"""simple docstring"""


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

__UpperCamelCase										=     {
    '''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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     for attribute in key.split('.'   ):
          snake_case_   			=   getattr(UpperCAmelCase		,   UpperCAmelCase   )

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

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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Dict:
     snake_case_   			=   []
     snake_case_   			=   fairseq_model.state_dict()

     snake_case_   			=   hf_model.feature_extractor

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

                    if key in name:
                         snake_case_   			=   True
                         if "*" in mapped_key:
                              snake_case_   			=   name.split(UpperCAmelCase   )[0].split('.'   )[-2]
                              snake_case_   			=   mapped_key.replace('*'		,   UpperCAmelCase   )
                         if "weight_g" in name:
                              snake_case_   			=   'weight_g'
                         elif "weight_v" in name:
                              snake_case_   			=   'weight_v'
                         elif "weight" in name:
                              snake_case_   			=   'weight'
                         elif "bias" in name:
                              snake_case_   			=   'bias'
                         else:
                              snake_case_   			=   None
                         set_recursively(UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )
                    continue
          if not is_used:
               unused_weights.append(UpperCAmelCase   )

     logger.warning(f'Unused weights: {unused_weights}'   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> Any:
     snake_case_   			=   full_name.split('conv_layers.'   )[-1]
     snake_case_   			=   name.split('.'   )
     snake_case_   			=   int(items[0]   )
     snake_case_   			=   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.'
               )
               snake_case_   			=   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.'
               )
               snake_case_   			=   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."
               )
               snake_case_   			=   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.'
               )
               snake_case_   			=   value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
     else:
          unused_weights.append(UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase   )							-> Optional[Any]:
     snake_case_   			=   HubertConfig()
     snake_case_   			=   model.config

     snake_case_   			=   fs_config.activation_dropout
     snake_case_   			=   False
     snake_case_   			=   fs_config.attention_dropout
     snake_case_   			=   False
     snake_case_   			=   eval(fs_config.extractor_conv_feature_layers   )
     snake_case_   			=   [x[0] for x in conv_layers]
     snake_case_   			=   [x[1] for x in conv_layers]
     snake_case_   			=   [x[2] for x in conv_layers]
     snake_case_   			=   'gelu'
     snake_case_   			=   'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     snake_case_   			=   False
     snake_case_   			=   0.0
     snake_case_   			=   0.0
     snake_case_   			=   fs_config.activation_fn
     snake_case_   			=   fs_config.dropout
     snake_case_   			=   fs_config.encoder_embed_dim
     snake_case_   			=   0.02
     snake_case_   			=   fs_config.encoder_ffn_embed_dim
     snake_case_   			=   1e-5
     snake_case_   			=   0.0
     snake_case_   			=   fs_config.encoder_attention_heads
     snake_case_   			=   fs_config.conv_pos_groups
     snake_case_   			=   fs_config.conv_pos
     snake_case_   			=   len(UpperCAmelCase   )
     snake_case_   			=   fs_config.encoder_layers

     return config







@torch.no_grad()
def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase=None   )							-> str:
     snake_case_   			=   distilhubert().model.model

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

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

     recursively_load_weights(UpperCAmelCase		,   UpperCAmelCase   )

     feature_extractor.save_pretrained(UpperCAmelCase   )
     hf_model.save_pretrained(UpperCAmelCase   )


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


"""simple docstring"""


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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "splinter"







       def __init__(					self,				lowerCAmelCase__=3_0522,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=512,				lowerCAmelCase__=2,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-12,				lowerCAmelCase__=True,				lowerCAmelCase__=0,				lowerCAmelCase__=104,				**lowerCAmelCase__,				) -> Dict:
            super().__init__(pad_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   layer_norm_eps
            snake_case_   			=   use_cache
            snake_case_   			=   question_token_id

