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

__magic_name__								=logging.get_logger(__name__)



@add_end_docstrings(__UpperCamelCase							)
class     _A   (    __UpperCamelCase							):


  def __init__(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       super().__init__(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
       requires_backends(self   ,				'''decord'''	)
       self.check_model_type(SCREAMING_SNAKE_CASE_	)


  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None	)		->      Tuple:





       '''simple docstring'''





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

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


  def __call__(self   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





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


  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=1	)		->      Optional[int]:





       '''simple docstring'''





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

       if video.startswith('''http://'''	) or video.startswith('''https://'''	):
            UpperCamelCase__										=      BytesIO(requests.get(SCREAMING_SNAKE_CASE_	).content	)

       UpperCamelCase__										=      VideoReader(SCREAMING_SNAKE_CASE_	)
       videoreader.seek(0	)

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

       UpperCamelCase__										=      videoreader.get_batch(SCREAMING_SNAKE_CASE_	).asnumpy()
       UpperCamelCase__										=      list(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.image_processor(SCREAMING_SNAKE_CASE_   ,				return_tensors=self.framework	)
       return model_inputs


  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model(**SCREAMING_SNAKE_CASE_	)
       return model_outputs





  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=5	)		->      Tuple:





       '''simple docstring'''





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

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

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




from __future__ import annotations







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      get_failure_array(A							)

     # 2) Step through text searching for pattern
     UpperCamelCase__						,       UpperCamelCase__										=      0, 0  # index into text, pattern
     while i < len(A							):
          if pattern[j] == text[i]:
               if j == (len(A							) - 1):
                    return True
               j += 1

          # if this is a prefix in our pattern
          # just go back far enough to continue
          elif j > 0:
               UpperCamelCase__										=      failure[j - 1]
               continue
          i += 1
     return False







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      [0]
     UpperCamelCase__										=      0
     UpperCamelCase__										=      1
     while j < len(A							):
          if pattern[i] == pattern[j]:
               i += 1
          elif i > 0:
               UpperCamelCase__										=      failure[i - 1]
               continue
          j += 1
          failure.append(A							)
     return failure


if __name__ == "__main__":
     # Test 1)
     __magic_name__								='''abc1abc12'''
     __magic_name__								='''alskfjaldsabc1abc1abc12k23adsfabcabc'''
     __magic_name__								='''alskfjaldsk23adsfabcabc'''
     assert kmp(pattern, texta) and not kmp(pattern, texta)

     # Test 2)
     __magic_name__								='''ABABX'''
     __magic_name__								='''ABABZABABYABABX'''
     assert kmp(pattern, text)

     # Test 3)
     __magic_name__								='''AAAB'''
     __magic_name__								='''ABAAAAAB'''
     assert kmp(pattern, text)

     # Test 4)
     __magic_name__								='''abcdabcy'''
     __magic_name__								='''abcxabcdabxabcdabcdabcy'''
     assert kmp(pattern, text)

     # Test 5)
     __magic_name__								='''aabaabaaa'''
     assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]



