


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowerCAmelCase__			:      List[str]								=logging.get_logger('transformers.models.speecht5')

lowerCAmelCase__			:      Union[str, Any]								={
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
lowerCAmelCase__			:      Tuple								={
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
lowerCAmelCase__			:      Dict								={
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
lowerCAmelCase__			:      Union[str, Any]								={
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
lowerCAmelCase__			:      int								={
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
lowerCAmelCase__			:      Any								={
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
lowerCAmelCase__			:      Optional[Any]								={
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
lowerCAmelCase__			:      List[str]								={
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
lowerCAmelCase__			:      List[str]								={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCAmelCase__			:      List[Any]								={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase__			:      Tuple								={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase__			:      int								=[]
lowerCAmelCase__			:      str								=[
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
lowerCAmelCase__			:      Any								=IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
lowerCAmelCase__			:      List[Any]								=IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
lowerCAmelCase__			:      Tuple								=IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]






def 						a__			(				A__,      A__,      A__,      A__,      A__				):
			for attribute in key.split('.'				):
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					getattr(A__,      A__				)

			if weight_type is not None:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					getattr(A__,      A__				).shape
			else:
						SCREAMING_SNAKE_CASE_		:     Dict					=					hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    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_		:     Dict					=					value
			elif weight_type == "weight_g":
						SCREAMING_SNAKE_CASE_		:     str					=					value
			elif weight_type == "weight_v":
						SCREAMING_SNAKE_CASE_		:     str					=					value
			elif weight_type == "bias":
						SCREAMING_SNAKE_CASE_		:     Dict					=					value
			elif weight_type == "running_mean":
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					value
			elif weight_type == "running_var":
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					value
			elif weight_type == "num_batches_tracked":
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					value
			else:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					value

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






def 						a__			(				A__,      A__				):
			for key in ignore_keys:
						if key.endswith('.*'				):
									if name.startswith(key[:-1]				):
												return True
						elif ".*." in key:
									SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					key.split('.*.'				)
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[]

			if task == "s2t":
						SCREAMING_SNAKE_CASE_		:     str					=					hf_model.speechta.encoder.prenet.feature_encoder
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					MAPPING_S2T
						SCREAMING_SNAKE_CASE_		:     int					=					IGNORE_KEYS_S2T
			elif task == "t2s":
						SCREAMING_SNAKE_CASE_		:     int					=					None
						SCREAMING_SNAKE_CASE_		:     Dict					=					MAPPING_T2S
						SCREAMING_SNAKE_CASE_		:     Any					=					IGNORE_KEYS_T2S
			elif task == "s2s":
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hf_model.speechta.encoder.prenet.feature_encoder
						SCREAMING_SNAKE_CASE_		:     List[str]					=					MAPPING_S2S
						SCREAMING_SNAKE_CASE_		:     int					=					IGNORE_KEYS_S2S
			else:
						raise ValueError(F'''Unsupported task: {task}'''				)

			for name, value in fairseq_dict.items():
						if should_ignore(A__,      A__				):
									logger.info(F'''{name} was ignored'''				)
									continue

						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					False
						if "conv_layers" in name:
									load_conv_layer(
									    A__,      A__,      A__,      A__,      hf_model.config.feat_extract_norm == 'group',      )
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					True
						else:
									for key, mapped_key in MAPPING.items():
												# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

												if "*" in key:
															SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Tuple					=					key.split('.*.'				)
															if prefix in name and suffix in name:
																		SCREAMING_SNAKE_CASE_		:     Any					=					suffix

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

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






def 						a__			(				A__,      A__,      A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					full_name.split('conv_layers.'				)[-1]
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					name.split('.'				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					int(items[0]				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					int(items[1]				)

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''				)
									SCREAMING_SNAKE_CASE_		:     List[Any]					=					value
									logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''				)
									SCREAMING_SNAKE_CASE_		:     Tuple					=					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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''				)
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''				)
									SCREAMING_SNAKE_CASE_		:     List[str]					=					value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
			else:
						unused_weights.append(A__				)






@torch.no_grad()
def 						a__			(				A__,      A__,      A__,      A__=None,      A__=None,      A__=None,      ):
			if config_path is not None:
						SCREAMING_SNAKE_CASE_		:     Any					=					SpeechTaConfig.from_pretrained(A__				)
			else:
						SCREAMING_SNAKE_CASE_		:     int					=					SpeechTaConfig()

			if task == "s2t":
						SCREAMING_SNAKE_CASE_		:     List[str]					=					config.max_text_positions
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					SpeechTaForSpeechToText(A__				)
			elif task == "t2s":
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					1_8_7_6
						SCREAMING_SNAKE_CASE_		:     int					=					6_0_0
						SCREAMING_SNAKE_CASE_		:     int					=					config.max_speech_positions
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					SpeechTaForTextToSpeech(A__				)
			elif task == "s2s":
						SCREAMING_SNAKE_CASE_		:     int					=					1_8_7_6
						SCREAMING_SNAKE_CASE_		:     List[str]					=					config.max_speech_positions
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					SpeechTaForSpeechToSpeech(A__				)
			else:
						raise ValueError(F'''Unknown task name: {task}'''				)

			if vocab_path:
						SCREAMING_SNAKE_CASE_		:     Dict					=					SpeechTaTokenizer(A__,      model_max_length=config.max_text_positions				)

