



'''simple docstring'''



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase	 =       logging.get_logger(__name__)

lowerCamelCase	 =       {
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}





class _UpperCamelCase		(     lowercase__		):





				'''simple docstring'''





				lowerCAmelCase__					    =    """unispeech-sat"""

				def __init__(							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  str=3_2  ,       _lowerCAmelCase  :  Any=7_6_8  ,       _lowerCAmelCase  :  Tuple=1_2  ,       _lowerCAmelCase  :  List[str]=1_2  ,       _lowerCAmelCase  :  int=3_0_7_2  ,       _lowerCAmelCase  :  Optional[Any]="gelu"  ,       _lowerCAmelCase  :  Tuple=0.1  ,       _lowerCAmelCase  :  List[Any]=0.1  ,       _lowerCAmelCase  :  Optional[int]=0.1  ,       _lowerCAmelCase  :  Optional[Any]=0.0  ,       _lowerCAmelCase  :  Optional[Any]=0.0  ,       _lowerCAmelCase  :  List[str]=0.1  ,       _lowerCAmelCase  :  str=0.1  ,       _lowerCAmelCase  :  Optional[int]=0.02  ,       _lowerCAmelCase  :  Optional[int]=1e-5  ,       _lowerCAmelCase  :  Any="group"  ,       _lowerCAmelCase  :  Dict="gelu"  ,       _lowerCAmelCase  :  List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)  ,       _lowerCAmelCase  :  Dict=(5, 2, 2, 2, 2, 2, 2)  ,       _lowerCAmelCase  :  Union[str, Any]=(1_0, 3, 3, 3, 3, 2, 2)  ,       _lowerCAmelCase  :  List[Any]=False  ,       _lowerCAmelCase  :  Union[str, Any]=1_2_8  ,       _lowerCAmelCase  :  Optional[int]=1_6  ,       _lowerCAmelCase  :  Union[str, Any]=False  ,       _lowerCAmelCase  :  Optional[int]=True  ,       _lowerCAmelCase  :  List[Any]=0.05  ,       _lowerCAmelCase  :  Any=1_0  ,       _lowerCAmelCase  :  Tuple=2  ,       _lowerCAmelCase  :  Optional[Any]=0.0  ,       _lowerCAmelCase  :  str=1_0  ,       _lowerCAmelCase  :  List[Any]=0  ,       _lowerCAmelCase  :  Dict=3_2_0  ,       _lowerCAmelCase  :  str=2  ,       _lowerCAmelCase  :  Optional[int]=0.1  ,       _lowerCAmelCase  :  int=1_0_0  ,       _lowerCAmelCase  :  Any=2_5_6  ,       _lowerCAmelCase  :  str=2_5_6  ,       _lowerCAmelCase  :  Tuple=0.1  ,       _lowerCAmelCase  :  Dict="mean"  ,       _lowerCAmelCase  :  Optional[Any]=False  ,       _lowerCAmelCase  :  Optional[Any]=False  ,       _lowerCAmelCase  :  Optional[Any]=2_5_6  ,       _lowerCAmelCase  :  Optional[int]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)  ,       _lowerCAmelCase  :  Optional[Any]=(5, 3, 3, 1, 1)  ,       _lowerCAmelCase  :  Optional[int]=(1, 2, 3, 1, 1)  ,       _lowerCAmelCase  :  Tuple=5_1_2  ,       _lowerCAmelCase  :  Tuple=0  ,       _lowerCAmelCase  :  str=1  ,       _lowerCAmelCase  :  Optional[Any]=2  ,       _lowerCAmelCase  :  List[str]=5_0_4  ,       **_lowerCAmelCase  :  Optional[int]  ,       ):







