



'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :Tuple       ):
				for attribute in key.split('''.'''       ):
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= getattr(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				if weight_type is not None:
								SCREAMING_SNAKE_CASE  :						Any      					= getattr(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ).shape
				else:
								SCREAMING_SNAKE_CASE  :						Tuple      					= hf_pointer.shape

				assert hf_shape == value.shape, (
				    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
				    F''' {value.shape} for {full_name}'''
				)

				if weight_type == "weight":
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= value
				elif weight_type == "weight_g":
								SCREAMING_SNAKE_CASE  :						Dict      					= value
				elif weight_type == "weight_v":
								SCREAMING_SNAKE_CASE  :						List[str]      					= value
				elif weight_type == "bias":
								SCREAMING_SNAKE_CASE  :						List[Any]      					= value
				else:
								SCREAMING_SNAKE_CASE  :						List[str]      					= value

				logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				SCREAMING_SNAKE_CASE  :						Tuple      					= []
				SCREAMING_SNAKE_CASE  :						int      					= fairseq_model.state_dict()

				SCREAMING_SNAKE_CASE  :						Any      					= hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								SCREAMING_SNAKE_CASE  :						Any      					= False
								if "conv_layers" in name:
												load_conv_layer(
												    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	hf_model.config.feat_extract_norm == '''group'''    ,	)
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= True
								else:
												for key, mapped_key in MAPPING.items():
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
																				SCREAMING_SNAKE_CASE  :						Optional[int]      					= True
																				if "*" in mapped_key:
																								SCREAMING_SNAKE_CASE  :						Dict      					= name.split(_SCREAMING_SNAKE_CASE       )[0].split('''.'''       )[-2]
																								SCREAMING_SNAKE_CASE  :						int      					= mapped_key.replace('''*'''    ,	_SCREAMING_SNAKE_CASE       )
																				if "weight_g" in name:
																								SCREAMING_SNAKE_CASE  :						List[str]      					= '''weight_g'''
																				elif "weight_v" in name:
																								SCREAMING_SNAKE_CASE  :						Any      					= '''weight_v'''
																				elif "weight" in name:
																								SCREAMING_SNAKE_CASE  :						Dict      					= '''weight'''
																				elif "bias" in name:
																								SCREAMING_SNAKE_CASE  :						Any      					= '''bias'''
																				else:
																								SCREAMING_SNAKE_CASE  :						Dict      					= None
																				set_recursively(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																continue
								if not is_used:
												unused_weights.append(_SCREAMING_SNAKE_CASE       )

				logger.warning(F'''Unused weights: {unused_weights}'''       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :int       ):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= full_name.split('''conv_layers.'''       )[-1]
				SCREAMING_SNAKE_CASE  :						str      					= name.split('''.'''       )
				SCREAMING_SNAKE_CASE  :						Dict      					= int(items[0]       )
				SCREAMING_SNAKE_CASE  :						str      					= int(items[1]       )

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
												)
												SCREAMING_SNAKE_CASE  :						str      					= value
												logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
												)
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= value
												logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
												    " found."
												)
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
												)
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
				else:
								unused_weights.append(_SCREAMING_SNAKE_CASE       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Any       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= SEWConfig()
				if is_finetuned:
								SCREAMING_SNAKE_CASE  :						int      					= model.wav_encoder.wav_model.cfg
				else:
								SCREAMING_SNAKE_CASE  :						Tuple      					= model.cfg

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= fs_config.conv_bias
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= eval(fs_config.conv_feature_layers       )
				SCREAMING_SNAKE_CASE  :						int      					= [x[0] for x in conv_layers]
				SCREAMING_SNAKE_CASE  :						Dict      					= [x[1] for x in conv_layers]
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [x[2] for x in conv_layers]
				SCREAMING_SNAKE_CASE  :						List[Any]      					= '''gelu'''
				SCREAMING_SNAKE_CASE  :						Tuple      					= '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
				SCREAMING_SNAKE_CASE  :						List[Any]      					= 0.0
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= fs_config.activation_fn.name
				SCREAMING_SNAKE_CASE  :						List[str]      					= fs_config.encoder_embed_dim
				SCREAMING_SNAKE_CASE  :						List[str]      					= 0.02
				SCREAMING_SNAKE_CASE  :						Any      					= fs_config.encoder_ffn_embed_dim
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= 1E-5
				SCREAMING_SNAKE_CASE  :						List[str]      					= fs_config.encoder_layerdrop
				SCREAMING_SNAKE_CASE  :						Dict      					= fs_config.encoder_attention_heads
				SCREAMING_SNAKE_CASE  :						Tuple      					= fs_config.conv_pos_groups
				SCREAMING_SNAKE_CASE  :						Any      					= fs_config.conv_pos
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= len(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						str      					= fs_config.encoder_layers
				SCREAMING_SNAKE_CASE  :						Dict      					= fs_config.squeeze_factor

