import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

__A         =				{
    "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 SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)						->						Optional[Any]:
   for attribute in key.split('''.'''				):
      lowercase__:     Any      =   getattr(lowercase__ ,					lowercase__				)

   if weight_type is not None:
      lowercase__:     Dict      =   getattr(lowercase__ ,					lowercase__				).shape
   else:
      lowercase__:     List[Any]      =   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__:     List[Any]      =   value
   elif weight_type == "weight_g":
      lowercase__:     Optional[int]      =   value
   elif weight_type == "weight_v":
      lowercase__:     Optional[int]      =   value
   elif weight_type == "bias":
      lowercase__:     Optional[Any]      =   value
   else:
      lowercase__:     List[str]      =   value

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



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase				)						->						int:
   lowercase__:     Union[str, Any]      =   []
   lowercase__:     Tuple      =   fairseq_model.state_dict()

   lowercase__:     List[Any]      =   hf_model.feature_extractor

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

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

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



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



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Any:
   lowercase__:     int      =   HubertConfig()
   lowercase__:     List[Any]      =   model.config

   lowercase__:     Tuple      =   fs_config.activation_dropout
   lowercase__:     Dict      =   False
   lowercase__:     int      =   fs_config.attention_dropout
   lowercase__:     Tuple      =   False
   lowercase__:     int      =   eval(fs_config.extractor_conv_feature_layers				)
   lowercase__:     List[str]      =   [x[0] for x in conv_layers]
   lowercase__:     Optional[Any]      =   [x[1] for x in conv_layers]
   lowercase__:     Any      =   [x[2] for x in conv_layers]
   lowercase__:     Union[str, Any]      =   '''gelu'''
   lowercase__:     int      =   '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   lowercase__:     List[Any]      =   False
   lowercase__:     List[str]      =   0.0
   lowercase__:     List[Any]      =   0.0
   lowercase__:     Any      =   fs_config.activation_fn
   lowercase__:     Any      =   fs_config.dropout
   lowercase__:     Dict      =   fs_config.encoder_embed_dim
   lowercase__:     Optional[int]      =   0.0_2
   lowercase__:     List[str]      =   fs_config.encoder_ffn_embed_dim
   lowercase__:     Union[str, Any]      =   1e-5
   lowercase__:     List[Any]      =   0.0
   lowercase__:     Any      =   fs_config.encoder_attention_heads
   lowercase__:     Optional[int]      =   fs_config.conv_pos_groups
   lowercase__:     List[str]      =   fs_config.conv_pos
   lowercase__:     Optional[Any]      =   len(lowercase__				)
   lowercase__:     Optional[Any]      =   fs_config.encoder_layers

   return config



@torch.no_grad()
def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase=None				)						->						Union[str, Any]:
   lowercase__:     Tuple      =   distilhubert().model.model

   if config_path is not None:
      lowercase__:     Any      =   HubertConfig.from_pretrained(lowercase__				)
   else:
      lowercase__:     Any      =   convert_config(lowercase__				)
   lowercase__:     Dict      =   model.eval()

   lowercase__:     Optional[Any]      =   WavaVecaFeatureExtractor(
       feature_size=1 ,					sampling_rate=1_6_0_0_0 ,					padding_value=0 ,					do_normalize=lowercase__ ,					return_attention_mask=lowercase__ ,					)
   lowercase__:     Optional[int]      =   HubertModel(lowercase__				)

   recursively_load_weights(lowercase__ ,					lowercase__				)

   feature_extractor.save_pretrained(lowercase__				)
   hf_model.save_pretrained(lowercase__				)


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

"""simple docstring"""






import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__A         =				get_tests_dir("fixtures/test_sentencepiece.model")


@require_torch
@require_torchaudio
@require_sentencepiece
class 					UpperCAmelCase		(unittest.TestCase						):



