

import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
UpperCAmelCase__			:  Optional[int]					     =     logging.get_logger(__name__)

UpperCAmelCase__			:  List[Any]					     =     {
    """post_extract_proj""": """feature_projection.projection""",
    """models.0.layer_norm""": """feature_projection.layer_norm""",
    """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""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCAmelCase__			:  Tuple					     =     [
    """lm_head""",
]






def        __lowercase   (  _A    ,     _A    ,     _A    ,     _A    ,     _A      )  ->    Any:
      for attribute in key.split("""."""      ):
            SCREAMING_SNAKE_CASE			:   List[str]	  = getattr(_A    ,     _A      )

      if weight_type is not None:
            SCREAMING_SNAKE_CASE			:   int	  = getattr(_A    ,     _A      ).shape
      else:
            SCREAMING_SNAKE_CASE			:   List[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":
            SCREAMING_SNAKE_CASE			:   Any	  = value
      elif weight_type == "weight_g":
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = value
      elif weight_type == "weight_v":
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = value
      elif weight_type == "bias":
            SCREAMING_SNAKE_CASE			:   Any	  = value
      else:
            SCREAMING_SNAKE_CASE			:   Any	  = value

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






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Union[str, Any]:
      SCREAMING_SNAKE_CASE			:   Dict	  = []
      SCREAMING_SNAKE_CASE			:   Dict	  = fairseq_model.state_dict()

      if not is_headless:
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = hf_model.dataavec_audio.feature_extractor
            SCREAMING_SNAKE_CASE			:   List[Any]	  = hf_model.dataavec_audio.encoder.pos_conv_embed

      else:
            SCREAMING_SNAKE_CASE			:   int	  = hf_model.feature_extractor
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = hf_model.encoder.pos_conv_embed

      for name, value in fairseq_dict.items():
            SCREAMING_SNAKE_CASE			:   Any	  = False
            if "conv_layers" in name:
                  load_conv_layer(
                      _A    ,     _A    ,     _A    ,     _A    ,     )
                  SCREAMING_SNAKE_CASE			:   str	  = True
            elif "pos_conv" in name:
                  load_pos_conv_layer(
                      _A    ,     _A    ,     _A    ,     _A    ,     )
                  SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = True
            else:
                  for key, mapped_key in MAPPING.items():
                        if not is_headless:
                              SCREAMING_SNAKE_CASE			:   List[Any]	  = """data2vec_audio.""" + 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]:
                              SCREAMING_SNAKE_CASE			:   Any	  = True
                              if "*" in mapped_key:
                                    SCREAMING_SNAKE_CASE			:   int	  = name.split(_A      )[0].split("""."""      )[-2]
                                    SCREAMING_SNAKE_CASE			:   Dict	  = mapped_key.replace("""*"""    ,     _A      )
                              if "weight_g" in name:
                                    SCREAMING_SNAKE_CASE			:   Optional[int]	  = """weight_g"""
                              elif "weight_v" in name:
                                    SCREAMING_SNAKE_CASE			:   int	  = """weight_v"""
                              elif "bias" in name:
                                    SCREAMING_SNAKE_CASE			:   Tuple	  = """bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    SCREAMING_SNAKE_CASE			:   str	  = """weight"""
                              else:
                                    SCREAMING_SNAKE_CASE			:   Any	  = None
                              set_recursively(_A    ,     _A    ,     _A    ,     _A    ,     _A      )
                        continue
            if not is_used:
                  unused_weights.append(_A      )

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






def        __lowercase   (  _A    ,     _A      )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Dict	  = path.split("""."""      )
      return reduce(_A    ,     _A    ,     _A      )






def        __lowercase   (  _A    ,     _A    ,     _A    ,     _A      )  ->    Union[str, Any]:
      SCREAMING_SNAKE_CASE			:   List[str]	  = access_by_string(_A    ,     _A      )
      SCREAMING_SNAKE_CASE			:   int	  = hf_weight.data

      if fsq_value.shape != hf_value.shape:
            raise ValueError(F"{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."      )
      SCREAMING_SNAKE_CASE			:   int	  = fsq_value
      logger.info(F"{full_name} was correctly initialized from {hf_weight_path}."      )






def        __lowercase   (  _A    ,     _A    ,     _A    ,     _A      )  ->    int:
      SCREAMING_SNAKE_CASE			:   str	  = full_name.split("""conv_layers."""      )[-1]
      SCREAMING_SNAKE_CASE			:   Any	  = name.split("""."""      )
      SCREAMING_SNAKE_CASE			:   str	  = int(items[0]      )
      SCREAMING_SNAKE_CASE			:   str	  = int(items[1]      )

