


from typing import TYPE_CHECKING

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


UpperCAmelCase    =					{"""configuration_mmbt""": ["""MMBTConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase    =					["""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

   UpperCAmelCase    =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

UpperCAmelCase    =					{
    """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""",
}
UpperCAmelCase    =					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      for attribute in key.split('''.'''      ):
            snake_case_ =   getattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

      if weight_type is not None:
            snake_case_ =   getattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

      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(
                      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_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(SCREAMING_SNAKE_CASE      )[0].split('''.'''      )[-2]
                                    snake_case_ =   mapped_key.replace('''*'''      ,      SCREAMING_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(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
                        continue
            if not is_used:
                  unused_weights.append(SCREAMING_SNAKE_CASE      )

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      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(SCREAMING_SNAKE_CASE      )






@torch.no_grad()
def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=True      )->						Dict:




      """simple docstring"""

      if config_path is not None:
            snake_case_ =   UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
      else:
            snake_case_ =   UniSpeechSatConfig()

      snake_case_ =   ''''''

      if is_finetuned:
            snake_case_ =   UniSpeechSatForCTC(SCREAMING_SNAKE_CASE      )
      else:
            snake_case_ =   UniSpeechSatForPreTraining(SCREAMING_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(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

      hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   UpperCAmelCase    =					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    =					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
   )