


"""simple docstring"""


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   (     lowerCamelCase__	:							Any       ,		lowerCamelCase__	:							Any       ,		lowerCamelCase__	:							Optional[Any]       ,		lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							Optional[int]  ):
 for attribute in key.split("""."""  ):
  lowercase__       :       str				=						getattr(lowerCamelCase__       ,		lowerCamelCase__  )

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

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


def    _lowerCamelCase   (     lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							Optional[Any]  ):
 lowercase__       :       Dict				=						[]
 lowercase__       :       List[str]				=						fairseq_model.state_dict()

 lowercase__       :       int				=						hf_model.unispeech_sat.feature_extractor

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

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


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


@torch.no_grad()
def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							int=None       ,		lowerCamelCase__	:							Tuple=None       ,		lowerCamelCase__	:							Optional[Any]=True  ):
 if config_path is not None:
  lowercase__       :       Optional[Any]				=						UniSpeechSatConfig.from_pretrained(lowerCamelCase__  )
 else:
  lowercase__       :       Optional[Any]				=						UniSpeechSatConfig()

 lowercase__       :       int				=						""""""

 if is_finetuned:
  lowercase__       :       Optional[int]				=						UniSpeechSatForCTC(lowerCamelCase__  )
 else:
  lowercase__       :       List[str]				=						UniSpeechSatForPreTraining(lowerCamelCase__  )

 lowercase__	,       lowercase__	,       lowercase__       :       Tuple				=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]       ,		arg_overrides={"""data""": """/""".join(dict_path.split("""/"""  )[:-1]  )}  )
 lowercase__       :       Optional[Any]				=						model[0].eval()

 recursively_load_weights(lowerCamelCase__       ,		lowerCamelCase__  )

 hf_wavavec.save_pretrained(lowerCamelCase__  )


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"""


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case    					=					{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

   __snake_case    					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)