






"""simple docstring"""


import copy

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


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	int,			_lowerCamelCase					:	Any=2_56,			_lowerCamelCase					:	str=10_24,			_lowerCamelCase					:	Any=16,			_lowerCamelCase					:	Any=16,			_lowerCamelCase					:	Tuple=4,			_lowerCamelCase					:	str="gelu",			_lowerCamelCase					:	int=1e-6,			**_lowerCamelCase					:	Tuple,			):

     '''simple docstring'''
     super().__init__(**_lowerCamelCase    )
     __A		     =					hidden_size
     __A		     =					image_size
     __A		     =					patch_size
     __A		     =					image_size // patch_size
     __A		     =					mask_input_channels
     __A		     =					num_point_embeddings
     __A		     =					hidden_act
     __A		     =					layer_norm_eps





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	int,			_lowerCamelCase					:	str=2_56,			_lowerCamelCase					:	Tuple="relu",			_lowerCamelCase					:	Optional[Any]=20_48,			_lowerCamelCase					:	int=2,			_lowerCamelCase					:	List[Any]=8,			_lowerCamelCase					:	List[Any]=2,			_lowerCamelCase					:	str=3,			_lowerCamelCase					:	Dict=3,			_lowerCamelCase					:	List[str]=2_56,			_lowerCamelCase					:	str=1e-6,			**_lowerCamelCase					:	Union[str, Any],			):

     '''simple docstring'''
     super().__init__(**_lowerCamelCase    )
     __A		     =					hidden_size
     __A		     =					hidden_act
     __A		     =					mlp_dim
     __A		     =					num_hidden_layers
     __A		     =					num_attention_heads
     __A		     =					attention_downsample_rate
     __A		     =					num_multimask_outputs
     __A		     =					iou_head_depth
     __A		     =					iou_head_hidden_dim
     __A		     =					layer_norm_eps





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	int,			_lowerCamelCase					:	Union[str, Any]=7_68,			_lowerCamelCase					:	List[Any]=2_56,			_lowerCamelCase					:	Optional[int]=12,			_lowerCamelCase					:	Tuple=12,			_lowerCamelCase					:	Optional[Any]=3,			_lowerCamelCase					:	Any=10_24,			_lowerCamelCase					:	Dict=16,			_lowerCamelCase					:	List[str]="gelu",			_lowerCamelCase					:	Optional[int]=1e-06,			_lowerCamelCase					:	Optional[Any]=0.0,			_lowerCamelCase					:	Any=1e-10,			_lowerCamelCase					:	Tuple=True,			_lowerCamelCase					:	int=4.0,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	List[Any]=True,			_lowerCamelCase					:	List[str]=14,			_lowerCamelCase					:	Optional[int]=[2, 5, 8, 11],			_lowerCamelCase					:	List[str]=1_28,			_lowerCamelCase					:	Union[str, Any]=None,			**_lowerCamelCase					:	int,			):

     '''simple docstring'''
     super().__init__(**_lowerCamelCase    )

     __A		     =					hidden_size
     __A		     =					output_channels
     __A		     =					num_hidden_layers
     __A		     =					num_attention_heads
     __A		     =					num_channels
     __A		     =					image_size
     __A		     =					patch_size
     __A		     =					hidden_act
     __A		     =					layer_norm_eps
     __A		     =					attention_dropout
     __A		     =					initializer_range
     __A		     =					qkv_bias
     __A		     =					mlp_ratio
     __A		     =					use_abs_pos
     __A		     =					use_rel_pos
     __A		     =					window_size
     __A		     =					global_attn_indexes
     __A		     =					num_pos_feats
     __A		     =					int(hidden_size * mlp_ratio    ) if mlp_dim is None else mlp_dim





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Any							=					"sam"
   A_		:      Optional[Any]							=					True


   def __init__(      self					:	Tuple,			_lowerCamelCase					:	Optional[int]=None,			_lowerCamelCase					:	Dict=None,			_lowerCamelCase					:	int=None,			_lowerCamelCase					:	str=0.02,			**_lowerCamelCase					:	List[str],			):

     '''simple docstring'''
     super().__init__(**_lowerCamelCase    )
     __A		     =					vision_config if vision_config is not None else {}
     __A		     =					prompt_encoder_config if prompt_encoder_config is not None else {}
     __A		     =					mask_decoder_config if mask_decoder_config is not None else {}

     if isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       __A		     =					vision_config.to_dict()
     if isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       __A		     =					prompt_encoder_config.to_dict()
     if isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       __A		     =					mask_decoder_config.to_dict()

     __A		     =					SamVisionConfig(**_lowerCamelCase    )
     __A		     =					SamPromptEncoderConfig(**_lowerCamelCase    )
     __A		     =					SamMaskDecoderConfig(**_lowerCamelCase    )
     __A		     =					initializer_range






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					copy.deepcopy(self.__dict__    )
     __A		     =					self.vision_config.to_dict()
     __A		     =					self.prompt_encoder_config.to_dict()
     __A		     =					self.mask_decoder_config.to_dict()
     __A		     =					self.__class__.model_type
     return output











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)



