class 			__snake_case  :




					def __init__(       self		)					->  None:



									'''simple docstring'''







									snake_case__ :     dict[int, list[int]] 							=				{}
									# indexmap keys are an index and it's values are the number at that index
									snake_case__ :     dict[int, int] 							=				{}




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  list[int]:



									'''simple docstring'''







									if isinstance(__UpperCamelCase   ,	(range, str)		):
													snake_case__ :     List[str] 							=				list(__UpperCamelCase		)
									elif not isinstance(__UpperCamelCase   ,	__UpperCamelCase		):
													raise TypeError(
													    'binary_search_delete() only accepts either a list, range or str'		)

									snake_case__ :     int 							=				0
									snake_case__ :     Any 							=				len(__UpperCamelCase		) - 1

									while low <= high:
													snake_case__ :     List[Any] 							=				(low + high) // 2
													if array[mid] == item:
																	array.pop(__UpperCamelCase		)
																	return array
													elif array[mid] < item:
																	snake_case__ :     Union[str, Any] 							=				mid + 1
													else:
																	snake_case__ :     Optional[Any] 							=				mid - 1
									raise ValueError(
									    'Either the item is not in the array or the array was unsorted'		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  list[int]:



									'''simple docstring'''







									if isinstance(__UpperCamelCase   ,	(range, str)		):
													snake_case__ :     Tuple 							=				list(__UpperCamelCase		)
									elif not isinstance(__UpperCamelCase   ,	__UpperCamelCase		):
													raise TypeError(
													    'binary_search_insert() only accepts either a list, range or str'		)

									snake_case__ :     Dict 							=				0
									snake_case__ :     int 							=				len(__UpperCamelCase		) - 1

									while low <= high:
													snake_case__ :     Any 							=				(low + high) // 2
													if array[mid] == index:
																	# If the item already exists in the array,
																	# insert it after the existing item
																	array.insert(mid + 1   ,	__UpperCamelCase		)
																	return array
													elif array[mid] < index:
																	snake_case__ :     List[Any] 							=				mid + 1
													else:
																	snake_case__ :     List[str] 							=				mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
									array.insert(__UpperCamelCase   ,	__UpperCamelCase		)
									return array




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  None:



									'''simple docstring'''







									if index in self.indexmap:
													snake_case__ :     List[Any] 							=				self.indexmap[index]
													if len(self.numbermap[n]		) == 1:
																	del self.numbermap[n]
													else:
																	snake_case__ :     Tuple 							=				self.binary_search_delete(self.numbermap[n]   ,	__UpperCamelCase		)

        # Set new index
									snake_case__ :     List[Any] 							=				number

									# Number not seen before or empty so insert number value
									if number not in self.numbermap:
													snake_case__ :     int 							=				[index]

									# Here we need to perform a binary search insertion in order to insert
									# The item in the correct place
									else:
													snake_case__ :     Tuple 							=				self.binary_search_insert(
													    self.numbermap[number]   ,	__UpperCamelCase		)




					def       __a      (       self   ,	__UpperCamelCase		)					->  int:



									'''simple docstring'''







									return self.numbermap.get(__UpperCamelCase   ,	[-1]		)[0]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase__					:       Any		=       logging.get_logger(__name__)

lowerCAmelCase__					:       int		=       {
    '''post_extract_proj''': '''feature_projection.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.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__      ,			A__   )       -> Optional[int]:
				for attribute in key.split('.'   ):
								snake_case__ :     Optional[Any] 							=				getattr(A__      ,			A__   )

				if weight_type is not None:
								snake_case__ :     Any 							=				getattr(A__      ,			A__   ).shape
				else:
								snake_case__ :     str 							=				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":
								snake_case__ :     List[str] 							=				value
				elif weight_type == "weight_g":
								snake_case__ :     str 							=				value
				elif weight_type == "weight_v":
								snake_case__ :     List[str] 							=				value
				elif weight_type == "bias":
								snake_case__ :     List[Any] 							=				value
				else:
								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 			UpperCamelCase__						(    A__      ,			A__   )       -> List[str]:
				snake_case__ :     Tuple 							=				[]
				snake_case__ :     int 							=				fairseq_model.state_dict()

