


from __future__ import annotations




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	List[str]								=len(lowerCamelCase   )
				# We need to create solution object to save path.
				__magic_name__	:	List[Any]								=[[0 for _ in range(lowerCamelCase   )] for _ in range(lowerCamelCase   )]
				__magic_name__	:	Tuple								=run_maze(lowerCamelCase	,     0	,     0	,     lowerCamelCase   )
				if solved:
								print("""\n""".join(str(lowerCamelCase   ) for row in solutions   )   )
				else:
								print("""No solution exists!"""   )
				return solved




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Tuple								=len(lowerCamelCase   )
				# Final check point.
				if i == j == (size - 1):
								__magic_name__	:	Tuple								=1
								return True

				__magic_name__	:	Optional[int]								=(not i < 0) and (not j < 0)  # Check lower bounds
				__magic_name__	:	List[str]								=(i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								__magic_name__	:	Any								=(not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												__magic_name__	:	Dict								=1

												# check for directions
												if (
												    run_maze(lowerCamelCase	,     i + 1	,     lowerCamelCase	,     lowerCamelCase   )
												    or run_maze(lowerCamelCase	,     lowerCamelCase	,     j + 1	,     lowerCamelCase   )
												    or run_maze(lowerCamelCase	,     i - 1	,     lowerCamelCase	,     lowerCamelCase   )
												    or run_maze(lowerCamelCase	,     lowerCamelCase	,     j - 1	,     lowerCamelCase   )
												):
																return True

												__magic_name__	:	Union[str, Any]								=0
												return False
				return False


if __name__ == "__main__":
							import doctest

							doctest.testmod()










from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_tf_available():
							import tensorflow as tf

							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

UpperCAmelCase_		:				str									=     logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__			)
class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Any    , *__snake_case   :List[Any]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										super().__init__(*__snake_case    , **__snake_case )
										requires_backends(self    , """vision""" )
										self.check_model_type(
										    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
										    if self.framework == """tf"""
										    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )

						def     A__						(		self   :int    , __snake_case   :Optional[Any]=None ):
										'''simple docstring'''

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

						def __call__(		self   :Tuple    , __snake_case   :Union[str, List[str], "Image.Image", List["Image.Image"]]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :str    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=load_image(__snake_case )
										__magic_name__	:	Any								=self.image_processor(images=__snake_case    , return_tensors=self.framework )
										return model_inputs

						def     A__						(		self   :int    , __snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model(**__snake_case )
										return model_outputs


						def     A__						(		self   :List[str]    , __snake_case   :int    , __snake_case   :Union[str, Any]=5 ):
										'''simple docstring'''

										if top_k > self.model.config.num_labels:
														__magic_name__	:	Dict								=self.model.config.num_labels

										if self.framework == "pt":
														__magic_name__	:	int								=model_outputs.logits.softmax(-1 )[0]
														__magic_name__				, __magic_name__	:	str								=probs.topk(__snake_case )
										elif self.framework == "tf":
														__magic_name__	:	Union[str, Any]								=stable_softmax(model_outputs.logits    , axis=-1 )[0]
														__magic_name__	:	List[str]								=tf.math.top_k(__snake_case    , k=__snake_case )
														__magic_name__				, __magic_name__	:	str								=topk.values.numpy(), topk.indices.numpy()
										else:
														raise ValueError(f"Unsupported framework: {self.framework}" )

										__magic_name__	:	str								=scores.tolist()
										__magic_name__	:	Union[str, Any]								=ids.tolist()
										return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__snake_case    , __snake_case )]




