



'''simple docstring'''
from __future__ import annotations






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  ):
						_UpperCamelCase					:		Optional[Any] 				=   len(A__							)

						# If row is equal to the size of the board it means there are a queen in each row in
						# the current board (possible_board)
						if row == n:
												# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
												# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
												boards.append(['. ' * i + 'Q ' + '. ' * (n - 1 - i) for i in possible_board]							)
												return

						# We iterate each column in the row to find all possible results in each row
						for col in range(A__							):
												# We apply that we learned previously. First we check that in the current board
												# (possible_board) there are not other same value because if there is it means
												# that there are a collision in vertical. Then we apply the two formulas we
												# learned before:
												#
												# 45º: y - x = b or 45: row - col = b
												# 135º: y + x = b or row + col = b.
												#
												# And we verify if the results of this two formulas not exist in their variables
												# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
												#
												# If any or these are True it means there is a collision so we continue to the
												# next value in the for loop.
												if (
												    col in possible_board
												    or row - col in diagonal_right_collisions
												    or row + col in diagonal_left_collisions
												):
																		continue

												# If it is False we call dfs function again and we update the inputs
												depth_first_search(
												    [*possible_board, col]  ,  [*diagonal_right_collisions, row - col]  ,  [*diagonal_left_collisions, row + col]  ,  A__  ,  A__  ,  )






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		Tuple 				=   []
						depth_first_search([]  ,  []  ,  []  ,  A__  ,  A__							)

						# Print all the boards
						for board in boards:
												for column in board:
																		print(A__							)
												print(''							)

						print(len(A__							)  ,  'solutions were found.'							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					n_queens_solution(4)







'''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

_lowerCamelCase	:       Any      	=	logging.get_logger(__name__)

@add_end_docstrings(_a		)
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						*UpperCamelCase__      :					Dict  ,						**UpperCamelCase__      :					Union[str, Any] ):


       """simple docstring"""







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






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					Optional[Any]=None  ,						UpperCamelCase__      :					Optional[Any]=None ):


       """simple docstring"""







       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      :					List[str]  ,						UpperCamelCase__      :					Union[str, List[str]]  ,						**UpperCamelCase__      :					Dict ):


       """simple docstring"""







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






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple=None  ,						UpperCamelCase__      :					Tuple=1 ):


       """simple docstring"""







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

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

       UpperCamelCase					=  VideoReader(UpperCamelCase__ )
       videoreader.seek(0 )

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

       UpperCamelCase					=  videoreader.get_batch(UpperCamelCase__ ).asnumpy()
       UpperCamelCase					=  list(UpperCamelCase__ )

       UpperCamelCase					=  self.image_processor(UpperCamelCase__  ,						return_tensors=self.framework )
       return model_inputs






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  self.model(**UpperCamelCase__ )
       return model_outputs






    def  A       (  self      :					int  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any]=5 ):


       """simple docstring"""







       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(UpperCamelCase__ )
       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(UpperCamelCase__  ,						UpperCamelCase__ )]

