"""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									=						logging.get_logger(__name__)



@add_end_docstrings(__lowerCamelCase     )
class  _lowerCamelCase			(__lowerCamelCase     ):







				def __init__( self    :       Any			,					*lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					super().__init__(*UpperCamelCase_			,					**UpperCamelCase_						)
					requires_backends(self			,					'decord'						)
					self.check_model_type(UpperCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       List[Any]=None			,					lowerCamelCase_    :       str=None			,					lowerCamelCase_    :       List[Any]=None						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							{}
					if frame_sampling_rate is not None:
						_lowercase :    str							=							frame_sampling_rate
					if num_frames is not None:
						_lowercase :    int							=							num_frames

					_lowercase :    str							=							{}
					if top_k is not None:
						_lowercase :    Any							=							top_k
					return preprocess_params, {}, postprocess_params







				def __call__( self    :       Union[str, Any]			,					lowerCamelCase_    :       Union[str, List[str]]			,					**lowerCamelCase_    :       str						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       int=None			,					lowerCamelCase_    :       Tuple=1						):

					"""simple docstring"""






					if num_frames is None:
						_lowercase :    str							=							self.model.config.num_frames

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

					_lowercase :    str							=							VideoReader(UpperCamelCase_						)
					videoreader.seek(0						)

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

					_lowercase :    Union[str, Any]							=							videoreader.get_batch(UpperCamelCase_						).asnumpy()
					_lowercase :    Dict							=							list(UpperCamelCase_						)

					_lowercase :    Optional[Any]							=							self.image_processor(UpperCamelCase_			,					return_tensors=self.framework						)
					return model_inputs







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					_lowercase :    List[str]							=							self.model(**UpperCamelCase_						)
					return model_outputs







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Optional[int]=5						):

					"""simple docstring"""






					if top_k > self.model.config.num_labels:
						_lowercase :    Optional[int]							=							self.model.config.num_labels

					if self.framework == "pt":
						_lowercase :    List[Any]							=							model_outputs.logits.softmax(-1						)[0]
						_lowercase :    Tuple							=							probs.topk(UpperCamelCase_						)
					else:
						raise ValueError(F'''Unsupported framework: {self.framework}'''						)

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




"""simple docstring"""







from __future__ import annotations

SCREAMING_SNAKE_CASE									=						[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,):






	"""simple docstring"""





	_lowercase :    List[str]							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the reference grid
	_lowercase :    Optional[Any]							=							1
	_lowercase :    Any							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the action grid

	_lowercase :    List[Any]							=							init[0]
	_lowercase :    Optional[Any]							=							init[1]
	_lowercase :    Union[str, Any]							=							0
	_lowercase :    Union[str, Any]							=							g + heuristic[x][y]  # cost from starting cell to destination cell
	_lowercase :    Dict							=							[[f, g, x, y]]

	_lowercase :    Tuple							=							False  # flag that is set when search is complete
	_lowercase :    Tuple							=							False  # flag set if we can't find expand

	while not found and not resign:
		if len(__UpperCAmelCase			) == 0:
			raise ValueError('Algorithm is unable to find solution'			)
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			_lowercase :    List[Any]							=							cell.pop()
			_lowercase :    Optional[Any]							=							next_cell[2]
			_lowercase :    List[str]							=							next_cell[3]
			_lowercase :    Optional[int]							=							next_cell[1]

			if x == goal[0] and y == goal[1]:
				_lowercase :    Optional[Any]							=							True
			else:
				for i in range(len(__UpperCAmelCase			)			):  # to try out different valid actions
					_lowercase :    Any							=							x + DIRECTIONS[i][0]
					_lowercase :    Union[str, Any]							=							y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(__UpperCAmelCase			) and ya >= 0 and ya < len(grid[0]			):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							_lowercase :    int							=							g + cost
							_lowercase :    List[str]							=							ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]			)
							_lowercase :    List[Any]							=							1
							_lowercase :    Dict							=							i
	_lowercase :    Union[str, Any]							=							[]
	_lowercase :    Optional[int]							=							goal[0]
	_lowercase :    Any							=							goal[1]
	invpath.append([x, y]			)  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		_lowercase :    str							=							x - DIRECTIONS[action[x][y]][0]
		_lowercase :    Any							=							y - DIRECTIONS[action[x][y]][1]
		_lowercase :    Dict							=							xa
		_lowercase :    Tuple							=							ya
		invpath.append([x, y]			)

	_lowercase :    List[str]							=							[]
	for i in range(len(__UpperCAmelCase			)			):
		path.append(invpath[len(__UpperCAmelCase			) - 1 - i]			)
	return path, action


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						[
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 1, 0],
						    [0, 0, 0, 0, 1, 0],
						]

						SCREAMING_SNAKE_CASE									=						[0, 0]
						# all coordinates are given in format [y,x]
						SCREAMING_SNAKE_CASE									=						[len(grid) - 1, len(grid[0]) - 1]
						SCREAMING_SNAKE_CASE									=						1

						# the cost map which pushes the path closer to the goal
						SCREAMING_SNAKE_CASE									=						[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
						for i in range(len(grid)):
												for j in range(len(grid[0])):
																		SCREAMING_SNAKE_CASE									=						abs(i - goal[0]) + abs(j - goal[1])
																		if grid[i][j] == 1:
																								# added extra penalty in the heuristic map
																								SCREAMING_SNAKE_CASE									=						99

						SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE									=						search(grid, init, goal, cost, heuristic)

						print('ACTION MAP')
						for i in range(len(action)):
												print(action[i])

						for i in range(len(path)):
												print(path[i])



