






"""simple docstring"""







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            =	logging.get_logger('transformers.models.speecht5')

_lowerCAmelCase            =	{
    '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            =	{
    '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            =	{
    '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            =	{
    '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            =	{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
_lowerCAmelCase            =	{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
_lowerCAmelCase            =	{
    '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            =	{
    '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            =	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_lowerCAmelCase            =	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase            =	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase            =	[]
_lowerCAmelCase            =	[
    '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            =	IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
_lowerCAmelCase            =	IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
_lowerCAmelCase            =	IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A )     ->       List[Any]:
				for attribute in key.split(""".""" ):
								lowercase    :      List[str]       = getattr(_A  ,				_A )

				if weight_type is not None:
								lowercase    :      Any       = getattr(_A  ,				_A ).shape
				else:
								lowercase    :      Optional[Any]       = 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":
								lowercase    :      Optional[int]       = value
				elif weight_type == "weight_g":
								lowercase    :      Optional[Any]       = value
				elif weight_type == "weight_v":
								lowercase    :      Union[str, Any]       = value
				elif weight_type == "bias":
								lowercase    :      Optional[int]       = value
				elif weight_type == "running_mean":
								lowercase    :      Any       = value
				elif weight_type == "running_var":
								lowercase    :      int       = value
				elif weight_type == "num_batches_tracked":
								lowercase    :      Dict       = value
				else:
								lowercase    :      Optional[Any]       = value

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


def      UpperCamelCase       ( _A  ,				_A )     ->       List[Any]:
				for key in ignore_keys:
								if key.endswith(""".*""" ):
												if name.startswith(key[:-1] ):
																return True
								elif ".*." in key:
												lowercase      ,      lowercase    :      Dict       = key.split(""".*.""" )
												if prefix in name and suffix in name:
																return True
								elif key in name:
												return True
				return False


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Any:
				lowercase    :      int       = []

				if task == "s2t":
								lowercase    :      Tuple       = hf_model.speechta.encoder.prenet.feature_encoder
								lowercase    :      List[Any]       = MAPPING_S2T
								lowercase    :      Union[str, Any]       = IGNORE_KEYS_S2T
				elif task == "t2s":
								lowercase    :      Optional[int]       = None
								lowercase    :      str       = MAPPING_T2S
								lowercase    :      Optional[int]       = IGNORE_KEYS_T2S
				elif task == "s2s":
								lowercase    :      Optional[Any]       = hf_model.speechta.encoder.prenet.feature_encoder
								lowercase    :      Optional[int]       = MAPPING_S2S
								lowercase    :      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

								lowercase    :      str       = False
								if "conv_layers" in name:
												load_conv_layer(
												    _A  ,				_A  ,				_A  ,				_A  ,				hf_model.config.feat_extract_norm == """group"""  ,				)
												lowercase    :      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:
																				lowercase      ,      lowercase    :      List[Any]       = key.split(""".*.""" )
																				if prefix in name and suffix in name:
																								lowercase    :      int       = suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																if key in name:
																				lowercase    :      List[Any]       = True
																				if "*" in mapped_key:
																								lowercase    :      Dict       = name.split(_A )[0].split(""".""" )[-2]
																								lowercase    :      Tuple       = mapped_key.replace("""*"""  ,				_A )
																				if "weight_g" in name:
																								lowercase    :      Optional[int]       = """weight_g"""
																				elif "weight_v" in name:
																								lowercase    :      List[Any]       = """weight_v"""
																				elif "bias" in name:
																								lowercase    :      List[str]       = """bias"""
																				elif "weight" in name:
																								lowercase    :      Union[str, Any]       = """weight"""
																				elif "running_mean" in name:
																								lowercase    :      Any       = """running_mean"""
																				elif "running_var" in name:
																								lowercase    :      Optional[Any]       = """running_var"""
																				elif "num_batches_tracked" in name:
																								lowercase    :      List[Any]       = """num_batches_tracked"""
																				else:
																								lowercase    :      Any       = 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      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A )     ->       int:
				lowercase    :      str       = full_name.split("""conv_layers.""" )[-1]
				lowercase    :      List[str]       = name.split(""".""" )
				lowercase    :      List[str]       = int(items[0] )
				lowercase    :      int       = 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.""" )
												lowercase    :      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.""" )
												lowercase    :      Dict       = 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.""" )
												lowercase    :      List[Any]       = 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.""" )
												lowercase    :      int       = 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      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A=None  ,				_A=None  ,				_A=None  ,				)     ->       int:
				if config_path is not None:
								lowercase    :      str       = SpeechTaConfig.from_pretrained(_A )
				else:
								lowercase    :      List[Any]       = SpeechTaConfig()

