




'''simple docstring'''




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


snake_case__ 							=    [nn.LayerNorm]

snake_case__ 							=    logging.get_logger(__name__)

snake_case__ 							=    version.parse(version.parse(torch.__version__).base_version)

snake_case__ 							=    parsed_torch_version_base >= version.parse("""2.0""")
snake_case__ 							=    parsed_torch_version_base >= version.parse("""1.12""")
snake_case__ 							=    parsed_torch_version_base >= version.parse("""1.11""")
snake_case__ 							=    parsed_torch_version_base < version.parse("""1.11""")




def     snake_case__     (			lowerCamelCase__					: Union[str, Any]      ,		lowerCamelCase__					: str      ,		lowerCamelCase__					: Optional[Any]      ,		lowerCamelCase__					: str      ,		self					: int      ) -> Optional[int]:
					from torch import _softmax_backward_data

					if is_torch_less_than_1_11:
										return _softmax_backward_data(UpperCamelCase__      ,		UpperCamelCase__      ,		parent.dim      ,		self      )
					else:
										return _softmax_backward_data(UpperCamelCase__      ,		UpperCamelCase__      ,		parent.dim      ,		self.dtype      )




def     snake_case__     (			lowerCamelCase__					: nn.Linear      ,		lowerCamelCase__					: torch.LongTensor      ,		lowerCamelCase__					: int = 0      ) -> nn.Linear:
					A_			:  Optional[int]			=				index.to(layer.weight.device      )
					A_			:  List[str]			=				layer.weight.index_select(UpperCamelCase__      ,		UpperCamelCase__      ).clone().detach()
					if layer.bias is not None:
										if dim == 1:
															A_			:  List[Any]			=				layer.bias.clone().detach()
										else:
															A_			:  List[str]			=				layer.bias[index].clone().detach()
					A_			:  Optional[Any]			=				list(layer.weight.size()      )
					A_			:  Any			=				len(UpperCamelCase__      )
					A_			:  List[Any]			=				nn.Linear(new_size[1]      ,		new_size[0]      ,		bias=layer.bias is not None      ).to(layer.weight.device      )
					A_			:  Tuple			=				False
					new_layer.weight.copy_(W.contiguous()      )
					A_			:  List[Any]			=				True
					if layer.bias is not None:
										A_			:  Dict			=				False
										new_layer.bias.copy_(b.contiguous()      )
										A_			:  Optional[int]			=				True
					return new_layer






class      UpperCamelCase_  (nn.Module  ):







						"""simple docstring"""


						def __init__(  self   :							List[str]				,			_lowerCamelCase   :							Union[str, Any]				,			_lowerCamelCase   :							List[str]     ):





											"""simple docstring"""







											super().__init__()
											A_			:  Optional[Any]			=				nf
											A_			:  str			=				nn.Parameter(torch.empty(lowerCAmelCase_				,			lowerCAmelCase_     )     )
											A_			:  int			=				nn.Parameter(torch.zeros(lowerCAmelCase_     )     )
											nn.init.normal_(self.weight				,			std=0.02     )







						def    _a     (  self   :							Union[str, Any]				,			_lowerCamelCase   :							Optional[Any]     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				x.size()[:-1] + (self.nf,)
											A_			:  str			=				torch.addmm(self.bias				,			x.view(-1				,			x.size(-1     )     )				,			self.weight     )
											A_			:  str			=				x.view(lowerCAmelCase_     )
											return x






def     snake_case__     (			lowerCamelCase__					: ConvaD      ,		lowerCamelCase__					: torch.LongTensor      ,		lowerCamelCase__					: int = 1      ) -> ConvaD:
					A_			:  Optional[Any]			=				index.to(layer.weight.device      )
					A_			:  Tuple			=				layer.weight.index_select(UpperCamelCase__      ,		UpperCamelCase__      ).clone().detach()
					if dim == 0:
										A_			:  List[str]			=				layer.bias.clone().detach()
					else:
										A_			:  int			=				layer.bias[index].clone().detach()
					A_			:  str			=				list(layer.weight.size()      )
					A_			:  Optional[int]			=				len(UpperCamelCase__      )
					A_			:  str			=				ConvaD(new_size[1]      ,		new_size[0]      ).to(layer.weight.device      )
					A_			:  List[Any]			=				False
					new_layer.weight.copy_(W.contiguous()      )
					A_			:  int			=				True
					A_			:  Union[str, Any]			=				False
					new_layer.bias.copy_(b.contiguous()      )
					A_			:  int			=				True
					return new_layer




def     snake_case__     (			lowerCamelCase__					: Union[nn.Linear, ConvaD]      ,		lowerCamelCase__					: torch.LongTensor      ,		lowerCamelCase__					: Optional[int] = None      ) -> Union[nn.Linear, ConvaD]:




					if isinstance(UpperCamelCase__      ,		nn.Linear      ):
										return prune_linear_layer(UpperCamelCase__      ,		UpperCamelCase__      ,		dim=0 if dim is None else dim      )
					elif isinstance(UpperCamelCase__      ,		UpperCamelCase__      ):
										return prune_convad_layer(UpperCamelCase__      ,		UpperCamelCase__      ,		dim=1 if dim is None else dim      )
					else:
										raise ValueError(f'Can\'t prune layer of class {layer.__class__}'      )




def     snake_case__     (			lowerCamelCase__					: Callable[..., torch.Tensor]      ,		lowerCamelCase__					: int      ,		lowerCamelCase__					: int      ,		*lowerCamelCase__					: Tuple      ) -> torch.Tensor:
					assert len(UpperCamelCase__      ) > 0, f'{input_tensors} has to be a tuple/list of tensors'

					# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
					A_			:  int			=				len(inspect.signature(UpperCamelCase__      ).parameters      )
					if num_args_in_forward_chunk_fn != len(UpperCamelCase__      ):
										raise ValueError(
										    f'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCamelCase__      )} input '
										    '''tensors are given'''      )

					if chunk_size > 0:
										A_			:  str			=				input_tensors[0].shape[chunk_dim]
										for input_tensor in input_tensors:
															if input_tensor.shape[chunk_dim] != tensor_shape:
																				raise ValueError(
																				    f'All input tenors have to be of the same shape: {tensor_shape}, '
																				    f'found shape {input_tensor.shape[chunk_dim]}'      )

										if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
															raise ValueError(
															    f'The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '
															    f'size {chunk_size}'      )

										A_			:  List[Any]			=				input_tensors[0].shape[chunk_dim] // chunk_size

										# chunk input tensor into tuples
										A_			:  Tuple			=				tuple(input_tensor.chunk(UpperCamelCase__      ,		dim=UpperCamelCase__      ) for input_tensor in input_tensors      )
										# apply forward fn to every tuple
										A_			:  List[Any]			=				tuple(forward_fn(*UpperCamelCase__      ) for input_tensors_chunk in zip(*UpperCamelCase__      )      )
										# concatenate output at same dimension
										return torch.cat(UpperCamelCase__      ,		dim=UpperCamelCase__      )

					return forward_fn(*UpperCamelCase__      )




def     snake_case__     (			lowerCamelCase__					: List[int]      ,		lowerCamelCase__					: int      ,		lowerCamelCase__					: int      ,		lowerCamelCase__					: Set[int]      ) -> Tuple[Set[int], torch.LongTensor]:
					A_			:  List[Any]			=				torch.ones(UpperCamelCase__      ,		UpperCamelCase__      )
					A_			:  int			=				set(UpperCamelCase__      ) - already_pruned_heads  # Convert to set and remove already pruned heads
					for head in heads:
										# Compute how many pruned heads are before the head and move the index accordingly
										A_			:  List[Any]			=				head - sum(1 if h < head else 0 for h in already_pruned_heads      )
										A_			:  Any			=				0
					A_			:  Optional[Any]			=				mask.view(-1      ).contiguous().eq(1      )
					A_			:  Dict			=				torch.arange(len(UpperCamelCase__      )      )[mask].long()
					return heads, index




def     snake_case__     (			*lowerCamelCase__					: Union[torch.Tensor, List[torch.Tensor]]      ,		lowerCamelCase__					: Optional[str] = None      ) -> Tuple[torch.Tensor, ...]:
					return torch.meshgrid(*UpperCamelCase__      ,		indexing=UpperCamelCase__      )





def     snake_case__     (			lowerCamelCase__					: torch.Tensor      ) -> Tuple[torch.device, int, int]:
					return tensor.device, storage_ptr(UpperCamelCase__      ), storage_size(UpperCamelCase__      )










import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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





def     lowerCamelCase__  (  UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Optional[Any]    )      ->			Optional[int]:



					'''simple docstring'''




					_snake_case						=					WavLMForSequenceClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['projector.weight']
					_snake_case						=					downstream_dict['projector.bias']
					_snake_case						=					downstream_dict['model.post_net.linear.weight']
					_snake_case						=					downstream_dict['model.post_net.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Optional[Any]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							List[Any]    )      ->			Optional[Any]:



					'''simple docstring'''




					_snake_case						=					WavLMForAudioFrameClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['model.linear.weight']
					_snake_case						=					downstream_dict['model.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict    )      ->			Tuple:



					'''simple docstring'''




					_snake_case						=					WavLMForXVector.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['connector.weight']
					_snake_case						=					downstream_dict['connector.bias']
					for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
										_snake_case						=					downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										_snake_case						=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
					_snake_case						=					downstream_dict['objective.W']
					return model





@torch.no_grad()
def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							Optional[int]    )      ->			int:



					'''simple docstring'''




					_snake_case						=					torch.load(UpperCamelCase__							,     map_location='cpu'    )

					_snake_case						=					checkpoint['Downstream']

					_snake_case						=					WavLMConfig.from_pretrained(UpperCamelCase__    )
					_snake_case						=					WavaVecaFeatureExtractor.from_pretrained(
					    UpperCamelCase__							,     return_attention_mask=UpperCamelCase__							,     do_normalize=UpperCamelCase__    )

					_snake_case						=					hf_config.architectures[0]
					if arch.endswith('ForSequenceClassification'    ):
										_snake_case						=					convert_classification(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					elif arch.endswith('ForAudioFrameClassification'    ):
										_snake_case						=					convert_diarization(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					elif arch.endswith('ForXVector'    ):
										_snake_case						=					convert_xvector(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					else:
										raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''    )

					if hf_config.use_weighted_layer_sum:
										_snake_case						=					checkpoint['Featurizer']['weights']

					hf_feature_extractor.save_pretrained(UpperCamelCase__    )
					hf_model.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
					UpperCAmelCase_   				=  argparse.ArgumentParser()
					parser.add_argument(
					    """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
					)
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
					parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
					UpperCAmelCase_   				=  parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



