


'''simple docstring'''


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCamelCase__     						=  logging.get_logger(__name__)

lowerCamelCase__     						=  {
    '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 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		for attribute in key.split("." ):
				_UpperCAmelCase		:							int             =   getattr(__lowerCAmelCase   ,			__lowerCAmelCase )

		if weight_type is not None:
				_UpperCAmelCase		:							List[Any]             =   getattr(__lowerCAmelCase   ,			__lowerCAmelCase ).shape
		else:
				_UpperCAmelCase		:							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":
				_UpperCAmelCase		:							str             =   value
		elif weight_type == "weight_g":
				_UpperCAmelCase		:							List[Any]             =   value
		elif weight_type == "weight_v":
				_UpperCAmelCase		:							Tuple             =   value
		elif weight_type == "bias":
				_UpperCAmelCase		:							Union[str, Any]             =   value
		else:
				_UpperCAmelCase		:							Union[str, Any]             =   value

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






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							Tuple             =   []
		_UpperCAmelCase		:							int             =   fairseq_model.state_dict()

		_UpperCAmelCase		:							Optional[int]             =   hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				_UpperCAmelCase		:							Tuple             =   False
				if "conv_layers" in name:
						load_conv_layer(
						    __lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			hf_model.config.feat_extract_norm == "group"   ,			)
						_UpperCAmelCase		:							str             =   True
				else:
						for key, mapped_key in MAPPING.items():
								_UpperCAmelCase		:							Any             =   mapped_key

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

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






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							Dict             =   full_name.split("conv_layers." )[-1]
		_UpperCAmelCase		:							str             =   name.split("." )
		_UpperCAmelCase		:							Any             =   int(items[0] )
		_UpperCAmelCase		:							Union[str, 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		:							Optional[int]             =   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		:							Union[str, Any]             =   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		:							Dict             =   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		:							List[str]             =   value
						logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
		else:
				unused_weights.append(__lowerCAmelCase )






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							int             =   HubertConfig()
		_UpperCAmelCase		:							Any             =   model.config

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

		return config






@torch.no_grad()
def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase=None ):
		_UpperCAmelCase		:							List[Any]             =   distilhubert().model.model

		if config_path is not None:
				_UpperCAmelCase		:							Tuple             =   HubertConfig.from_pretrained(__lowerCAmelCase )
		else:
				_UpperCAmelCase		:							List[Any]             =   convert_config(__lowerCAmelCase )
		_UpperCAmelCase		:							Any             =   model.eval()

		_UpperCAmelCase		:							Optional[Any]             =   WavaVecaFeatureExtractor(
		    feature_size=1   ,			sampling_rate=16_000   ,			padding_value=0   ,			do_normalize=__lowerCAmelCase   ,			return_attention_mask=__lowerCAmelCase   ,			)
		_UpperCAmelCase		:							Tuple             =   HubertModel(__lowerCAmelCase )

		recursively_load_weights(__lowerCAmelCase   ,			__lowerCAmelCase )

		feature_extractor.save_pretrained(__lowerCAmelCase )
		hf_model.save_pretrained(__lowerCAmelCase )


if __name__ == "__main__":
						lowerCamelCase__     						=  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__     						=  parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''


from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    'cross_attention',
    '0.20.0',
    'Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.',
    standard_warn=False,
)


lowerCamelCase__     						=  AttentionProcessor






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Union[str, Any]						,	*lowerCamelCase__	:  int						,	**lowerCamelCase__	:  List[str]			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Optional[Any]						,	*lowerCamelCase__	:  Any						,	**lowerCamelCase__	:  Tuple			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  List[Any]						,	*lowerCamelCase__	:  List[Any]						,	**lowerCamelCase__	:  List[str]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  int						,	*lowerCamelCase__	:  Tuple						,	**lowerCamelCase__	:  Union[str, Any]			) ->int:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  int						,	*lowerCamelCase__	:  List[str]						,	**lowerCamelCase__	:  Tuple			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[Any]             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  List[Any]						,	*lowerCamelCase__	:  int						,	**lowerCamelCase__	:  Optional[int]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Any						,	*lowerCamelCase__	:  Optional[Any]						,	**lowerCamelCase__	:  Union[str, Any]			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Optional[Any]						,	*lowerCamelCase__	:  List[Any]						,	**lowerCamelCase__	:  Dict			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)