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								SCREAMING_SNAKE_CASE  :						Any      					= model.cfg
								SCREAMING_SNAKE_CASE  :						Any      					= fs_config.final_dropout
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= fs_config.layerdrop
				SCREAMING_SNAKE_CASE  :						int      					= fs_config.activation_dropout
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= fs_config.attention_dropout
				SCREAMING_SNAKE_CASE  :						Any      					= fs_config.dropout_input
				SCREAMING_SNAKE_CASE  :						List[str]      					= fs_config.dropout
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= fs_config.mask_channel_length
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= fs_config.mask_channel_prob
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= fs_config.mask_length
				SCREAMING_SNAKE_CASE  :						int      					= fs_config.mask_prob

				SCREAMING_SNAKE_CASE  :						Any      					= '''Wav2Vec2FeatureExtractor'''
				SCREAMING_SNAKE_CASE  :						int      					= '''Wav2Vec2CTCTokenizer'''

				return config






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Dict=None    ,	_SCREAMING_SNAKE_CASE      :List[str]=None    ,	_SCREAMING_SNAKE_CASE      :List[str]=True       ):

				if is_finetuned:
								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Dict      					= fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]    ,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
				else:
								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						str      					= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

				if config_path is not None:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= SEWConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
				else:
								SCREAMING_SNAKE_CASE  :						str      					= convert_config(model[0]    ,	_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= model[0].eval()

				SCREAMING_SNAKE_CASE  :						List[str]      					= True if config.feat_extract_norm == '''layer''' else False
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= WavaVecaFeatureExtractor(
				    feature_size=1    ,	sampling_rate=1_60_00    ,	padding_value=0    ,	do_normalize=_SCREAMING_SNAKE_CASE    ,	return_attention_mask=_SCREAMING_SNAKE_CASE    ,	)

				if is_finetuned:
								if dict_path:
												SCREAMING_SNAKE_CASE  :						Dict      					= Dictionary.load(_SCREAMING_SNAKE_CASE       )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												SCREAMING_SNAKE_CASE  :						Tuple      					= target_dict.pad_index
												SCREAMING_SNAKE_CASE  :						List[str]      					= target_dict.bos_index
												SCREAMING_SNAKE_CASE  :						Any      					= target_dict.pad_index
												SCREAMING_SNAKE_CASE  :						str      					= target_dict.bos_index
												SCREAMING_SNAKE_CASE  :						Dict      					= target_dict.eos_index
												SCREAMING_SNAKE_CASE  :						List[Any]      					= len(target_dict.symbols       )
												SCREAMING_SNAKE_CASE  :						Tuple      					= os.path.join(_SCREAMING_SNAKE_CASE    ,	'''vocab.json'''       )
												if not os.path.isdir(_SCREAMING_SNAKE_CASE       ):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_SCREAMING_SNAKE_CASE       )       )
																return
												os.makedirs(_SCREAMING_SNAKE_CASE    ,	exist_ok=_SCREAMING_SNAKE_CASE       )
												with open(_SCREAMING_SNAKE_CASE    ,	'''w'''    ,	encoding='''utf-8'''       ) as vocab_handle:
																json.dump(target_dict.indices    ,	_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						str      					= WavaVecaCTCTokenizer(
												    _SCREAMING_SNAKE_CASE    ,	unk_token=target_dict.unk_word    ,	pad_token=target_dict.pad_word    ,	bos_token=target_dict.bos_word    ,	eos_token=target_dict.eos_word    ,	word_delimiter_token='''|'''    ,	do_lower_case=_SCREAMING_SNAKE_CASE    ,	)
												SCREAMING_SNAKE_CASE  :						List[str]      					= WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE    ,	tokenizer=_SCREAMING_SNAKE_CASE       )
												processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

								SCREAMING_SNAKE_CASE  :						List[str]      					= SEWForCTC(_SCREAMING_SNAKE_CASE       )
				else:
								SCREAMING_SNAKE_CASE  :						Dict      					= SEWModel(_SCREAMING_SNAKE_CASE       )
								feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE       )

				recursively_load_weights(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				hf_model.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	snake_case_			=     argparse.ArgumentParser()
	parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
	parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
	parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
	parser.add_argument(
	    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
	)
	snake_case_			=     parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)









'''simple docstring'''

import heapq
import sys

import numpy as np

snake_case_			=     tuple[int, int]


class 	a__     :


	def __init__(self : int      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= []
					SCREAMING_SNAKE_CASE  :						Tuple      					= set()


	def 				lowercase__  (self : Any      )     ->			Dict:

