


from random import randint
from tempfile import TemporaryFile

import numpy as np
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Dict:







 """simple docstring"""


 _lowercase							      =0
 if start < end:
  _lowercase							      =randint(__snake_case     ,						__snake_case						)
  _lowercase							      =a[end]
  _lowercase							      =a[pivot]
  _lowercase							      =temp

  _lowercase      ,    _lowercase							      =_in_place_partition(__snake_case     ,						__snake_case     ,						__snake_case						)
  count += _in_place_quick_sort(__snake_case     ,						__snake_case     ,						p - 1						)
  count += _in_place_quick_sort(__snake_case     ,						p + 1     ,						__snake_case						)
 return count
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      =0
 _lowercase							      =randint(__snake_case     ,						__snake_case						)
 _lowercase							      =a[end]
 _lowercase							      =a[pivot]
 _lowercase							      =temp
 _lowercase							      =start - 1
 for index in range(__snake_case     ,						__snake_case						):
  count += 1
  if a[index] < a[end]:  # check if current val is less than pivot value
   _lowercase							      =new_pivot_index + 1
   _lowercase							      =a[new_pivot_index]
   _lowercase							      =a[index]
   _lowercase							      =temp

 _lowercase							      =a[new_pivot_index + 1]
 _lowercase							      =a[end]
 _lowercase							      =temp
 return new_pivot_index + 1, count


UpperCAmelCase__   	=							TemporaryFile()
UpperCAmelCase__   	=							100  # 1000 elements are to be sorted


UpperCAmelCase__	,UpperCAmelCase__   	=							0, 1  # mean and standard deviation
UpperCAmelCase__   	=							np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
UpperCAmelCase__   	=							np.load(outfile)
UpperCAmelCase__   	=							len(M) - 1
UpperCAmelCase__   	=							_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)



print(z)


"""simple docstring"""




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

__lowerCamelCase							 =     logging.get_logger(__name__)
@add_end_docstrings(__A       )
class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> Tuple:
													super().__init__(*__UpperCAmelCase						,**__UpperCAmelCase   )
													requires_backends(self						,'decord'   )
													self.check_model_type(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,__UpperCAmelCase=None   )    -> int:
													A__		   =							{}
													if frame_sampling_rate is not None:
																				A__		   =							frame_sampling_rate
													if num_frames is not None:
																				A__		   =							num_frames

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





						def __call__(				self						,__UpperCAmelCase						,**__UpperCAmelCase   )    -> Dict:
													return super().__call__(__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=None						,__UpperCAmelCase=1   )    -> Union[str, Any]:
													if num_frames is None:
																				A__		   =							self.model.config.num_frames

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

													A__		   =							VideoReader(__UpperCAmelCase   )
													videoreader.seek(0   )

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

													A__		   =							videoreader.get_batch(__UpperCAmelCase   ).asnumpy()
													A__		   =							list(__UpperCAmelCase   )

													A__		   =							self.image_processor(__UpperCAmelCase						,return_tensors=self.framework   )
													return model_inputs





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Dict:
													A__		   =							self.model(**__UpperCAmelCase   )
													return model_outputs





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=5   )    -> Union[str, Any]:
													if top_k > self.model.config.num_labels:
																				A__		   =							self.model.config.num_labels

													if self.framework == "pt":
																				A__		   =							model_outputs.logits.softmax(-1   )[0]
																				A__   ,     A__		   =							probs.topk(__UpperCAmelCase   )
													else:
																				raise ValueError(f'''Unsupported framework: {self.framework}'''   )

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





