





'''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

__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.get_logger(__name__)







@add_end_docstrings(lowerCAmelCase_			)
class   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    Tuple	,			*snake_case_       :    Optional[int]	,			**snake_case_       :    Optional[int]    ):
      super().__init__(*snake_case_	,			**snake_case_    )
      requires_backends(self	,			"decord"    )
      self.check_model_type(snake_case_    )




 def      lowercase	(					self       :    Optional[int]	,			snake_case_       :    List[str]=None	,			snake_case_       :    Union[str, Any]=None	,			snake_case_       :    Union[str, Any]=None    ):
      _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       :    Any	,			snake_case_       :    Union[str, List[str]]	,			**snake_case_       :    int    ):
      return super().__call__(snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    int	,			snake_case_       :    str=None	,			snake_case_       :    List[Any]=1    ):
      if num_frames is None:
           _UpperCAmelCase     							=   self.model.config.num_frames

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

      _UpperCAmelCase     							=   VideoReader(snake_case_    )
      videoreader.seek(0    )

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

      _UpperCAmelCase     							=   videoreader.get_batch(snake_case_    ).asnumpy()
      _UpperCAmelCase     							=   list(snake_case_    )

      _UpperCAmelCase     							=   self.image_processor(snake_case_	,			return_tensors=self.framework    )
      return model_inputs




 def      lowercase	(					self       :    str	,			snake_case_       :    Dict    ):
      _UpperCAmelCase     							=   self.model(**snake_case_    )
      return model_outputs




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Dict	,			snake_case_       :    Dict=5    ):
      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(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(snake_case_	,			snake_case_    )]











'''simple docstring'''




import string
from math import logaa
def 	UpperCAmelCase_							(					__lowercase							:      str			,     __lowercase							:      str       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   document.translate(
         str.maketrans(""			,     ""			,     string.punctuation       )       ).replace("\n"			,     ""       )
     _UpperCAmelCase     							=   document_without_punctuation.split(" "       )  # word tokenization
     return len([word for word in tokenize_document if word.lower() == term.lower()]       )
def 	UpperCAmelCase_							(					__lowercase							:      str			,     __lowercase							:      str       )		->       tuple[int, int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   corpus.lower().translate(
         str.maketrans(""			,     ""			,     string.punctuation       )       )  # strip all punctuation and replace it with ''
     _UpperCAmelCase     							=   corpus_without_punctuation.split("\n"       )
     _UpperCAmelCase     							=   term.lower()
     return (len([doc for doc in docs if term in doc]       ), len(__lowercase       ))
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      int			,     __lowercase							:      Union[str, Any]=False       )		->       float:


     '''simple docstring'''



     if smoothing:
          if n == 0:
               raise ValueError("log10(0) is undefined."       )
          return round(1 + logaa(n / (1 + df)       )			,     3       )

     if df == 0:
          raise ZeroDivisionError("df must be > 0"       )
     elif n == 0:
          raise ValueError("log10(0) is undefined."       )
     return round(logaa(n / df       )			,     3       )






def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      int       )		->       float:


     '''simple docstring'''



     return round(tf * idf			,     3       )




