
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

__a  			=					logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase							)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Optional[int]       ,							*SCREAMING_SNAKE_CASE_  :			Tuple       ,							**SCREAMING_SNAKE_CASE_  :			Union[str, Any]       )       ->     Union[str, Any]:
					super().__init__(*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
					requires_backends(self       ,							'''decord'''       )
					self.check_model_type(SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			int=None       ,							SCREAMING_SNAKE_CASE_  :			str=None       ,							SCREAMING_SNAKE_CASE_  :			int=None       )       ->     Union[str, Any]:
					lowercase_       =					{}
					if frame_sampling_rate is not None:
									lowercase_       =					frame_sampling_rate
					if num_frames is not None:
									lowercase_       =					num_frames

					lowercase_       =					{}
					if top_k is not None:
									lowercase_       =					top_k
					return preprocess_params, {}, postprocess_params
	def __call__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Union[str, List[str]]       ,							**SCREAMING_SNAKE_CASE_  :			Dict       )       ->     List[Any]:
					return super().__call__(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=None       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=1       )       ->     int:
					if num_frames is None:
									lowercase_       =					self.model.config.num_frames

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

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

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

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

					lowercase_       =					self.image_processor(SCREAMING_SNAKE_CASE_       ,							return_tensors=self.framework       )
					return model_inputs
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     str:
					lowercase_       =					self.model(**SCREAMING_SNAKE_CASE_       )
					return model_outputs




	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=5       )       ->     Optional[Any]:
					if top_k > self.model.config.num_labels:
									lowercase_       =					self.model.config.num_labels

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

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











'''simple docstring'''

from __future__ import annotations






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :int							)->							None:
     A__				=			order

     # a_{0} ... a_{k}
     A__				=			[1.0] + [0.0] * order
     # b_{0} ... b_{k}
     A__				=			[1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     A__				=			[0.0] * self.order
     # y[n-1] ... y[n-k]
     A__				=			[0.0] * self.order
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :list[float]						,       lowercase_      :list[float]							)->							None:
     if len(lowercase_							) < self.order:
         A__				=			[1.0, *a_coeffs]

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

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

     A__				=			a_coeffs
     A__				=			b_coeffs



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :float							)->							float:
     A__				=			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]
         )

     A__				=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     A__				=			self.input_history[:-1]
     A__				=			self.output_history[:-1]

     A__				=			sample
     A__				=			result

     return result



