



"""simple docstring"""


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

snake_case_								=			{
    """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 (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		):
						for attribute in key.split('.'		):
												UpperCAmelCase			 =					getattr(lowercase_						,      lowercase_		)

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

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

def 					_lowerCAmelCase (  lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					[]
						UpperCAmelCase			 =					fairseq_model.state_dict()

						UpperCAmelCase			 =					hf_model.feature_extractor

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

																								if key in name:
																														UpperCAmelCase			 =					True
																														if "*" in mapped_key:
																																				UpperCAmelCase			 =					name.split(lowercase_		)[0].split('.'		)[-2]
																																				UpperCAmelCase			 =					mapped_key.replace('*'						,      lowercase_		)
																														if "weight_g" in name:
																																				UpperCAmelCase			 =					'weight_g'
																														elif "weight_v" in name:
																																				UpperCAmelCase			 =					'weight_v'
																														elif "weight" in name:
																																				UpperCAmelCase			 =					'weight'
																														elif "bias" in name:
																																				UpperCAmelCase			 =					'bias'
																														else:
																																				UpperCAmelCase			 =					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 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					full_name.split('conv_layers.'		)[-1]
						UpperCAmelCase			 =					name.split('.'		)
						UpperCAmelCase			 =					int(items[0]		)
						UpperCAmelCase			 =					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			 =					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			 =					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			 =					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			 =					value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
						else:
												unused_weights.append(lowercase_		)

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					HubertConfig()
						UpperCAmelCase			 =					model.config

						UpperCAmelCase			 =					fs_config.activation_dropout
						UpperCAmelCase			 =					False
						UpperCAmelCase			 =					fs_config.attention_dropout
						UpperCAmelCase			 =					False
						UpperCAmelCase			 =					eval(fs_config.extractor_conv_feature_layers		)
						UpperCAmelCase			 =					[x[0] for x in conv_layers]
						UpperCAmelCase			 =					[x[1] for x in conv_layers]
						UpperCAmelCase			 =					[x[2] for x in conv_layers]
						UpperCAmelCase			 =					'gelu'
						UpperCAmelCase			 =					'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
						UpperCAmelCase			 =					False
						UpperCAmelCase			 =					0.0
						UpperCAmelCase			 =					0.0
						UpperCAmelCase			 =					fs_config.activation_fn
						UpperCAmelCase			 =					fs_config.dropout
						UpperCAmelCase			 =					fs_config.encoder_embed_dim
						UpperCAmelCase			 =					0.0_2
						UpperCAmelCase			 =					fs_config.encoder_ffn_embed_dim
						UpperCAmelCase			 =					1e-5
						UpperCAmelCase			 =					0.0
						UpperCAmelCase			 =					fs_config.encoder_attention_heads
						UpperCAmelCase			 =					fs_config.conv_pos_groups
						UpperCAmelCase			 =					fs_config.conv_pos
						UpperCAmelCase			 =					len(lowercase_		)
						UpperCAmelCase			 =					fs_config.encoder_layers

						return config

@torch.no_grad()
def 					_lowerCAmelCase (  lowercase_						,      lowercase_=None		):
						UpperCAmelCase			 =					distilhubert().model.model

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

						UpperCAmelCase			 =					WavaVecaFeatureExtractor(
						    feature_size=1						,      sampling_rate=16000						,      padding_value=0						,      do_normalize=lowercase_						,      return_attention_mask=lowercase_						,      )
						UpperCAmelCase			 =					HubertModel(lowercase_		)

						recursively_load_weights(lowercase_						,      lowercase_		)

						feature_extractor.save_pretrained(lowercase_		)
						hf_model.save_pretrained(lowercase_		)


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











import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


SCREAMING_SNAKE_CASE_    :       Tuple       =							logging.get_logger(__name__)







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



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






         """simple docstring"""
         super().__init__()
         if version.parse(torch.__version__     ) < version.parse("""1.12.0"""     ):
             raise ImportError(
                 f"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
                 """PytorchGELUTanh. Please upgrade torch."""     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return nn.functional.gelu(A    ,					approximate="""tanh"""     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi     ) * (input + 0.0_4_4_7_1_5 * torch.pow(A    ,					3.0     ))     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Optional[Any]    ,					A			:							bool = False     )      ->      Optional[Any]:






