


from __future__ import annotations






class      UpperCamelCase					:


			'''simple docstring'''







			def __init__(     self   ,							UpperCamelCase_		):
								lowercase_       :Tuple     =       order

								# a_{0} ... a_{k}
								lowercase_       :Any     =       [1.0] + [0.0] * order
								# b_{0} ... b_{k}
								lowercase_       :Optional[Any]     =       [1.0] + [0.0] * order

								# x[n-1] ... x[n-k]
								lowercase_       :int     =       [0.0] * self.order
								# y[n-1] ... y[n-k]
								lowercase_       :Any     =       [0.0] * self.order
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
								if len(UpperCamelCase_		) < self.order:
													lowercase_       :Union[str, Any]     =       [1.0, *a_coeffs]

								if len(UpperCamelCase_		) != self.order + 1:
													lowercase_       :Tuple     =       (
													    f"Expected a_coeffs to have {self.order + 1} elements "
													    f"for {self.order}-order filter, got {len(UpperCamelCase_		)}"
													)
													raise ValueError(UpperCamelCase_		)

								if len(UpperCamelCase_		) != self.order + 1:
													lowercase_       :Tuple     =       (
													    f"Expected b_coeffs to have {self.order + 1} elements "
													    f"for {self.order}-order filter, got {len(UpperCamelCase_		)}"
													)
													raise ValueError(UpperCamelCase_		)

								lowercase_       :Optional[int]     =       a_coeffs
								lowercase_       :Optional[Any]     =       b_coeffs




			def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
								lowercase_       :Tuple     =       0.0

								# Start at index 1 and do index 0 at the end.
								for i in range(1   ,							self.order + 1		):
													result += (
													    self.b_coeffs[i] * self.input_history[i - 1]
													    - self.a_coeffs[i] * self.output_history[i - 1]
													)

								lowercase_       :int     =       (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

								lowercase_       :Dict     =       self.input_history[:-1]
								lowercase_       :Optional[Any]     =       self.output_history[:-1]

								lowercase_       :int     =       sample
								lowercase_       :str     =       result

								return result





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       :		List[str]         =				logging.get_logger(__name__)






@add_end_docstrings(lowercase__     )
class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							*UpperCamelCase_   ,							**UpperCamelCase_		):
        super().__init__(*UpperCamelCase_   ,							**UpperCamelCase_		)
        requires_backends(self   ,							'''decord'''		)
        self.check_model_type(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None		):
        lowercase_       :int     =       {}
        if frame_sampling_rate is not None:
             lowercase_       :int     =       frame_sampling_rate
        if num_frames is not None:
             lowercase_       :int     =       num_frames

        lowercase_       :str     =       {}
        if top_k is not None:
             lowercase_       :Optional[int]     =       top_k
        return preprocess_params, {}, postprocess_params
   def __call__(     self   ,							UpperCamelCase_   ,							**UpperCamelCase_		):
        return super().__call__(UpperCamelCase_   ,							**UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=None   ,							UpperCamelCase_=1		):
        if num_frames is None:
             lowercase_       :str     =       self.model.config.num_frames

        if video.startswith('''http://'''		) or video.startswith('''https://'''		):
             lowercase_       :str     =       BytesIO(requests.get(UpperCamelCase_		).content		)

        lowercase_       :Optional[int]     =       VideoReader(UpperCamelCase_		)
        videoreader.seek(0		)

        lowercase_       :Tuple     =       0
        lowercase_       :Optional[Any]     =       num_frames * frame_sampling_rate - 1
        lowercase_       :Any     =       np.linspace(UpperCamelCase_   ,							UpperCamelCase_   ,							num=UpperCamelCase_   ,							dtype=np.intaa		)

        lowercase_       :Dict     =       videoreader.get_batch(UpperCamelCase_		).asnumpy()
        lowercase_       :List[Any]     =       list(UpperCamelCase_		)

        lowercase_       :Any     =       self.image_processor(UpperCamelCase_   ,							return_tensors=self.framework		)
        return model_inputs
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :List[str]     =       self.model(**UpperCamelCase_		)
        return model_outputs




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=5		):
        if top_k > self.model.config.num_labels:
             lowercase_       :List[str]     =       self.model.config.num_labels

        if self.framework == "pt":
             lowercase_       :Optional[int]     =       model_outputs.logits.softmax(-1		)[0]
             lowercase_    ,      lowercase_       :Dict     =       probs.topk(UpperCamelCase_		)
        else:
             raise ValueError(f"Unsupported framework: {self.framework}"		)

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

