


"""simple docstring"""



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}






def    lowerCamelCase       (a_    :List[str]					,					a_    :Optional[Any]					,					a_    :Optional[int]					,					a_    :int					,					a_    :Tuple)  ->				int:
       for attribute in key.split('''.'''):
              lowercase		:int											=					getattr(__snake_case					,					__snake_case)

       if weight_type is not None:
              lowercase		:List[str]											=					getattr(__snake_case					,					__snake_case).shape
       else:
              lowercase		:int											=					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":
              lowercase		:Union[str, Any]											=					value
       elif weight_type == "weight_g":
              lowercase		:int											=					value
       elif weight_type == "weight_v":
              lowercase		:Tuple											=					value
       elif weight_type == "bias":
              lowercase		:List[Any]											=					value
       else:
              lowercase		:int											=					value

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






def    lowerCamelCase       (a_    :int					,					a_    :Dict					,					a_    :Tuple)  ->				Dict:
       lowercase		:Tuple											=					[]
       lowercase		:int											=					fairseq_model.state_dict()

       lowercase		:List[str]											=					hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase		:List[str]											=					False
              if "conv_layers" in name:
                     load_conv_layer(
                         __snake_case					,					__snake_case					,					__snake_case					,					__snake_case					,					hf_model.config.feat_extract_norm == '''group'''					,					)
                     lowercase		:Optional[Any]											=					True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase		:Any											=					'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                            if key in name or (key.split('''w2v_model.''')[-1] == name.split('''.''')[0] and not is_finetuned):
                                   lowercase		:Union[str, Any]											=					True
                                   if "*" in mapped_key:
                                          lowercase		:int											=					name.split(__snake_case)[0].split('''.''')[-2]
                                          lowercase		:Optional[int]											=					mapped_key.replace('''*'''					,					__snake_case)
                                   if "weight_g" in name:
                                          lowercase		:int											=					'''weight_g'''
                                   elif "weight_v" in name:
                                          lowercase		:Dict											=					'''weight_v'''
                                   elif "weight" in name:
                                          lowercase		:int											=					'''weight'''
                                   elif "bias" in name:
                                          lowercase		:Optional[Any]											=					'''bias'''
                                   else:
                                          lowercase		:Union[str, Any]											=					None
                                   set_recursively(__snake_case					,					__snake_case					,					__snake_case					,					__snake_case					,					__snake_case)
                            continue
              if not is_used:
                     unused_weights.append(__snake_case)

       logger.warning(F"""Unused weights: {unused_weights}""")






def    lowerCamelCase       (a_    :Dict					,					a_    :List[str]					,					a_    :List[Any]					,					a_    :int					,					a_    :str)  ->				int:
       lowercase		:Optional[int]											=					full_name.split('''conv_layers.''')[-1]
       lowercase		:Dict											=					name.split('''.''')
       lowercase		:Optional[Any]											=					int(items[0])
       lowercase		:Optional[Any]											=					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."""
                     )
                     lowercase		:Optional[Any]											=					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."""
                     )
                     lowercase		:Union[str, Any]											=					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."
                     )
                     lowercase		:int											=					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."""
                     )
                     lowercase		:Any											=					value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
       else:
              unused_weights.append(__snake_case)






@torch.no_grad()
def    lowerCamelCase       (a_    :Any					,					a_    :Optional[Any]					,					a_    :Dict=None					,					a_    :int=None					,					a_    :Optional[int]=True)  ->				Tuple:
       if config_path is not None:
              lowercase		:List[str]											=					HubertConfig.from_pretrained(__snake_case)
       else:
              lowercase		:Dict											=					HubertConfig()