				snake_case__ :     int 							=				hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								snake_case__ :     Union[str, Any] 							=				False
								if "conv_layers" in name:
												load_conv_layer(
												    A__      ,			A__      ,			A__      ,			A__      ,			hf_model.config.feat_extract_norm == 'group'      ,			)
												snake_case__ :     Tuple 							=				True
								else:
												for key, mapped_key in MAPPING.items():
																snake_case__ :     Dict 							=				mapped_key

																if key in name:
																				snake_case__ :     int 							=				True
																				if "*" in mapped_key:
																								snake_case__ :     List[str] 							=				name.split(A__   )[0].split('.'   )[-2]
																								snake_case__ :     Dict 							=				mapped_key.replace('*'      ,			A__   )
																				if "weight_g" in name:
																								snake_case__ :     int 							=				'weight_g'
																				elif "weight_v" in name:
																								snake_case__ :     int 							=				'weight_v'
																				elif "weight" in name:
																								snake_case__ :     Any 							=				'weight'
																				elif "bias" in name:
																								snake_case__ :     Tuple 							=				'bias'
																				else:
																								snake_case__ :     int 							=				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__   )       -> Tuple:
				snake_case__ :     Optional[int] 							=				full_name.split('conv_layers.'   )[-1]
				snake_case__ :     Dict 							=				name.split('.'   )
				snake_case__ :     str 							=				int(items[0]   )
				snake_case__ :     Optional[Any] 							=				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."""
												)
												snake_case__ :     List[Any] 							=				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."""
												)
												snake_case__ :     Optional[int] 							=				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."
												)
												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:
												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."""
												)
												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(A__   )
def 			UpperCamelCase__						(    A__   )       -> Union[str, Any]:
				snake_case__ :     Optional[Any] 							=				HubertConfig()
				snake_case__ :     Dict 							=				model.config

				snake_case__ :     Optional[Any] 							=				fs_config.activation_dropout
				snake_case__ :     Union[str, Any] 							=				False
				snake_case__ :     Tuple 							=				fs_config.attention_dropout
				snake_case__ :     List[str] 							=				False
				snake_case__ :     Optional[int] 							=				eval(fs_config.extractor_conv_feature_layers   )
				snake_case__ :     Optional[int] 							=				[x[0] for x in conv_layers]
				snake_case__ :     Union[str, Any] 							=				[x[1] for x in conv_layers]
				snake_case__ :     Dict 							=				[x[2] for x in conv_layers]
				snake_case__ :     int 							=				'gelu'
				snake_case__ :     Dict 							=				'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
				snake_case__ :     Any 							=				False
				snake_case__ :     List[Any] 							=				0.0
				snake_case__ :     Any 							=				0.0
				snake_case__ :     Union[str, Any] 							=				fs_config.activation_fn
				snake_case__ :     List[str] 							=				fs_config.dropout
				snake_case__ :     Dict 							=				fs_config.encoder_embed_dim
				snake_case__ :     Any 							=				0.0_2
				snake_case__ :     Union[str, Any] 							=				fs_config.encoder_ffn_embed_dim
				snake_case__ :     List[Any] 							=				1e-5
				snake_case__ :     Union[str, Any] 							=				0.0
				snake_case__ :     int 							=				fs_config.encoder_attention_heads
				snake_case__ :     Any 							=				fs_config.conv_pos_groups
				snake_case__ :     int 							=				fs_config.conv_pos
				snake_case__ :     Any 							=				len(A__   )
				snake_case__ :     str 							=				fs_config.encoder_layers

				return config
@torch.no_grad()
def 			UpperCamelCase__						(    A__      ,			A__=None   )       -> str:
				snake_case__ :     int 							=				distilhubert().model.model

				if config_path is not None:
								snake_case__ :     Optional[int] 							=				HubertConfig.from_pretrained(A__   )
				else:
								snake_case__ :     int 							=				convert_config(A__   )
				snake_case__ :     Dict 							=				model.eval()

				snake_case__ :     int 							=				WavaVecaFeatureExtractor(
				    feature_size=1      ,			sampling_rate=1_6000      ,			padding_value=0      ,			do_normalize=A__      ,			return_attention_mask=A__      ,			)
				snake_case__ :     Optional[int] 							=				HubertModel(A__   )

				recursively_load_weights(A__      ,			A__   )

				feature_extractor.save_pretrained(A__   )
				hf_model.save_pretrained(A__   )


if __name__ == "__main__":
				lowerCAmelCase__					:       Union[str, Any]		=       argparse.ArgumentParser()
				parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
				lowerCAmelCase__					:       List[Any]		=       parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