    """simple docstring"""

    def _snake_case    (     self							):
       lowercase__:     Dict      =   tempfile.mkdtemp()

       lowercase__:     str      =   ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
       lowercase__:     Optional[Any]      =   dict(zip(_UpperCAmelCase					,    range(len(_UpperCAmelCase							)							)							)							)
       lowercase__:     List[Any]      =   Path(self.tmpdirname							)
       save_json(_UpperCAmelCase					,    save_dir / VOCAB_FILES_NAMES['''vocab_file''']							)
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
          copyfile(_UpperCAmelCase					,    save_dir / VOCAB_FILES_NAMES['''spm_file''']							)

       lowercase__:     Tuple      =   SpeechaTextTokenizer.from_pretrained(self.tmpdirname							)
       tokenizer.save_pretrained(self.tmpdirname							)

       lowercase__:     Optional[int]      =   {
           '''feature_size''': 24,
           '''num_mel_bins''': 24,
           '''padding_value''': 0.0,
           '''sampling_rate''': 16000,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }
       save_json(_UpperCAmelCase					,    save_dir / FEATURE_EXTRACTOR_NAME							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






    def _snake_case    (     self							):
       shutil.rmtree(self.tmpdirname							)






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_tokenizer()
       lowercase__:     Optional[int]      =   self.get_feature_extractor()

       lowercase__:     int      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       processor.save_pretrained(self.tmpdirname							)
       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor.from_pretrained(self.tmpdirname							)

       self.assertEqual(processor.tokenizer.get_vocab()					,    tokenizer.get_vocab()							)
       self.assertIsInstance(processor.tokenizer					,    _UpperCAmelCase							)

       self.assertEqual(processor.feature_extractor.to_json_string()					,    feature_extractor.to_json_string()							)
       self.assertIsInstance(processor.feature_extractor					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   SpeechaTextProcessor(
           tokenizer=self.get_tokenizer()					,    feature_extractor=self.get_feature_extractor()							)
       processor.save_pretrained(self.tmpdirname							)

       lowercase__:     Any      =   self.get_tokenizer(bos_token='''(BOS)'''					,    eos_token='''(EOS)'''							)
       lowercase__:     Tuple      =   self.get_feature_extractor(do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

       lowercase__:     List[str]      =   SpeechaTextProcessor.from_pretrained(
           self.tmpdirname					,    bos_token='''(BOS)'''					,    eos_token='''(EOS)'''					,    do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

       self.assertEqual(processor.tokenizer.get_vocab()					,    tokenizer_add_kwargs.get_vocab()							)
       self.assertIsInstance(processor.tokenizer					,    _UpperCAmelCase							)

       self.assertEqual(processor.feature_extractor.to_json_string()					,    feature_extractor_add_kwargs.to_json_string()							)
       self.assertIsInstance(processor.feature_extractor					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     List[Any]      =   self.get_tokenizer()

       lowercase__:     Any      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     int      =   floats_list((3, 1000)							)

       lowercase__:     Optional[Any]      =   feature_extractor(_UpperCAmelCase					,    return_tensors='''np'''							)
       lowercase__:     Dict      =   processor(_UpperCAmelCase					,    return_tensors='''np'''							)

       for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()					,    input_processor[key].sum()					,    delta=1e-2							)






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     Dict      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     Dict      =   '''This is a test string'''

       lowercase__:     int      =   processor(text=_UpperCAmelCase							)

       lowercase__:     Union[str, Any]      =   tokenizer(_UpperCAmelCase							)

       for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]					,    encoded_processor[key]							)






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   self.get_feature_extractor()
       lowercase__:     Union[str, Any]      =   self.get_tokenizer()

       lowercase__:     Tuple      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     List[str]      =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowercase__:     List[Any]      =   processor.batch_decode(_UpperCAmelCase							)
       lowercase__:     Dict      =   tokenizer.batch_decode(_UpperCAmelCase							)

       self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Optional[Any]      =   self.get_feature_extractor()
       lowercase__:     List[str]      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       self.assertListEqual(
           processor.model_input_names					,    feature_extractor.model_input_names					,    msg='''`processor` and `feature_extractor` model input names do not match'''					,    )