					"""simple docstring"""

					if not self.empty():
									return self.elements[0][0]
					else:
									return float('''inf'''      )


	def 				lowercase__  (self : Dict      )     ->			Union[str, Any]:

					"""simple docstring"""

					return len(self.elements      ) == 0


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : int,					__UpperCAmelCase : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					if item not in self.set:
									heapq.heappush(self.elements,					(priority, item)      )
									self.set.add(__UpperCAmelCase      )
					else:
									# update
									# print("update", item)
									SCREAMING_SNAKE_CASE  :						List[Any]      					= []
									((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Any      					= heapq.heappop(self.elements      )
									while x != item:
													temp.append((pri, x)      )
													((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Any      					= heapq.heappop(self.elements      )
									temp.append((priority, item)      )
									for pro, xxx in temp:
													heapq.heappush(self.elements,					(pro, xxx)      )


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					if item in self.set:
									self.set.remove(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
									((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Any      					= heapq.heappop(self.elements      )
									while x != item:
													temp.append((pro, x)      )
													((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Optional[Any]      					= heapq.heappop(self.elements      )
									for prito, yyy in temp:
													heapq.heappush(self.elements,					(prito, yyy)      )


	def 				lowercase__  (self : Tuple      )     ->			Dict:

					"""simple docstring"""

					return self.elements[0][1]






	def 				lowercase__  (self : Optional[Any]      )     ->			Tuple:

					"""simple docstring"""

					((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Optional[int]      					= heapq.heappop(self.elements      )
					self.set.remove(__UpperCAmelCase      )
					return (priority, item)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :TPos       ):
				# euclidean distance
				SCREAMING_SNAKE_CASE  :						str      					= np.array(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= np.array(_SCREAMING_SNAKE_CASE       )
				return np.linalg.norm(a - b       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :TPos       ):
				# integer division by time variable
				return consistent_heuristic(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ) // t






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :TPos       ):
				# manhattan distance
				return abs(p[0] - goal[0]       ) + abs(p[1] - goal[1]       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :dict[TPos, float]       ):
				SCREAMING_SNAKE_CASE  :						List[str]      					= g_function[start] + Wa * heuristics[i](_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				return ans






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Tuple       ):
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= np.chararray((n, n)       )
				for i in range(_SCREAMING_SNAKE_CASE       ):
								for j in range(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						Tuple      					= '''*'''

				for i in range(_SCREAMING_SNAKE_CASE       ):
								for j in range(_SCREAMING_SNAKE_CASE       ):
												if (j, (n - 1) - i) in blocks:
																SCREAMING_SNAKE_CASE  :						Tuple      					= '''#'''

				SCREAMING_SNAKE_CASE  :						List[Any]      					= '''-'''
				SCREAMING_SNAKE_CASE  :						Any      					= back_pointer[goal]
				while x != start:
								((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Optional[int]      					= x
								# print(x)
								SCREAMING_SNAKE_CASE  :						int      					= '''-'''
								SCREAMING_SNAKE_CASE  :						Dict      					= back_pointer[x]
				SCREAMING_SNAKE_CASE  :						int      					= '''-'''

				for i in range(_SCREAMING_SNAKE_CASE       ):
								for j in range(_SCREAMING_SNAKE_CASE       ):
												if (i, j) == (0, n - 1):
																print(grid[i][j]    ,	end=''' '''       )
																print('''<-- End position'''    ,	end=''' '''       )
												else:
																print(grid[i][j]    ,	end=''' '''       )
								print()
				print('''^'''       )
				print('''Start position'''       )
				print()
				print('''# is an obstacle'''       )
				print('''- is the path taken by algorithm'''       )
				print('''PATH TAKEN BY THE ALGORITHM IS:-'''       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= back_pointer[goal]
				while x != start:
								print(_SCREAMING_SNAKE_CASE    ,	end=''' '''       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= back_pointer[x]
				print(_SCREAMING_SNAKE_CASE       )
				sys.exit()






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos       ):
				if p[0] < 0 or p[0] > n - 1:
								return False
				if p[1] < 0 or p[1] > n - 1:
								return False
				return True