       if is_finetuned:
              if dict_path:
                     lowercase		:Dict											=					Dictionary.load(__snake_case)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowercase		:Optional[int]											=					target_dict.pad_index
                     lowercase		:str											=					target_dict.bos_index
                     lowercase		:Any											=					target_dict.eos_index
                     lowercase		:int											=					len(target_dict.symbols)
                     lowercase		:Dict											=					os.path.join(__snake_case					,					'''vocab.json''')
                     if not os.path.isdir(__snake_case):
                            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case))
                            return
                     os.makedirs(__snake_case					,					exist_ok=__snake_case)
                     with open(__snake_case					,					'''w'''					,					encoding='''utf-8''') as vocab_handle:
                            json.dump(target_dict.indices					,					__snake_case)
                     lowercase		:List[str]											=					WavaVecaCTCTokenizer(
                         __snake_case					,					unk_token=target_dict.unk_word					,					pad_token=target_dict.pad_word					,					bos_token=target_dict.bos_word					,					eos_token=target_dict.eos_word					,					word_delimiter_token='''|'''					,					do_lower_case=__snake_case					,					)
                     lowercase		:Optional[int]											=					True if config.feat_extract_norm == '''layer''' else False
                     lowercase		:Any											=					WavaVecaFeatureExtractor(
                         feature_size=1					,					sampling_rate=1_6000					,					padding_value=0					,					do_normalize=__snake_case					,					return_attention_mask=__snake_case					,					)
                     lowercase		:Tuple											=					WavaVecaProcessor(feature_extractor=__snake_case					,					tokenizer=__snake_case)
                     processor.save_pretrained(__snake_case)

              lowercase		:Any											=					HubertForCTC(__snake_case)
       else:
              lowercase		:List[Any]											=					HubertModel(__snake_case)

       if is_finetuned:
              lowercase				,	lowercase				,	lowercase		:Union[str, Any]											=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]					,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1])})
       else:
              lowercase				,	lowercase				,	lowercase		:Optional[int]											=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

       lowercase		:Any											=					model[0].eval()

       recursively_load_weights(__snake_case					,					__snake_case					,					__snake_case)

       hf_wavavec.save_pretrained(__snake_case)


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('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 parser.add_argument(
     '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 UpperCAmelCase   		=      parser.parse_args()
 convert_hubert_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )









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


lowerCamelCase      :       Optional[int]    =						logging.get_logger(__name__)

lowerCamelCase      :       Optional[Any]    =						{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				"""pegasus"""
      snake_case_	=				["""past_key_values"""]
      snake_case_	=				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


      def __init__(						self       ,    __UpperCamelCase=5_0_2_6_5       ,    __UpperCamelCase=1_0_2_4       ,    __UpperCamelCase=1_2       ,    __UpperCamelCase=4_0_9_6       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=1_2       ,    __UpperCamelCase=4_0_9_6       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=True       ,    __UpperCamelCase=True       ,    __UpperCamelCase="gelu"       ,    __UpperCamelCase=1_0_2_4       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.0_2       ,    __UpperCamelCase=0       ,    __UpperCamelCase=False       ,    __UpperCamelCase=0       ,    __UpperCamelCase=1       ,    __UpperCamelCase=1       ,    **__UpperCamelCase       ,    )->						Tuple:
           __lowerCAmelCase             =       vocab_size
           __lowerCAmelCase             =       max_position_embeddings
           __lowerCAmelCase             =       d_model
           __lowerCAmelCase             =       encoder_ffn_dim
           __lowerCAmelCase             =       encoder_layers
           __lowerCAmelCase             =       encoder_attention_heads
           __lowerCAmelCase             =       decoder_ffn_dim
           __lowerCAmelCase             =       decoder_layers
           __lowerCAmelCase             =       decoder_attention_heads
           __lowerCAmelCase             =       dropout
           __lowerCAmelCase             =       attention_dropout
           __lowerCAmelCase             =       activation_dropout
           __lowerCAmelCase             =       activation_function
           __lowerCAmelCase             =       init_std
           __lowerCAmelCase             =       encoder_layerdrop
           __lowerCAmelCase             =       decoder_layerdrop
           __lowerCAmelCase             =       use_cache
           __lowerCAmelCase             =       encoder_layers
           __lowerCAmelCase             =       scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=__UpperCamelCase       ,    eos_token_id=__UpperCamelCase       ,    is_encoder_decoder=__UpperCamelCase       ,    decoder_start_token_id=__UpperCamelCase       ,    forced_eos_token_id=__UpperCamelCase       ,    **__UpperCamelCase       ,    )


      @property
      def 			__UpperCAmelCase		(						self						)->						int:
           return self.encoder_attention_heads


      @property
      def 			__UpperCAmelCase		(						self						)->						int:
           return self.d_model