				if task == "s2t":
								lowercase    :      List[Any]       = config.max_text_positions
								lowercase    :      Optional[int]       = SpeechTaForSpeechToText(_A )
				elif task == "t2s":
								lowercase    :      Dict       = 1_876
								lowercase    :      str       = 600
								lowercase    :      Union[str, Any]       = config.max_speech_positions
								lowercase    :      Tuple       = SpeechTaForTextToSpeech(_A )
				elif task == "s2s":
								lowercase    :      Tuple       = 1_876
								lowercase    :      Dict       = config.max_speech_positions
								lowercase    :      Any       = SpeechTaForSpeechToSpeech(_A )
				else:
								raise ValueError(F"""Unknown task name: {task}""" )

				if vocab_path:
								lowercase    :      List[str]       = SpeechTaTokenizer(_A  ,				model_max_length=config.max_text_positions )

								# Mask token behaves like a normal word, i.e. include the space before it
								lowercase    :      int       = AddedToken("""<mask>"""  ,				lstrip=_A  ,				rstrip=_A )
								lowercase    :      Tuple       = mask_token
								tokenizer.add_special_tokens({"""mask_token""": mask_token} )
								tokenizer.add_tokens(["""<ctc_blank>"""] )

				lowercase    :      Optional[int]       = SpeechTaFeatureExtractor()
				lowercase    :      Optional[int]       = SpeechTaProcessor(tokenizer=_A  ,				feature_extractor=_A )
				processor.save_pretrained(_A )

				lowercase    :      List[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            =	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            =	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,
				)








"""simple docstring"""







import numpy as np


class      UpperCamelCase		:






						def __init__(		self  :List[Any]				)      ->Optional[Any]:
										lowercase    :      Dict       = (0, 0)
										lowercase    :      str       = None
										lowercase    :      Any       = 0
										lowercase    :      List[Any]       = 0
										lowercase    :      Union[str, Any]       = 0






						def __eq__(		self  :List[Any]			, __magic_name__  :Optional[Any]				)      ->Tuple:
										return self.position == cell.position






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


class      UpperCamelCase		:






						def __init__(		self  :Any			, __magic_name__  :int=(5, 5)				)      ->Dict:
										lowercase    :      Tuple       = np.zeros(__magic_name__				)
										lowercase    :      List[str]       = world_size[0]
										lowercase    :      str       = world_size[1]






						def      __snake_case      (		self  :str				)      ->int:
										print(self.w				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Any				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										lowercase    :      List[str]       = cell.position[0]
										lowercase    :      List[str]       = cell.position[1]
										lowercase    :      Optional[int]       = []
										for n in neughbour_cord:
														lowercase    :      List[Any]       = current_x + n[0]
														lowercase    :      List[Any]       = current_y + n[1]
														if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																		lowercase    :      Tuple       = Cell()
																		lowercase    :      Union[str, Any]       = (x, y)
																		lowercase    :      Dict       = cell
																		neighbours.append(__magic_name__				)
										return neighbours


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Tuple:
				lowercase    :      Dict       = []
				lowercase    :      List[str]       = []
				_open.append(_A )

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

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(_A )
				lowercase    :      List[str]       = []
				while current.parent is not None:
								path.append(current.position )
								lowercase    :      int       = 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)