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Optional[int]    ,	):
				for itera in range(_SCREAMING_SNAKE_CASE       ):
								open_list[itera].remove_element(_SCREAMING_SNAKE_CASE       )
				# print("s", s)
				# print("j", j)
				((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Tuple      					= s
				SCREAMING_SNAKE_CASE  :						List[Any]      					= (x - 1, y)
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= (x + 1, y)
				SCREAMING_SNAKE_CASE  :						List[Any]      					= (x, y + 1)
				SCREAMING_SNAKE_CASE  :						Any      					= (x, y - 1)

				for neighbours in [left, right, up, down]:
								if neighbours not in blocks:
												if valid(_SCREAMING_SNAKE_CASE       ) and neighbours not in visited:
																# print("neighbour", neighbours)
																visited.add(_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= -1
																SCREAMING_SNAKE_CASE  :						List[str]      					= float('''inf'''       )

												if valid(_SCREAMING_SNAKE_CASE       ) and g_function[neighbours] > g_function[s] + 1:
																SCREAMING_SNAKE_CASE  :						List[str]      					= g_function[s] + 1
																SCREAMING_SNAKE_CASE  :						Optional[Any]      					= s
																if neighbours not in close_list_anchor:
																				open_list[0].put(_SCREAMING_SNAKE_CASE    ,	key(_SCREAMING_SNAKE_CASE    ,	0    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )       )
																				if neighbours not in close_list_inad:
																								for var in range(1    ,	_SCREAMING_SNAKE_CASE       ):
																												if key(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ) <= Wa * key(
																												    _SCREAMING_SNAKE_CASE    ,	0    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ):
																																open_list[j].put(
																																    _SCREAMING_SNAKE_CASE    ,	key(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )       )






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
				for x in range(1    ,	5       ):
								for y in range(1    ,	6       ):
												some_list.append((x, y)       )

				for x in range(15    ,	20       ):
								some_list.append((x, 17)       )

				for x in range(10    ,	19       ):
								for y in range(1    ,	15       ):
												some_list.append((x, y)       )

    # L block
				for x in range(1    ,	4       ):
								for y in range(12    ,	19       ):
												some_list.append((x, y)       )
				for x in range(3    ,	13       ):
								for y in range(16    ,	19       ):
												some_list.append((x, y)       )
				return some_list


snake_case_			=     {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

snake_case_			=     [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
snake_case_			=     make_common_ground()


snake_case_			=     blocks_blk
# hyper parameters
snake_case_			=     1
snake_case_			=     1
snake_case_			=     20
snake_case_			=     3  # one consistent and two other inconsistent

# start and end destination
snake_case_			=     (0, 0)
snake_case_			=     (n - 1, n - 1)

snake_case_			=     1






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :int       ):
				SCREAMING_SNAKE_CASE  :						Any      					= {start: 0, goal: float('''inf'''       )}
				SCREAMING_SNAKE_CASE  :						Tuple      					= {start: -1, goal: -1}
				SCREAMING_SNAKE_CASE  :						Tuple      					= []
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= set()

				for i in range(_SCREAMING_SNAKE_CASE       ):
								open_list.append(PriorityQueue()       )
								open_list[i].put(_SCREAMING_SNAKE_CASE    ,	key(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )       )

				SCREAMING_SNAKE_CASE  :						list[int]      					= []
				SCREAMING_SNAKE_CASE  :						list[int]      					= []
				while open_list[0].minkey() < float('''inf'''       ):
								for i in range(1    ,	_SCREAMING_SNAKE_CASE       ):
												# print(open_list[0].minkey(), open_list[i].minkey())
												if open_list[i].minkey() <= Wa * open_list[0].minkey():
																global t
																t += 1
																if g_function[goal] <= open_list[i].minkey():
																				if g_function[goal] < float('''inf'''       ):
																								do_something(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																else:
																				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= open_list[i].top_show()
																				visited.add(_SCREAMING_SNAKE_CASE       )
																				expand_state(
																				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	)
																				close_list_inad.append(_SCREAMING_SNAKE_CASE       )
												else:
																if g_function[goal] <= open_list[0].minkey():
																				if g_function[goal] < float('''inf'''       ):
																								do_something(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																else:
																				SCREAMING_SNAKE_CASE  :						List[Any]      					= open_list[0].top_show()
																				visited.add(_SCREAMING_SNAKE_CASE       )
																				expand_state(
																				    _SCREAMING_SNAKE_CASE    ,	0    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	)
																				close_list_anchor.append(_SCREAMING_SNAKE_CASE       )
				print('''No path found to goal'''       )
				print()
				for i in range(n - 1    ,	-1    ,	-1       ):
								for j in range(_SCREAMING_SNAKE_CASE       ):
												if (j, i) in blocks:
																print('''#'''    ,	end=''' '''       )
												elif (j, i) in back_pointer:
																if (j, i) == (n - 1, n - 1):
																				print('''*'''    ,	end=''' '''       )
																else:
																				print('''-'''    ,	end=''' '''       )
												else:
																print('''*'''    ,	end=''' '''       )
												if (j, i) == (n - 1, n - 1):
																print('''<-- End position'''    ,	end=''' '''       )
								print()
				print('''^'''       )
				print('''Start position'''       )
				print()
				print('''# is an obstacle'''       )
				print('''- is the path taken by algorithm'''       )


if __name__ == "__main__":
	multi_a_star(start, goal, n_heuristic)




