





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

snake_case_				=			{
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
snake_case_				=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]






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

   if weight_type is not None:
      __snake_case							       =   getattr(snake_case_       ,      snake_case_       ).shape
   else:
      __snake_case							       =   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":
      __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 lowerCamelCase__    (							snake_case_	:  Any       ,      snake_case_	:  List[str]       )      ->				List[str]:
   __snake_case							       =   []
   __snake_case							       =   fairseq_model.state_dict()

   __snake_case							       =   hf_model.unispeech_sat.feature_extractor

   for name, value in fairseq_dict.items():
      __snake_case							       =   False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      hf_model.config.feat_extract_norm == '''group'''       ,      )
         __snake_case							       =   True
      else:
         for key, mapped_key in MAPPING.items():
            __snake_case							       =   '''unispeech_sat.''' + 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]:
               if "layer_norm_for_extract" in name and (".".join(name.split('''.'''       )[:-1]       ) != key):
                  # special case since naming is very similar
                  continue
               __snake_case							       =   True
               if "*" in mapped_key:
                  __snake_case							       =   name.split(snake_case_       )[0].split('''.'''       )[-2]
                  __snake_case							       =   mapped_key.replace('''*'''       ,      snake_case_       )
               if "weight_g" in name:
                  __snake_case							       =   '''weight_g'''
               elif "weight_v" in name:
                  __snake_case							       =   '''weight_v'''
               elif "bias" in name:
                  __snake_case							       =   '''bias'''
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  __snake_case							       =   '''weight'''
               else:
                  __snake_case							       =   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__    (							snake_case_	:  str       ,      snake_case_	:  Union[str, Any]       ,      snake_case_	:  Tuple       ,      snake_case_	:  Tuple       ,      snake_case_	:  Optional[int]       )      ->				List[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:
         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."""       )
         __snake_case							       =   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."""       )
         __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:
         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[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:
         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[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(snake_case_       )






@torch.no_grad()
def lowerCamelCase__    (							snake_case_	:  Optional[Any]       ,      snake_case_	:  Union[str, Any]       ,      snake_case_	:  int=None       ,      snake_case_	:  str=None       ,      snake_case_	:  Optional[int]=True       )      ->				int:
   if config_path is not None:
      __snake_case							       =   UniSpeechSatConfig.from_pretrained(snake_case_       )
   else:
      __snake_case							       =   UniSpeechSatConfig()

   __snake_case							       =   ''''''

   if is_finetuned:
      __snake_case							       =   UniSpeechSatForCTC(snake_case_       )
   else:
      __snake_case							       =   UniSpeechSatForPreTraining(snake_case_       )

   __snake_case       ,	__snake_case       ,	__snake_case							       =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
       [checkpoint_path]       ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
   __snake_case							       =   model[0].eval()

   recursively_load_weights(snake_case_       ,      snake_case_       )

   hf_wavavec.save_pretrained(snake_case_       )


if __name__ == "__main__":
    snake_case_				=			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'
    )
    snake_case_				=			parser.parse_args()
    convert_unispeech_sat_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )









"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list






@require_torchaudio
@require_sentencepiece
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       List[str]:
   _a  :       Any             =	"""laion/clap-htsat-unfused"""
   _a  :       Union[str, Any]             =	tempfile.mkdtemp()


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						Any							)					->       Dict:
   return RobertaTokenizer.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						List[str]							)					->       int:
   return ClapFeatureExtractor.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       Tuple:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						List[str]							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_tokenizer()
   _a  :       Any             =	self.get_feature_extractor()

   _a  :       Optional[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   processor.save_pretrained(self.tmpdirname							)
   _a  :       List[str]             =	ClapProcessor.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 _lowercase	(     self		:						Dict							)					->       Optional[int]:
   _a  :       Tuple             =	ClapProcessor(tokenizer=self.get_tokenizer()							,						feature_extractor=self.get_feature_extractor()							)
   processor.save_pretrained(self.tmpdirname							)

   _a  :       Dict             =	self.get_tokenizer(bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							)
   _a  :       Union[str, Any]             =	self.get_feature_extractor(do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   _a  :       Union[str, Any]             =	ClapProcessor.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 _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   _a  :       Optional[int]             =	self.get_feature_extractor()
   _a  :       Tuple             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	floats_list((3, 1000)							)

   _a  :       List[Any]             =	feature_extractor(UpperCAmelCase__							,						return_tensors="""np"""							)
   _a  :       List[str]             =	processor(audios=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 _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_feature_extractor()
   _a  :       Any             =	self.get_tokenizer()

   _a  :       Any             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Optional[int]             =	"""This is a test string"""

   _a  :       Tuple             =	processor(text=UpperCAmelCase__							)

   _a  :       int             =	tokenizer(UpperCAmelCase__							)

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


 def _lowercase	(     self		:						List[Any]							)					->       Any:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       List[str]             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   _a  :       Dict             =	processor.batch_decode(UpperCAmelCase__							)
   _a  :       Any             =	tokenizer.batch_decode(UpperCAmelCase__							)

   self.assertListEqual(UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						Dict							)					->       List[str]:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       Optional[Any]             =	self.get_tokenizer()

   _a  :       Union[str, Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   self.assertListEqual(
       processor.model_input_names[2:]							,						feature_extractor.model_input_names							,						msg="""`processor` and `feature_extractor` model input names do not match"""							,						)

