def 			UpperCamelCase__						(    A__   )       -> bool:
				# must be
				snake_case__ :     Optional[int] 							=				[list(A__   ) for row in matrix]
				if matrix and isinstance(A__      ,			A__   ):
								if isinstance(matrix[0]      ,			A__   ):
												snake_case__ :     Union[str, Any] 							=				0
												for row in matrix:
																if prev_len == 0:
																				snake_case__ :     Dict 							=				len(A__   )
																				snake_case__ :     List[Any] 							=				True
																else:
																				snake_case__ :     int 							=				prev_len == len(A__   )
								else:
												snake_case__ :     Optional[Any] 							=				True
				else:
								snake_case__ :     Optional[Any] 							=				False

				return result
def 			UpperCamelCase__						(    A__   )       -> None:
				if check_matrix(A__   ) and len(A__   ) > 0:
								snake_case__ :     List[str] 							=				[list(A__   ) for row in a]
								snake_case__ :     Tuple 							=				len(A__   )
								if isinstance(a[0]      ,			A__   ):
												snake_case__ :     Tuple 							=				len(a[0]   )
								else:
												for dat in a:
																print(A__   )
												return

								# horizotal printing increasing
								for i in range(0      ,			A__   ):
												print(a[0][i]   )
								# vertical printing down
								for i in range(1      ,			A__   ):
												print(a[i][mat_col - 1]   )
								# horizotal printing decreasing
								if mat_row > 1:
												for i in range(mat_col - 2      ,			-1      ,			-1   ):
																print(a[mat_row - 1][i]   )
        # vertical printing up
								for i in range(mat_row - 2      ,			0      ,			-1   ):
												print(a[i][0]   )
								snake_case__ :     List[Any] 							=				[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
								if len(A__   ) > 0:
												spiral_print_clockwise(A__   )
								else:
												return
				else:
								print('Not a valid matrix'   )
								return
def 			UpperCamelCase__						(    A__   )       -> list[int]:
				if matrix:
								return list(matrix.pop(0   )   ) + spiral_traversal(list(zip(*A__   )   )[::-1]   )
				else:
								return []


# driver code
if __name__ == "__main__":
				import doctest

				doctest.testmod()

				lowerCAmelCase__					:       List[Any]		=       [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
				spiral_print_clockwise(a)



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)


