
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__snake_case					:							int           =							logging.get_logger(__name__)

__snake_case					:							Any           =							{
    '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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			for attribute in key.split('.'							):
						UpperCAmelCase__		:Any 		=    getattr(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							)

			if weight_type is not None:
						UpperCAmelCase__		:Optional[Any] 		=    getattr(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							).shape
			else:
						UpperCAmelCase__		:Dict 		=    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":
						UpperCAmelCase__		:Union[str, Any] 		=    value
			elif weight_type == "weight_g":
						UpperCAmelCase__		:int 		=    value
			elif weight_type == "weight_v":
						UpperCAmelCase__		:Dict 		=    value
			elif weight_type == "bias":
						UpperCAmelCase__		:Tuple 		=    value
			else:
						UpperCAmelCase__		:str 		=    value

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




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Dict 		=    []
			UpperCAmelCase__		:List[str] 		=    fairseq_model.state_dict()

			UpperCAmelCase__		:Any 		=    hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						UpperCAmelCase__		:Dict 		=    False
						if "conv_layers" in name:
									load_conv_layer(
									    SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						hf_model.config.feat_extract_norm == 'group'     ,						)
									UpperCAmelCase__		:Tuple 		=    True
						else:
									for key, mapped_key in MAPPING.items():
												UpperCAmelCase__		:Optional[int] 		=    mapped_key

												if key in name:
															UpperCAmelCase__		:Any 		=    True
															if "*" in mapped_key:
																		UpperCAmelCase__		:Dict 		=    name.split(SCREAMING_SNAKE_CASE							)[0].split('.'							)[-2]
																		UpperCAmelCase__		:Tuple 		=    mapped_key.replace('*'     ,						SCREAMING_SNAKE_CASE							)
															if "weight_g" in name:
																		UpperCAmelCase__		:List[Any] 		=    'weight_g'
															elif "weight_v" in name:
																		UpperCAmelCase__		:Optional[int] 		=    'weight_v'
															elif "weight" in name:
																		UpperCAmelCase__		:Optional[int] 		=    'weight'
															elif "bias" in name:
																		UpperCAmelCase__		:List[Any] 		=    'bias'
															else:
																		UpperCAmelCase__		:Any 		=    None
															set_recursively(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							)
												continue
						if not is_used:
									unused_weights.append(SCREAMING_SNAKE_CASE							)

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




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


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




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    HubertConfig()
			UpperCAmelCase__		:List[str] 		=    model.config

			UpperCAmelCase__		:List[str] 		=    fs_config.activation_dropout
			UpperCAmelCase__		:Any 		=    False
			UpperCAmelCase__		:Union[str, Any] 		=    fs_config.attention_dropout
			UpperCAmelCase__		:Optional[int] 		=    False
			UpperCAmelCase__		:Dict 		=    eval(fs_config.extractor_conv_feature_layers							)
			UpperCAmelCase__		:Optional[Any] 		=    [x[0] for x in conv_layers]
			UpperCAmelCase__		:Optional[Any] 		=    [x[1] for x in conv_layers]
			UpperCAmelCase__		:Union[str, Any] 		=    [x[2] for x in conv_layers]
			UpperCAmelCase__		:Dict 		=    'gelu'
			UpperCAmelCase__		:Optional[int] 		=    'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
			UpperCAmelCase__		:List[Any] 		=    False
			UpperCAmelCase__		:Optional[Any] 		=    0.0
			UpperCAmelCase__		:Tuple 		=    0.0
			UpperCAmelCase__		:Optional[Any] 		=    fs_config.activation_fn
			UpperCAmelCase__		:Union[str, Any] 		=    fs_config.dropout
			UpperCAmelCase__		:List[Any] 		=    fs_config.encoder_embed_dim
			UpperCAmelCase__		:List[Any] 		=    0.02
			UpperCAmelCase__		:str 		=    fs_config.encoder_ffn_embed_dim
			UpperCAmelCase__		:Tuple 		=    1E-5
			UpperCAmelCase__		:str 		=    0.0
			UpperCAmelCase__		:Any 		=    fs_config.encoder_attention_heads
			UpperCAmelCase__		:Tuple 		=    fs_config.conv_pos_groups
			UpperCAmelCase__		:Tuple 		=    fs_config.conv_pos
			UpperCAmelCase__		:Union[str, Any] 		=    len(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Optional[int] 		=    fs_config.encoder_layers

			return config




@torch.no_grad()
def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=None							):




			"""simple docstring"""


			UpperCAmelCase__		:Optional[Any] 		=    distilhubert().model.model

			if config_path is not None:
						UpperCAmelCase__		:Optional[Any] 		=    HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE							)
			else:
						UpperCAmelCase__		:List[str] 		=    convert_config(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:List[str] 		=    model.eval()

			UpperCAmelCase__		:Dict 		=    WavaVecaFeatureExtractor(
			    feature_size=1     ,						sampling_rate=16000     ,						padding_value=0     ,						do_normalize=SCREAMING_SNAKE_CASE     ,						return_attention_mask=SCREAMING_SNAKE_CASE     ,						)
			UpperCAmelCase__		:Dict 		=    HubertModel(SCREAMING_SNAKE_CASE							)

			recursively_load_weights(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							)

			feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE							)
			hf_model.save_pretrained(SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
						__snake_case					:							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')
						__snake_case					:							Union[str, Any]           =							parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







from __future__ import annotations




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						):




			"""simple docstring"""


			if not all((nth_term, start_term_a, common_ratio_r)							):
						return []
			UpperCAmelCase__		:list[float | int] 		=    []
			UpperCAmelCase__		:str 		=    1
			UpperCAmelCase__		:Any 		=    common_ratio_r
			for _ in range(int(SCREAMING_SNAKE_CASE							)							):
						if not series:
									series.append(SCREAMING_SNAKE_CASE							)
						else:
									power += 1
									series.append(float(start_term_a * multiple							)							)
									UpperCAmelCase__		:str 		=    pow(float(SCREAMING_SNAKE_CASE							)     ,						SCREAMING_SNAKE_CASE							)
			return series


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						__snake_case					:							Optional[int]           =							float(input('Enter the last number (n term) of the Geometric Series'))
						__snake_case					:							List[str]           =							float(input('Enter the starting term (a) of the Geometric Series'))
						__snake_case					:							Optional[int]           =							float(
						    input('Enter the common ratio between two terms (r) of the Geometric Series')
						)
						print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
						print(geometric_series(nth_term, start_term_a, common_ratio_r))





