






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    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",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
__A					=	[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def       lowercase__	(					A_:						int  ,      A_:						Optional[int]  ,      A_:						Optional[int]  ,      A_:						int  ,      A_:						List[str]  ,      A_:						Union[str, Any]		)	->				Any:







       """simple docstring"""






       for attribute in key.split("""."""		):
              if is_finetuned:
                     if attribute in ["quantizer", "project_q", "project_hid"]:
                            # those layers are only relevant for pretraining and should be dropped
                            return

                     if attribute == "ctc_proj":
                            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                            __UpperCAmelCase     				="""lm_head"""

              __UpperCAmelCase     				=getattr(A_  ,      A_		)

       if weight_type is not None:
              __UpperCAmelCase     				=getattr(A_  ,      A_		).shape
       else:
              __UpperCAmelCase     				=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":
              __UpperCAmelCase     				=value
       elif weight_type == "weight_g":
              __UpperCAmelCase     				=value
       elif weight_type == "weight_v":
              __UpperCAmelCase     				=value
       elif weight_type == "bias":
              __UpperCAmelCase     				=value
       else:
              __UpperCAmelCase     				=value

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







def       lowercase__	(					A_:						str  ,      A_:						int  ,      A_:						int		)	->				List[str]:







       """simple docstring"""






       __UpperCAmelCase     				=[]
       __UpperCAmelCase     				=fairseq_model.state_dict()

       __UpperCAmelCase     				=hf_model.unispeech.feature_extractor

       for name, value in fairseq_dict.items():
              __UpperCAmelCase     				=False
              if "conv_layers" in name:
                     load_conv_layer(
                         A_  ,      A_  ,      A_  ,      A_  ,      hf_model.config.feat_extract_norm == """group"""  ,      )
                     __UpperCAmelCase     				=True
              else:
                     for key, mapped_key in MAPPING.items():
                            __UpperCAmelCase     				="""unispeech.""" + 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]:
                                   __UpperCAmelCase     				=True
                                   if "*" in mapped_key:
                                          __UpperCAmelCase     				=name.split(A_		)[0].split("""."""		)[-2]
                                          __UpperCAmelCase     				=mapped_key.replace("""*"""  ,      A_		)
                                   if "weight_g" in name:
                                          __UpperCAmelCase     				="""weight_g"""
                                   elif "weight_v" in name:
                                          __UpperCAmelCase     				="""weight_v"""
                                   elif "bias" in name:
                                          __UpperCAmelCase     				="""bias"""
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          __UpperCAmelCase     				="""weight"""
                                   else:
                                          __UpperCAmelCase     				=None
                                   set_recursively(A_  ,      A_  ,      A_  ,      A_  ,      A_  ,      A_		)
                            continue
              if not is_used:
                     unused_weights.append(A_		)

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







def       lowercase__	(					A_:						List[str]  ,      A_:						Union[str, Any]  ,      A_:						Any  ,      A_:						Optional[int]  ,      A_:						Union[str, Any]		)	->				Tuple:







       """simple docstring"""






       __UpperCAmelCase     				=full_name.split("""conv_layers."""		)[-1]
       __UpperCAmelCase     				=name.split("""."""		)
       __UpperCAmelCase     				=int(items[0]		)
       __UpperCAmelCase     				=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.'''
                     )
                     __UpperCAmelCase     				=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.'''
                     )
                     __UpperCAmelCase     				=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."
                     )
                     __UpperCAmelCase     				=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.'''
                     )
                     __UpperCAmelCase     				=value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
       else:
              unused_weights.append(A_		)







@torch.no_grad()
def       lowercase__	(					A_:						Optional[int]  ,      A_:						str  ,      A_:						List[Any]=None  ,      A_:						List[str]=None  ,      A_:						Optional[int]=True		)	->				Dict:







       """simple docstring"""






       if config_path is not None:
              __UpperCAmelCase     				=UniSpeechConfig.from_pretrained(A_		)
       else:
              __UpperCAmelCase     				=UniSpeechConfig()

       if is_finetuned:
              if dict_path:
                     __UpperCAmelCase     				=Dictionary.load_from_json(A_		)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     __UpperCAmelCase     				=target_dict.pad_index
                     __UpperCAmelCase     				=target_dict.bos_index
                     __UpperCAmelCase     				=target_dict.eos_index
                     __UpperCAmelCase     				=len(target_dict.symbols		)
                     __UpperCAmelCase     				=os.path.join(A_  ,      """vocab.json"""		)
                     if not os.path.isdir(A_		):
                            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(A_		)		)
                            return
                     os.makedirs(A_  ,      exist_ok=A_		)
                     __UpperCAmelCase     				=target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     __UpperCAmelCase     				=42
                     __UpperCAmelCase     				=43
                     with open(A_  ,      """w"""  ,      encoding="""utf-8"""		) as vocab_handle:
                            json.dump(A_  ,      A_		)
                     __UpperCAmelCase     				=WavaVecaPhonemeCTCTokenizer(
                         A_  ,      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=A_  ,      )
                     __UpperCAmelCase     				=True if config.feat_extract_norm == """layer""" else False
                     __UpperCAmelCase     				=WavaVecaFeatureExtractor(
                         feature_size=1  ,      sampling_rate=16000  ,      padding_value=0  ,      do_normalize=A_  ,      return_attention_mask=A_  ,      )
                     __UpperCAmelCase     				=WavaVecaProcessor(feature_extractor=A_  ,      tokenizer=A_		)
                     processor.save_pretrained(A_		)

              __UpperCAmelCase     				=UniSpeechForCTC(A_		)
       else:
              __UpperCAmelCase     				=UniSpeechForPreTraining(A_		)

       if is_finetuned:
              __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]  ,      arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		), """w2v_path""": checkpoint_path}		)
       else:
              __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

       __UpperCAmelCase     				=model[0].eval()

       recursively_load_weights(A_  ,      A_  ,      A_		)

       hf_unispeech.save_pretrained(A_		)


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("--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"
   )
   __A					=	parser.parse_args()
   convert_unispeech_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )












import numpy as np





def 		__UpperCAmelCase     (	a_):
							return 1 / (1 + np.exp(-vector))


if __name__ == "__main__":
	import doctest

	doctest.testmod()





