.num_frames

           if video.startswith("""http://""" ) or video.startswith("""https://""" ):
                 SCREAMING_SNAKE_CASE	=		BytesIO(requests.get(lowerCamelCase__ ).content )

           SCREAMING_SNAKE_CASE	=		VideoReader(lowerCamelCase__ )
           videoreader.seek(0 )

           SCREAMING_SNAKE_CASE	=		0
           SCREAMING_SNAKE_CASE	=		num_frames * frame_sampling_rate - 1
           SCREAMING_SNAKE_CASE	=		np.linspace(lowerCamelCase__		,lowerCamelCase__		,num=lowerCamelCase__		,dtype=np.intaa )

           SCREAMING_SNAKE_CASE	=		videoreader.get_batch(lowerCamelCase__ ).asnumpy()
           SCREAMING_SNAKE_CASE	=		list(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		self.image_processor(lowerCamelCase__		,return_tensors=self.framework )
           return model_inputs






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					Dict )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model(**lowerCamelCase__ )
           return model_outputs






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					Any=5 )  ->	Any:




           '''simple docstring'''


           if top_k > self.model.config.num_labels:
                 SCREAMING_SNAKE_CASE	=		self.model.config.num_labels

           if self.framework == "pt":
                 SCREAMING_SNAKE_CASE	=		model_outputs.logits.softmax(-1 )[0]
                 SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		probs.topk(lowerCamelCase__ )
           else:
                 raise ValueError(F"""Unsupported framework: {self.framework}""" )

           SCREAMING_SNAKE_CASE	=		scores.tolist()
           SCREAMING_SNAKE_CASE	=		ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase__		,lowerCamelCase__ )]









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      def get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) -> str:
            SCREAMING_SNAKE_CASE	=		[]
            SCREAMING_SNAKE_CASE	=		min(len(_stra			)				,		len(_stra			)			) // 2
            for i, l in enumerate(_stra			):
                  SCREAMING_SNAKE_CASE	=		int(max(0				,		i - limit			)			)
                  SCREAMING_SNAKE_CASE	=		int(min(i + limit + 1				,		len(_stra			)			)			)
                  if l in _stra[left:right]:
                        matched.append(_SCREAMING_SNAKE_CASE			)
                        SCREAMING_SNAKE_CASE	=		F"""{_stra[0:_stra.index(_SCREAMING_SNAKE_CASE			)]} {_stra[_stra.index(_SCREAMING_SNAKE_CASE			) + 1:]}"""

            return "".join(_SCREAMING_SNAKE_CASE			)

      # matching characters
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		len(_SCREAMING_SNAKE_CASE			)

      # transposition
      SCREAMING_SNAKE_CASE	=		(
          len([(ca, ca) for ca, ca in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) if ca != ca]			) // 2
      )

      if not match_count:
            SCREAMING_SNAKE_CASE	=		0.0
      else:
            SCREAMING_SNAKE_CASE	=		(
                1
                / 3
                * (
                    match_count / len(_SCREAMING_SNAKE_CASE			)
                    + match_count / len(_SCREAMING_SNAKE_CASE			)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      SCREAMING_SNAKE_CASE	=		0
      for ca, ca in zip(stra[:4]				,		stra[:4]			):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler("""hello""", """world"""))


