






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__a :    Optional[int]								=				logging.get_logger(__name__)

__a :    Optional[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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     str     ,			__lowercase				:     Optional[Any]     ,			__lowercase				:     Dict     ,			__lowercase				:     int     ,			__lowercase				:     Dict      )  -> Optional[int]:





				"""simple docstring"""

				for attribute in key.split("""."""      ):
								__A             =		getattr(__lowercase     ,			__lowercase      )

				if weight_type is not None:
								__A             =		getattr(__lowercase     ,			__lowercase      ).shape
				else:
								__A             =		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":
								__A             =		value
				elif weight_type == "weight_g":
								__A             =		value
				elif weight_type == "weight_v":
								__A             =		value
				elif weight_type == "bias":
								__A             =		value
				else:
								__A             =		value

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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     str     ,			__lowercase				:     Any      )  -> int:





				"""simple docstring"""

				__A             =		[]
				__A             =		fairseq_model.state_dict()

				__A             =		hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								__A             =		False
								if "conv_layers" in name:
												load_conv_layer(
												    __lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			hf_model.config.feat_extract_norm == """group"""     ,			)
												__A             =		True
								else:
												for key, mapped_key in MAPPING.items():
																__A             =		mapped_key

																if key in name:
																				__A             =		True
																				if "*" in mapped_key:
																								__A             =		name.split(__lowercase      )[0].split("""."""      )[-2]
																								__A             =		mapped_key.replace("""*"""     ,			__lowercase      )
																				if "weight_g" in name:
																								__A             =		"""weight_g"""
																				elif "weight_v" in name:
																								__A             =		"""weight_v"""
																				elif "weight" in name:
																								__A             =		"""weight"""
																				elif "bias" in name:
																								__A             =		"""bias"""
																				else:
																								__A             =		None
																				set_recursively(__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase      )
																continue
								if not is_used:
												unused_weights.append(__lowercase      )

				logger.warning(f"Unused weights: {unused_weights}"      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Union[str, Any]     ,			__lowercase				:     Optional[int]     ,			__lowercase				:     Optional[int]     ,			__lowercase				:     Tuple     ,			__lowercase				:     str      )  -> Tuple:





				"""simple docstring"""

				__A             =		full_name.split("""conv_layers."""      )[-1]
				__A             =		name.split("""."""      )
				__A             =		int(items[0]      )
				__A             =		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."
												)
												__A             =		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."
												)
												__A             =		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."
												)
												__A             =		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."
												)
												__A             =		value
												logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
				else:
								unused_weights.append(__lowercase      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]      )  -> Optional[int]:





				"""simple docstring"""

				__A             =		HubertConfig()
				__A             =		model.config

				__A             =		fs_config.activation_dropout
				__A             =		False
				__A             =		fs_config.attention_dropout
				__A             =		False
				__A             =		eval(fs_config.extractor_conv_feature_layers      )
				__A             =		[x[0] for x in conv_layers]
				__A             =		[x[1] for x in conv_layers]
				__A             =		[x[2] for x in conv_layers]
				__A             =		"""gelu"""
				__A             =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
				__A             =		False
				__A             =		0.0
				__A             =		0.0
				__A             =		fs_config.activation_fn
				__A             =		fs_config.dropout
				__A             =		fs_config.encoder_embed_dim
				__A             =		0.02
				__A             =		fs_config.encoder_ffn_embed_dim
				__A             =		1E-5
				__A             =		0.0
				__A             =		fs_config.encoder_attention_heads
				__A             =		fs_config.conv_pos_groups
				__A             =		fs_config.conv_pos
				__A             =		len(__lowercase      )
				__A             =		fs_config.encoder_layers

				return config



@torch.no_grad()
def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Any     ,			__lowercase				:     List[Any]=None      )  -> Optional[int]:





				"""simple docstring"""

				__A             =		distilhubert().model.model

				if config_path is not None:
								__A             =		HubertConfig.from_pretrained(__lowercase      )
				else:
								__A             =		convert_config(__lowercase      )
				__A             =		model.eval()

				__A             =		WavaVecaFeatureExtractor(
				    feature_size=1     ,			sampling_rate=1_6_0_0_0     ,			padding_value=0     ,			do_normalize=__lowercase     ,			return_attention_mask=__lowercase     ,			)
				__A             =		HubertModel(__lowercase      )

				recursively_load_weights(__lowercase     ,			__lowercase      )

				feature_extractor.save_pretrained(__lowercase      )
				hf_model.save_pretrained(__lowercase      )


if __name__ == "__main__":
		__a :    Dict								=				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")
		__a :    Optional[Any]								=				parser.parse_args()
		convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int     ,			__lowercase				:     int      )  -> int:





    """simple docstring"""

    __A             =		1.0
    for i in range(1     ,			r + 1      ):
        ret *= (n + 1 - i) / i
    return round(__lowercase      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int      )  -> int:





    """simple docstring"""

    return choose(8 + n     ,			__lowercase      ) + choose(9 + n     ,			__lowercase      ) - 1_0



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int      )  -> int:





    """simple docstring"""

    return sum(non_bouncy_exact(__lowercase      ) for i in range(1     ,			n + 1      )      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int = 1_0_0      )  -> int:





    """simple docstring"""

    return non_bouncy_upto(__lowercase      )


if __name__ == "__main__":
  print(f"""{solution() = }""")




