
'''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()
_lowercase			:  str   							=	logging.get_logger(__name__)

_lowercase			:  Tuple   							=	{
    '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  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]							):



      """simple docstring"""





      for attribute in key.split('''.'''							):
            lowercase_     :					Optional[Any]													=	getattr(__snake_case				,				__snake_case							)

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

      logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Optional[Any]							):



      """simple docstring"""





      lowercase_     :					Optional[int]													=	[]
      lowercase_     :					Tuple													=	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_     :					Union[str, Any]													=	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_     :					List[str]													=	True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowercase_     :					List[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_     :					List[str]													=	True
                              if "*" in mapped_key:
                                    lowercase_     :					Optional[int]													=	name.split(__snake_case							)[0].split('''.'''							)[-2]
                                    lowercase_     :					Tuple													=	mapped_key.replace('''*'''				,				__snake_case							)
                              if "weight_g" in name:
                                    lowercase_     :					Any													=	"weight_g"
                              elif "weight_v" in name:
                                    lowercase_     :					Optional[Any]													=	"weight_v"
                              elif "weight" in name:
                                    lowercase_     :					Optional[int]													=	"weight"
                              elif "bias" in name:
                                    lowercase_     :					Optional[Any]													=	"bias"
                              else:
                                    lowercase_     :					Dict													=	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  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[str]				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Optional[int]							):



      """simple docstring"""





      lowercase_     :					Tuple													=	full_name.split('''conv_layers.'''							)[-1]
      lowercase_     :					str													=	name.split('''.'''							)
      lowercase_     :					Any													=	int(items[0]							)
      lowercase_     :					Tuple													=	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_     :					Union[str, 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_     :					List[str]													=	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_     :					Optional[int]													=	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  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Dict=None				,				__SCREAMING_SNAKE_CASE :			Dict=None				,				__SCREAMING_SNAKE_CASE :			Tuple=True							):



      """simple docstring"""





      if config_path is not None:
            lowercase_     :					Optional[Any]													=	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_     :					Union[str, Any]													=	target_dict.pad_index
                  lowercase_     :					Dict													=	target_dict.bos_index
                  lowercase_     :					List[Any]													=	target_dict.eos_index
                  lowercase_     :					List[str]													=	len(target_dict.symbols							)
                  lowercase_     :					Any													=	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_     :					str													=	True if config.feat_extract_norm == "layer" else False
                  lowercase_     :					int													=	WavaVecaFeatureExtractor(
                      feature_size=1				,				sampling_rate=16000				,				padding_value=0				,				do_normalize=__snake_case				,				return_attention_mask=__snake_case				,				)
                  lowercase_     :					str													=	WavaVecaProcessor(feature_extractor=__snake_case				,				tokenizer=__snake_case							)
                  processor.save_pretrained(__snake_case							)

            lowercase_     :					List[str]													=	HubertForCTC(__snake_case							)
      else:
            lowercase_     :					str													=	HubertModel(__snake_case							)

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

      lowercase_     :					Optional[int]													=	model[0].eval()

      recursively_load_weights(__snake_case				,				__snake_case				,				__snake_case							)

      hf_wavavec.save_pretrained(__snake_case							)


if __name__ == "__main__":
       _lowercase			:  Optional[Any]   							=	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"
       )
       _lowercase			:  int   							=	parser.parse_args()
       convert_hubert_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )





'''simple docstring'''







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


_lowercase			:  Any   							=	logging.get_logger(__name__)

_lowercase			:  Dict   							=	{
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					'''gpt_neox'''






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE=5_04_32    ,							__SCREAMING_SNAKE_CASE=61_44    ,							__SCREAMING_SNAKE_CASE=44    ,							__SCREAMING_SNAKE_CASE=64    ,							__SCREAMING_SNAKE_CASE=2_45_76    ,							__SCREAMING_SNAKE_CASE="gelu"    ,							__SCREAMING_SNAKE_CASE=0.25    ,							__SCREAMING_SNAKE_CASE=1_00_00    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=20_48    ,							__SCREAMING_SNAKE_CASE=0.02    ,							__SCREAMING_SNAKE_CASE=1E-5    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=0    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE    ,							eos_token_id=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[Any]													=	vocab_size
											lowercase_     :					Optional[Any]													=	max_position_embeddings
											lowercase_     :					Optional[int]													=	hidden_size
											lowercase_     :					Tuple													=	num_hidden_layers
											lowercase_     :					Any													=	num_attention_heads
											lowercase_     :					str													=	intermediate_size
											lowercase_     :					Any													=	hidden_act
											lowercase_     :					Tuple													=	rotary_pct
											lowercase_     :					Optional[Any]													=	rotary_emb_base
											lowercase_     :					Any													=	attention_dropout
											lowercase_     :					str													=	hidden_dropout
											lowercase_     :					Dict													=	classifier_dropout
											lowercase_     :					Tuple													=	initializer_range
											lowercase_     :					List[str]													=	layer_norm_eps
											lowercase_     :					Union[str, Any]													=	use_cache
											lowercase_     :					int													=	tie_word_embeddings
											lowercase_     :					Tuple													=	use_parallel_residual
											lowercase_     :					Optional[Any]													=	rope_scaling
											self._rope_scaling_validation()

											if self.hidden_size % self.num_attention_heads != 0:
																	raise ValueError(
																	    '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											if self.rope_scaling is None:
																	return

											if not isinstance(self.rope_scaling    ,							__SCREAMING_SNAKE_CASE		) or len(self.rope_scaling		) != 2:
																	raise ValueError(
																	    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
																	    F'''got {self.rope_scaling}'''		)
											lowercase_     :					List[Any]													=	self.rope_scaling.get('''type'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					int													=	self.rope_scaling.get('''factor'''    ,							__SCREAMING_SNAKE_CASE		)
											if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																	raise ValueError(
																	    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''		)
											if rope_scaling_factor is None or not isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) or rope_scaling_factor <= 1.0:
																	raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''		)