         """simple docstring"""
         super().__init__()
         if use_gelu_python:
             a__  			=							self._gelu_python
         else:
             a__  			=							nn.functional.gelu

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0     )     ))






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return self.act(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return 0.5 * input * (1.0 + torch.tanh(input * 0.7_9_7_8_8_4_5_6_0_8 * (1.0 + 0.0_4_4_7_1_5 * input * input)     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input * torch.sigmoid(1.7_0_2 * input     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Tuple    ,					A			:							float    ,					A			:							float     )      ->      Optional[Any]:






         """simple docstring"""
         if min > max:
             raise ValueError(f"""min should be < max (got min: {min}, max: {max})"""     )

         super().__init__()
         a__  			=							min
         a__  			=							max






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return torch.clip(gelu(A     )    ,					self.min    ,					self.max     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Tuple     )      ->      List[Any]:






         """simple docstring"""
         super().__init__()
         a__  			=							math.sqrt(2 / math.pi     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(A    ,					3     ))     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return nn.functional.silu(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Optional[Any]     )      ->      List[str]:






         """simple docstring"""
         super().__init__()
         if version.parse(torch.__version__     ) < version.parse("""1.9.0"""     ):
             a__  			=							self._mish_python
         else:
             a__  			=							nn.functional.mish

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input * torch.tanh(nn.functional.softplus(A     )     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return self.act(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							str    ,					A			:							Dict=0.7_0_7_1_0_7    ,					A			:							Union[str, Any]=0.2_8_2_0_9_5     )      ->      int:






         """simple docstring"""
         a__  			=							(input - mu).div(sigma * math.sqrt(2.0     )     )
         return 0.5 * (1.0 + torch.erf(A     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]    ,					A			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							nn.functional.relu(A     )
         a__  			=							torch.square(A     )
         return squared







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     def __getitem__(   self			:							List[str]    ,					A			:							int     )      ->      Any:






         """simple docstring"""
         a__  			=							super().__getitem__(A     )
         a__			,		a__  			=							content if isinstance(A    ,					A     ) else (content, {})
         return cls(**A     )






SCREAMING_SNAKE_CASE_    :       List[Any]       =							{
    '''gelu''': GELUActivation,
    '''gelu_10''': (ClippedGELUActivation, {'''min''': -10, '''max''': 10}),
    '''gelu_fast''': FastGELUActivation,
    '''gelu_new''': NewGELUActivation,
    '''gelu_python''': (GELUActivation, {'''use_gelu_python''': True}),
    '''gelu_pytorch_tanh''': PytorchGELUTanh,
    '''gelu_accurate''': AccurateGELUActivation,
    '''laplace''': LaplaceActivation,
    '''linear''': LinearActivation,
    '''mish''': MishActivation,
    '''quick_gelu''': QuickGELUActivation,
    '''relu''': nn.ReLU,
    '''relu2''': ReLUSquaredActivation,
    '''relu6''': nn.ReLUa,
    '''sigmoid''': nn.Sigmoid,
    '''silu''': SiLUActivation,
    '''swish''': SiLUActivation,
    '''tanh''': nn.Tanh,
}
SCREAMING_SNAKE_CASE_    :       Any       =							ClassInstantier(ACTaCLS)
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    if activation_string in ACTaFN:
        return ACTaFN[activation_string]
    else:
        raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}"""		)


# For backwards compatibility with: from activations import gelu_python
SCREAMING_SNAKE_CASE_    :       Optional[int]       =							get_activation('''gelu_python''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''gelu_new''')
SCREAMING_SNAKE_CASE_    :       str       =							get_activation('''gelu''')
SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							get_activation('''gelu_fast''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''quick_gelu''')
SCREAMING_SNAKE_CASE_    :       Tuple       =							get_activation('''silu''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''mish''')







SCREAMING_SNAKE_CASE_    :       Tuple       =							get_activation('''linear''')






