




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

lowercase__							:	List[Any]											=		logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__     )
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	str			,      *__lowercase				:	str			,      **__lowercase				:	Dict		):






							"""simple docstring"""






							super().__init__(*__lowercase			,      **__lowercase		)
							requires_backends(self			,      "decord"		)
							self.check_model_type(__lowercase		)






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Optional[Any]=None			,      __lowercase				:	Tuple=None			,      __lowercase				:	Any=None		):






							"""simple docstring"""






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

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






						def __call__(		self				:	Optional[int]			,      __lowercase				:	Union[str, List[str]]			,      **__lowercase				:	Any		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Dict			,      __lowercase				:	int=None			,      __lowercase				:	Optional[int]=1		):






							"""simple docstring"""






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

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

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

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

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

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






						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.model(**__lowercase		)
							return model_outputs






						def 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	Optional[int]			,      __lowercase				:	str=5		):






							"""simple docstring"""






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

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

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






from __future__ import annotations

from dataclasses import dataclass
@dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  None
						lowerCAmelCase_							=  None







def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	def is_valid_tree(_A				) -> bool:
		if node is None:
			return True

		if not isinstance(_A	,      _A				):
			return False

		try:
			float(node.data				)
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left				) and is_valid_tree(node.right				)

	if not is_valid_tree(_A				):
		raise ValueError(
		    "Each node should be type of TreeNode and data should be float."				)

	def is_binary_search_tree_recursive_check(
	    _A	,      _A	,      _A				) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left	,      _A	,      node.data				)
		    and is_binary_search_tree_recursive_check(
		        node.right	,      node.data	,      _A				)
		)

	return is_binary_search_tree_recursive_check(_A	,      -float("inf"				)	,      float("inf"				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
