





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()
UpperCAmelCase_					:   int 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   List[Any] 							=						{
    """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""",
}
UpperCAmelCase_					:   List[Any] 							=						[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 							_lowerCAmelCase (  _a   :					List[Any]					,   _a   :					Dict					,   _a   :					Optional[Any]					,   _a   :					Optional[Any]					,   _a   :					Tuple					,   _a   :					Union[str, Any]			)	->			Dict:
     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
                    lowerCAmelCase_					:       Optional[int]    =		"""lm_head"""

          lowerCAmelCase_					:       Optional[int]    =		getattr(_a					,   _a			)

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

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






def 							_lowerCAmelCase (  _a   :					Any					,   _a   :					List[str]					,   _a   :					Dict			)	->			Dict:
     lowerCAmelCase_					:       Tuple    =		[]
     lowerCAmelCase_					:       Optional[int]    =		fairseq_model.state_dict()

     lowerCAmelCase_					:       Optional[Any]    =		hf_model.unispeech.feature_extractor

     for name, value in fairseq_dict.items():
          lowerCAmelCase_					:       Any    =		False
          if "conv_layers" in name:
               load_conv_layer(
                   _a					,   _a					,   _a					,   _a					,   hf_model.config.feat_extract_norm == """group"""					,   )
               lowerCAmelCase_					:       Tuple    =		True
          else:
               for key, mapped_key in MAPPING.items():
                    lowerCAmelCase_					:       Dict    =		"""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]:
                         lowerCAmelCase_					:       Any    =		True
                         if "*" in mapped_key:
                              lowerCAmelCase_					:       List[Any]    =		name.split(_a			)[0].split("""."""			)[-2]
                              lowerCAmelCase_					:       str    =		mapped_key.replace("""*"""					,   _a			)
                         if "weight_g" in name:
                              lowerCAmelCase_					:       Any    =		"""weight_g"""
                         elif "weight_v" in name:
                              lowerCAmelCase_					:       List[Any]    =		"""weight_v"""
                         elif "bias" in name:
                              lowerCAmelCase_					:       Tuple    =		"""bias"""
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowerCAmelCase_					:       Optional[Any]    =		"""weight"""
                         else:
                              lowerCAmelCase_					:       List[Any]    =		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 							_lowerCAmelCase (  _a   :					Any					,   _a   :					Optional[int]					,   _a   :					Any					,   _a   :					Tuple					,   _a   :					Union[str, Any]			)	->			int:
     lowerCAmelCase_					:       Tuple    =		full_name.split("""conv_layers."""			)[-1]
     lowerCAmelCase_					:       Optional[int]    =		name.split("""."""			)
     lowerCAmelCase_					:       List[Any]    =		int(items[0]			)
     lowerCAmelCase_					:       Dict    =		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.'
               )
               lowerCAmelCase_					:       str    =		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.'
               )
               lowerCAmelCase_					:       int    =		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."
               )
               lowerCAmelCase_					:       Optional[Any]    =		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.'
               )
               lowerCAmelCase_					:       Union[str, Any]    =		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 							_lowerCAmelCase (  _a   :					int					,   _a   :					Tuple					,   _a   :					Optional[Any]=None					,   _a   :					Tuple=None					,   _a   :					int=True			)	->			Union[str, Any]:
     if config_path is not None:
          lowerCAmelCase_					:       Any    =		UniSpeechConfig.from_pretrained(_a			)
     else:
          lowerCAmelCase_					:       List[str]    =		UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               lowerCAmelCase_					:       Optional[Any]    =		Dictionary.load_from_json(_a			)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               lowerCAmelCase_					:       str    =		target_dict.pad_index
               lowerCAmelCase_					:       Tuple    =		target_dict.bos_index
               lowerCAmelCase_					:       int    =		target_dict.eos_index
               lowerCAmelCase_					:       Dict    =		len(target_dict.symbols			)
               lowerCAmelCase_					:       Optional[int]    =		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			)
               lowerCAmelCase_					:       List[str]    =		target_dict.indices

               # fairseq has the <pad> and <s> switched
               lowerCAmelCase_					:       List[Any]    =		42
               lowerCAmelCase_					:       List[str]    =		43
               with open(_a					,   """w"""					,   encoding="""utf-8"""			) as vocab_handle:
                    json.dump(_a					,   _a			)
               lowerCAmelCase_					:       Dict    =		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					,   )
               lowerCAmelCase_					:       int    =		True if config.feat_extract_norm == """layer""" else False
               lowerCAmelCase_					:       int    =		WavaVecaFeatureExtractor(
                   feature_size=1					,   sampling_rate=1_60_00					,   padding_value=0					,   do_normalize=_a					,   return_attention_mask=_a					,   )
               lowerCAmelCase_					:       List[Any]    =		WavaVecaProcessor(feature_extractor=_a					,   tokenizer=_a			)
               processor.save_pretrained(_a			)

          lowerCAmelCase_					:       Union[str, Any]    =		UniSpeechForCTC(_a			)
     else:
          lowerCAmelCase_					:       List[str]    =		UniSpeechForPreTraining(_a			)

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

     lowerCAmelCase_					:       Dict    =		model[0].eval()

     recursively_load_weights(_a					,   _a					,   _a			)

     hf_unispeech.save_pretrained(_a			)


if __name__ == "__main__":
      UpperCAmelCase_					:   List[str] 							=						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_					:   Union[str, Any] 							=						parser.parse_args()
      convert_unispeech_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_					:   List[str] 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   int 							=						{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  """bit"""
  __UpperCamelCase	     =  ["""preactivation""", """bottleneck"""]
  __UpperCamelCase	     =  ["""SAME""", """VALID"""]







  def __init__(	self    ,						_lowercase=3    ,						_lowercase=64    ,						_lowercase=[256, 512, 1_024, 2_048]    ,						_lowercase=[3, 4, 6, 3]    ,						_lowercase="preactivation"    ,						_lowercase="relu"    ,						_lowercase=None    ,						_lowercase=32    ,						_lowercase=0.0    ,						_lowercase=False    ,						_lowercase=32    ,						_lowercase=1    ,						_lowercase=None    ,						_lowercase=None    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)
       if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
       if global_padding is not None:
            if global_padding.upper() in self.supported_padding:
                 lowerCAmelCase_					:       int    =		global_padding.upper()
            else:
                 raise ValueError(F'Padding strategy {global_padding} not supported'						)
       lowerCAmelCase_					:       Optional[Any]    =		num_channels
       lowerCAmelCase_					:       List[str]    =		embedding_size
       lowerCAmelCase_					:       Dict    =		hidden_sizes
       lowerCAmelCase_					:       Optional[int]    =		depths
       lowerCAmelCase_					:       List[Any]    =		layer_type
       lowerCAmelCase_					:       Union[str, Any]    =		hidden_act
       lowerCAmelCase_					:       Optional[int]    =		global_padding
       lowerCAmelCase_					:       Optional[Any]    =		num_groups
       lowerCAmelCase_					:       Optional[Any]    =		drop_path_rate
       lowerCAmelCase_					:       Tuple    =		embedding_dynamic_padding
       lowerCAmelCase_					:       Any    =		output_stride
       lowerCAmelCase_					:       List[Any]    =		width_factor

       lowerCAmelCase_					:       Union[str, Any]    =		["""stem"""] + [F'stage{idx}' for idx in range(1    ,						len(_lowercase						) + 1						)]
       lowerCAmelCase_   ,  lowerCAmelCase_					:       Tuple    =		get_aligned_output_features_output_indices(
           out_features=_lowercase    ,						out_indices=_lowercase    ,						stage_names=self.stage_names						)
