






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

__A          = {
    '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    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''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    __A    (   _lowercase    ,	_lowercase       ):

					'''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    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''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    __A    (   _lowercase       ):

					'''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    __A    (   _lowercase    ,	_lowercase=None       ):

					'''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_60_00    ,	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          = 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          = parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A          = logging.get_logger(__name__)

__A          = {
    'uw-madison/nystromformer-512': 'https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "nystromformer"

							def __init__(     self:      List[str]						,   __A:      Dict=3_00_00						,   __A:      int=7_68						,   __A:      int=12						,   __A:      Union[str, Any]=12						,   __A:      List[str]=30_72						,   __A:      Tuple="gelu_new"						,   __A:      Optional[Any]=0.1						,   __A:      Union[str, Any]=0.1						,   __A:      int=5_10						,   __A:      List[Any]=2						,   __A:      List[Any]=64						,   __A:      Optional[Any]=64						,   __A:      str=65						,   __A:      List[str]=False						,   __A:      Tuple=0.02						,   __A:      Dict=1e-5						,   __A:      int=1						,   __A:      str=0						,   __A:      int=2						,   **__A:      Union[str, Any]						,   )			-> int:
												_A									=       vocab_size
												_A									=       max_position_embeddings
												_A									=       hidden_size
												_A									=       num_hidden_layers
												_A									=       num_attention_heads
												_A									=       intermediate_size
												_A									=       hidden_act
												_A									=       hidden_dropout_prob
												_A									=       attention_probs_dropout_prob
												_A									=       initializer_range
												_A									=       type_vocab_size
												_A									=       segment_means_seq_len
												_A									=       num_landmarks
												_A									=       conv_kernel_size
												_A									=       inv_coeff_init_option
												_A									=       layer_norm_eps
												super().__init__(pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   **__A      )



