
import numpy as np





class 			_lowerCAmelCase      :




				def __init__(      self  )       ->   List[str]:
							lowerCAmelCase_      			=			(0, 0)
							lowerCAmelCase_      			=			None
							lowerCAmelCase_      			=			0
							lowerCAmelCase_      			=			0
							lowerCAmelCase_      			=			0




				def __eq__(      self       ,       _UpperCamelCase  )       ->   int:
							return self.position == cell.position




				def   __a		(      self  )       ->   List[Any]:
							print(self.position  )





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase=(5, 5)  )       ->   Optional[int]:
							lowerCAmelCase_      			=			np.zeros(_UpperCamelCase  )
							lowerCAmelCase_      			=			world_size[0]
							lowerCAmelCase_      			=			world_size[1]




				def   __a		(      self  )       ->   Union[str, Any]:
							print(self.w  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Tuple:
							lowerCAmelCase_      			=			[
							    (-1, -1),
							    (-1, 0),
							    (-1, 1),
							    (0, -1),
							    (0, 1),
							    (1, -1),
							    (1, 0),
							    (1, 1),
							]
							lowerCAmelCase_      			=			cell.position[0]
							lowerCAmelCase_      			=			cell.position[1]
							lowerCAmelCase_      			=			[]
							for n in neughbour_cord:
										lowerCAmelCase_      			=			current_x + n[0]
										lowerCAmelCase_      			=			current_y + n[1]
										if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
													lowerCAmelCase_      			=			Cell()
													lowerCAmelCase_      			=			(x, y)
													lowerCAmelCase_      			=			cell
													neighbours.append(_UpperCamelCase  )
							return neighbours






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						Tuple       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]
			lowerCAmelCase_      			=			[]
			_open.append(__lowerCAmelCase       )

			while _open:
						lowerCAmelCase_      			=			np.argmin([n.f for n in _open]       )
						lowerCAmelCase_      			=			_open[min_f]
						_closed.append(_open.pop(__lowerCAmelCase       )       )
						if current == goal:
									break
						for n in world.get_neigbours(__lowerCAmelCase       ):
									for c in _closed:
												if c == n:
															continue
									lowerCAmelCase_      			=			current.g + 1
									lowerCAmelCase_			,						lowerCAmelCase_      			=			n.position
									lowerCAmelCase_			,						lowerCAmelCase_      			=			goal.position
									lowerCAmelCase_      			=			(ya - ya) ** 2 + (xa - xa) ** 2
									lowerCAmelCase_      			=			n.h + n.g

									for c in _open:
												if c == n and c.f < n.f:
															continue
									_open.append(__lowerCAmelCase       )
			lowerCAmelCase_      			=			[]
			while current.parent is not None:
						path.append(current.position       )
						lowerCAmelCase_      			=			current.parent
			path.append(current.position       )
			return path[::-1]


if __name__ == "__main__":
			_A     =  Gridworld()
			# Start position and goal
			_A     =  Cell()
			_A     =  (0, 0)
			_A     =  Cell()
			_A     =  (4, 4)
			print(f"""path from {start.position} to {goal.position}""")
			_A     =  astar(world, start, goal)
			#   Just for visual reasons.
			for i in s:
						_A     =  1
			print(world.w)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A     =  {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Wav2Vec2ForAudioFrameClassification",
			    "Wav2Vec2ForCTC",
			    "Wav2Vec2ForMaskedLM",
			    "Wav2Vec2ForPreTraining",
			    "Wav2Vec2ForSequenceClassification",
			    "Wav2Vec2ForXVector",
			    "Wav2Vec2Model",
			    "Wav2Vec2PreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFWav2Vec2ForCTC",
			    "TFWav2Vec2Model",
			    "TFWav2Vec2PreTrainedModel",
			    "TFWav2Vec2ForSequenceClassification",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "FlaxWav2Vec2ForCTC",
			    "FlaxWav2Vec2ForPreTraining",
			    "FlaxWav2Vec2Model",
			    "FlaxWav2Vec2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

			_A     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