						# Mask token behaves like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					AddedToken('<mask>',      lstrip=A__,      rstrip=A__				)
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					mask_token
						tokenizer.add_special_tokens({'mask_token': mask_token}				)
						tokenizer.add_tokens(['<ctc_blank>']				)

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					SpeechTaFeatureExtractor()
			SCREAMING_SNAKE_CASE_		:     Tuple					=					SpeechTaProcessor(tokenizer=A__,      feature_extractor=A__				)
			processor.save_pretrained(A__				)

			SCREAMING_SNAKE_CASE_		:     Any					=					torch.load(A__				)
			recursively_load_weights(fairseq_checkpoint['model'],      A__,      A__				)

			model.save_pretrained(A__				)

			if repo_id:
						print('Pushing to the hub...'				)
						processor.push_to_hub(A__				)
						model.push_to_hub(A__				)


if __name__ == "__main__":
				lowerCAmelCase__			:      Union[str, Any]								=argparse.ArgumentParser()
				parser.add_argument(
				    '--task',
				    default='s2t',
				    type=str,
				    help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
				)
				parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
				parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				parser.add_argument(
				    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
				)

				lowerCAmelCase__			:      Any								=parser.parse_args()
				convert_speechta_checkpoint(
				    args.task,
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.vocab_path,
				    args.push_to_hub,
				)









from __future__ import annotations

lowerCAmelCase__			:      Union[str, Any]								=[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def 						a__			(				A__,      A__,      A__,      A__,      A__,      ):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[
			    [0 for col in range(len(grid[0]				)				)] for row in range(len(A__				)				)
			]  # the reference grid
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					1
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[
			    [0 for col in range(len(grid[0]				)				)] for row in range(len(A__				)				)
			]  # the action grid

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					init[0]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					init[1]
			SCREAMING_SNAKE_CASE_		:     Dict					=					0
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					g + heuristic[x][y]  # cost from starting cell to destination cell
			SCREAMING_SNAKE_CASE_		:     List[str]					=					[[f, g, x, y]]

			SCREAMING_SNAKE_CASE_		:     Dict					=					False  # flag that is set when search is complete
			SCREAMING_SNAKE_CASE_		:     Tuple					=					False  # flag set if we can't find expand

			while not found and not resign:
						if len(A__				) == 0:
									raise ValueError('Algorithm is unable to find solution'				)
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									SCREAMING_SNAKE_CASE_		:     Any					=					cell.pop()
									SCREAMING_SNAKE_CASE_		:     Any					=					next_cell[2]
									SCREAMING_SNAKE_CASE_		:     str					=					next_cell[3]
									SCREAMING_SNAKE_CASE_		:     Tuple					=					next_cell[1]

									if x == goal[0] and y == goal[1]:
												SCREAMING_SNAKE_CASE_		:     Dict					=					True
									else:
												for i in range(len(A__				)				):  # to try out different valid actions
															SCREAMING_SNAKE_CASE_		:     Optional[int]					=					x + DIRECTIONS[i][0]
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(A__				) and ya >= 0 and ya < len(grid[0]				):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					SCREAMING_SNAKE_CASE_		:     List[Any]					=					g + cost
																					SCREAMING_SNAKE_CASE_		:     List[Any]					=					ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya]				)
																					SCREAMING_SNAKE_CASE_		:     int					=					1
																					SCREAMING_SNAKE_CASE_		:     int					=					i
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					goal[0]
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					goal[1]
			invpath.append([x, y]				)  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						SCREAMING_SNAKE_CASE_		:     Any					=					x - DIRECTIONS[action[x][y]][0]
						SCREAMING_SNAKE_CASE_		:     int					=					y - DIRECTIONS[action[x][y]][1]
						SCREAMING_SNAKE_CASE_		:     int					=					xa
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					ya
						invpath.append([x, y]				)

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]
			for i in range(len(A__				)				):
						path.append(invpath[len(A__				) - 1 - i]				)
			return path, action


if __name__ == "__main__":
				lowerCAmelCase__			:      Union[str, Any]								=[
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 1, 0],
				    [0, 0, 0, 0, 1, 0],
				]

				lowerCAmelCase__			:      Any								=[0, 0]
				# all coordinates are given in format [y,x]
				lowerCAmelCase__			:      List[str]								=[len(grid) - 1, len(grid[0]) - 1]
				lowerCAmelCase__			:      str								=1

				# the cost map which pushes the path closer to the goal
				lowerCAmelCase__			:      Union[str, Any]								=[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
				for i in range(len(grid)):
								for j in range(len(grid[0])):
												lowerCAmelCase__			:      List[str]								=abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																lowerCAmelCase__			:      List[str]								=99

				lowerCAmelCase__							,lowerCAmelCase__			:      Any								=search(grid, init, goal, cost, heuristic)

				print('ACTION MAP')
				for i in range(len(action)):
								print(action[i])

				for i in range(len(path)):
								print(path[i])