											'''simple docstring'''

											super().__init__(**__lowercase  ,       pad_token_id=__lowercase  ,       bos_token_id=__lowercase  ,       eos_token_id=__lowercase)
											__lowercase       	=    hidden_size
											__lowercase       	=    feat_extract_norm
											__lowercase       	=    feat_extract_activation
											__lowercase       	=    list(__lowercase)
											__lowercase       	=    list(__lowercase)
											__lowercase       	=    list(__lowercase)
											__lowercase       	=    conv_bias
											__lowercase       	=    num_conv_pos_embeddings
											__lowercase       	=    num_conv_pos_embedding_groups
											__lowercase       	=    len(self.conv_dim)
											__lowercase       	=    num_hidden_layers
											__lowercase       	=    intermediate_size
											__lowercase       	=    hidden_act
											__lowercase       	=    num_attention_heads
											__lowercase       	=    hidden_dropout
											__lowercase       	=    attention_dropout
											__lowercase       	=    activation_dropout
											__lowercase       	=    feat_proj_dropout
											__lowercase       	=    final_dropout
											__lowercase       	=    layerdrop
											__lowercase       	=    layer_norm_eps
											__lowercase       	=    initializer_range
											__lowercase       	=    vocab_size
											__lowercase       	=    num_clusters
											__lowercase       	=    do_stable_layer_norm
											__lowercase       	=    use_weighted_layer_sum

											if (
											    (len(self.conv_stride) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel) != self.num_feat_extract_layers)
											    or (len(self.conv_dim) != self.num_feat_extract_layers)
											):
																		raise ValueError(
																		    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
																		    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
																		    f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
																		    f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											__lowercase       	=    apply_spec_augment
											__lowercase       	=    mask_time_prob
											__lowercase       	=    mask_time_length
											__lowercase       	=    mask_time_min_masks
											__lowercase       	=    mask_feature_prob
											__lowercase       	=    mask_feature_length
											__lowercase       	=    mask_feature_min_masks

											# parameters for pretraining with codevector quantized representations
											__lowercase       	=    num_codevectors_per_group
											__lowercase       	=    num_codevector_groups
											__lowercase       	=    contrastive_logits_temperature
											__lowercase       	=    feat_quantizer_dropout
											__lowercase       	=    num_negatives
											__lowercase       	=    codevector_dim
											__lowercase       	=    proj_codevector_dim
											__lowercase       	=    diversity_loss_weight

											# ctc loss
											__lowercase       	=    ctc_loss_reduction
											__lowercase       	=    ctc_zero_infinity

											# SequenceClassification-specific parameter. Feel free to ignore for other classes.
											__lowercase       	=    classifier_proj_size

											# XVector-specific parameters. Feel free to ignore for other classes.
											__lowercase       	=    list(__lowercase)
											__lowercase       	=    list(__lowercase)
											__lowercase       	=    list(__lowercase)
											__lowercase       	=    xvector_output_dim

				@property
				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											return functools.reduce(operator.mul  ,       self.conv_stride  ,       1)








'''simple docstring'''



import numpy as np





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Dict):







											'''simple docstring'''

											__lowercase       	=(0, 0)
											__lowercase       	=None
											__lowercase       	=0
											__lowercase       	=0
											__lowercase       	=0

				def __eq__(							self  :  List[str]  ,       _lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											return self.position == cell.position

				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											print(self.position)





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Any  ,       _lowerCAmelCase  :  List[str]=(5, 5)):







											'''simple docstring'''

											__lowercase       	=np.zeros(_lowerCAmelCase)
											__lowercase       	=world_size[0]
											__lowercase       	=world_size[1]

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											print(self.w)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  List[str]):







											'''simple docstring'''

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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=[]
							__lowercase       	=[]
							_open.append(_lowerCAmelCase					)

							while _open:
														__lowercase       	=np.argmin([n.f for n in _open]					)
														__lowercase       	=_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
																					__lowercase       	=current.g + 1
																					__lowercase	,				__lowercase       	=n.position
																					__lowercase	,				__lowercase       	=goal.position
																					__lowercase       	=(ya - ya) ** 2 + (xa - xa) ** 2
																					__lowercase       	=n.h + n.g

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


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



