


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    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.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
UpperCAmelCase__   	=							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Dict:







 """simple docstring"""


 for attribute in key.split('''.'''						):
  _lowercase							      =getattr(__snake_case     ,						__snake_case						)

 if weight_type is not None:
  _lowercase							      =getattr(__snake_case     ,						__snake_case						).shape
 else:
  _lowercase							      =hf_pointer.shape

 if hf_shape != value.shape:
  raise ValueError(
      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							      =value
 elif weight_type == "weight_g":
  _lowercase							      =value
 elif weight_type == "weight_v":
  _lowercase							      =value
 elif weight_type == "bias":
  _lowercase							      =value
 elif weight_type == "running_mean":
  _lowercase							      =value
 elif weight_type == "running_var":
  _lowercase							      =value
 elif weight_type == "num_batches_tracked":
  _lowercase							      =value
 elif weight_type == "inv_freq":
  _lowercase							      =value
 else:
  _lowercase							      =value

 logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[str]:







 """simple docstring"""


 _lowercase							      =[]
 _lowercase							      =fairseq_model.state_dict()

 _lowercase							      =hf_model.wavaveca_conformer.feature_extractor

 for name, value in fairseq_dict.items():
  _lowercase							      =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							      =True
  else:
   for key, mapped_key in MAPPING.items():
    _lowercase							      ='''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
     _lowercase							      =True
     if "*" in mapped_key:
      _lowercase							      =name.split(__snake_case						)[0].split('''.'''						)[-2]
      _lowercase							      =mapped_key.replace('''*'''     ,						__snake_case						)
     if "pos_bias_u" in name:
      _lowercase							      =None
     elif "pos_bias_v" in name:
      _lowercase							      =None
     elif "weight_g" in name:
      _lowercase							      ='''weight_g'''
     elif "weight_v" in name:
      _lowercase							      ='''weight_v'''
     elif "bias" in name:
      _lowercase							      ='''bias'''
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      _lowercase							      ='''weight'''
     elif "running_mean" in name:
      _lowercase							      ='''running_mean'''
     elif "inv_freq" in name:
      _lowercase							      ='''inv_freq'''
     elif "running_var" in name:
      _lowercase							      ='''running_var'''
     elif "num_batches_tracked" in name:
      _lowercase							      ='''num_batches_tracked'''
     else:
      _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      =full_name.split('''conv_layers.'''						)[-1]
 _lowercase							      =name.split('''.'''						)
 _lowercase							      =int(items[0]						)
 _lowercase							      =int(items[1]						)

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."						)
   _lowercase							      =value
   logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."						)
   _lowercase							      =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:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."						)
   _lowercase							      =value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."						)
   _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=True						)     ->			str:







 """simple docstring"""


 if config_path is not None:
  _lowercase							      =WavaVecaConformerConfig.from_pretrained(__snake_case     ,						hidden_act='''swish'''						)
 else:
  _lowercase							      =WavaVecaConformerConfig()

 if "rope" in checkpoint_path:
  _lowercase							      ='''rotary'''

 if is_finetuned:
  if dict_path:
   _lowercase							      =Dictionary.load(__snake_case						)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   _lowercase							      =target_dict.pad_index
   _lowercase							      =target_dict.bos_index
   _lowercase							      =target_dict.eos_index
   _lowercase							      =len(target_dict.symbols						)
   _lowercase							      =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						)
   _lowercase							      =target_dict.indices

   # fairseq has the <pad> and <s> switched
   _lowercase							      =0
   _lowercase							      =1
   with open(__snake_case     ,						'''w'''     ,						encoding='''utf-8'''						) as vocab_handle:
    json.dump(__snake_case     ,						__snake_case						)
   _lowercase							      =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							      =True if config.feat_extract_norm == '''layer''' else False
   _lowercase							      =WavaVecaFeatureExtractor(
       feature_size=1     ,						sampling_rate=16000     ,						padding_value=0     ,						do_normalize=__snake_case     ,						return_attention_mask=__snake_case     ,						)
   _lowercase							      =WavaVecaProcessor(feature_extractor=__snake_case     ,						tokenizer=__snake_case						)
   processor.save_pretrained(__snake_case						)

  _lowercase							      =WavaVecaConformerForCTC(__snake_case						)
 else:
  _lowercase							      =WavaVecaConformerForPreTraining(__snake_case						)

 if is_finetuned:
  _lowercase      ,    _lowercase      ,    _lowercase							      =fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]     ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
 else:
  _lowercase							      =argparse.Namespace(task='''audio_pretraining'''						)
  _lowercase							      =fairseq.tasks.setup_task(__snake_case						)

  _lowercase      ,    _lowercase      ,    _lowercase							      =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,						task=__snake_case						)

 _lowercase							      =model[0].eval()

 recursively_load_weights(__snake_case     ,						__snake_case     ,						not is_finetuned						)

 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_wavaveca_conformer_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			1





     @register_to_config
     def __init__(self		,     UpperCAmelCase=2_0_0_0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=1e-3       )     ->   List[str]:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   str:
      _lowercase							      =torch.linspace(1		,     self.config.sampling_eps		,     UpperCAmelCase		,     device=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Optional[int]:
      if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      _lowercase							      =(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      _lowercase							      =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
      _lowercase							      =std.flatten()
      while len(std.shape       ) < len(score.shape       ):
       _lowercase							      =std.unsqueeze(-1       )
      _lowercase							      =-score / std

      # compute
      _lowercase							      =-1.0 / len(self.timesteps       )

      _lowercase							      =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      _lowercase							      =beta_t.flatten()
      while len(beta_t.shape       ) < len(x.shape       ):
       _lowercase							      =beta_t.unsqueeze(-1       )
      _lowercase							      =-0.5 * beta_t * x

      _lowercase							      =torch.sqrt(UpperCAmelCase       )
      _lowercase							      =drift - diffusion**2 * score
      _lowercase							      =x + drift * dt

      # add noise
      _lowercase							      =randn_tensor(x.shape		,     layout=x.layout		,     generator=UpperCAmelCase		,     device=x.device		,     dtype=x.dtype       )
      _lowercase							      =x_mean + diffusion * math.sqrt(-dt       ) * noise

      return x, x_mean





     def __len__(self       )     ->   str:
      return self.config.num_train_timesteps