      SCREAMING_SNAKE_CASE			:   int	  = name.split("""."""      )[-1]
      if type_id == 0:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = """conv"""
      elif type_id == 2:
            SCREAMING_SNAKE_CASE			:   str	  = """layer_norm"""
      else:
            unused_weights.append(_A      )
            return

      set_weights(_A    ,     _A    ,     _A    ,     F"conv_layers.{layer_id}.{layer_type}.{weight_type}"      )






def        __lowercase   (  _A    ,     _A    ,     _A    ,     _A      )  ->    List[str]:
      SCREAMING_SNAKE_CASE			:   str	  = full_name.split("""pos_conv."""      )[-1]
      SCREAMING_SNAKE_CASE			:   List[str]	  = name.split("""."""      )
      SCREAMING_SNAKE_CASE			:   Tuple	  = int(items[0]      )
      SCREAMING_SNAKE_CASE			:   str	  = int(items[1]      )

      SCREAMING_SNAKE_CASE			:   int	  = name.split("""."""      )[-1]
      if type_id != 0:
            unused_weights.append(_A      )
            return
      else:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = """conv"""

      set_weights(_A    ,     _A    ,     _A    ,     F"layers.{layer_id}.{layer_type}.{weight_type}"      )






@torch.no_grad()
def        __lowercase   (  _A    ,     _A    ,     _A=None    ,     _A=None    ,     _A=True      )  ->    Any:
      if config_path is not None:
            SCREAMING_SNAKE_CASE			:   Tuple	  = DataaVecAudioConfig.from_pretrained(_A      )
      else:
            SCREAMING_SNAKE_CASE			:   Tuple	  = DataaVecAudioConfig()

      if not is_finetuned:
            # Modify final_proj layer name
            SCREAMING_SNAKE_CASE			:   Any	  = DataaVecAudioModel(_A      )
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = os.path.dirname(_A      )

            SCREAMING_SNAKE_CASE			:   str	  = torch.load(_A      )
            SCREAMING_SNAKE_CASE			:   List[Any]	  = state_dict["""model"""].pop("""final_proj.0.weight"""      )
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = state_dict["""model"""].pop("""final_proj.0.bias"""      )
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = os.path.join(_A    ,     """converted.pt"""      )
            torch.save(_A    ,     _A      )
      else:
            SCREAMING_SNAKE_CASE			:   Dict	  = DataaVecAudioForCTC(_A      )
            SCREAMING_SNAKE_CASE			:   List[Any]	  = checkpoint_path

      def load_dataavec(_A      ):
            SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = fairseq.checkpoint_utils.load_model_ensemble_and_task([path]      )
            return model[0].eval()

      SCREAMING_SNAKE_CASE			:   List[str]	  = load_dataavec(_A      )

      recursively_load_weights(_A    ,     _A    ,     not is_finetuned      )

      SCREAMING_SNAKE_CASE			:   List[str]	  = WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""      )

      SCREAMING_SNAKE_CASE			:   Tuple	  = load_dataset("""patrickvonplaten/librispeech_asr_dummy"""    ,     """clean"""    ,     split="""validation"""      )
      SCREAMING_SNAKE_CASE			:   int	  = [x["""array"""] for x in ds[:4]["""audio"""]]

      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = processor(_A    ,     return_tensors="""pt"""    ,     padding=_A      )

      SCREAMING_SNAKE_CASE			:   Any	  = inputs.input_values
      SCREAMING_SNAKE_CASE			:   Tuple	  = inputs.attention_mask
      #    input_values = inputs.input_values[:, :-1]
      #    attention_mask = inputs.attention_mask[:, :-1]

      hf_wavavec.eval()
      model.eval()
      if is_finetuned:
            SCREAMING_SNAKE_CASE			:   str	  = model(source=_A    ,     padding_mask=(1 - attention_mask)    ,     mask=_A    ,     features_only=_A      )[
                """encoder_out"""
            ].transpose(0    ,     1      )
            SCREAMING_SNAKE_CASE			:   List[str]	  = hf_wavavec(_A    ,     attention_mask=_A      )["""logits"""]

            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = torch.argmax(_A    ,     dim=-1      )
            SCREAMING_SNAKE_CASE			:   int	  = processor.batch_decode(_A      )

            print(F"Expected Output: {ds[:4]['text']}, Pred: {output_string}"      )
      else:
            SCREAMING_SNAKE_CASE			:   str	  = model(source=_A    ,     padding_mask=(1 - attention_mask)    ,     mask=_A    ,     features_only=_A      )[
                """layer_results"""
            ][-1][0].transpose(0    ,     1      )
            SCREAMING_SNAKE_CASE			:   str	  = hf_wavavec(_A    ,     attention_mask=_A      )["""last_hidden_state"""]

      print(our_output.shape    ,     their_output.shape      )
      SCREAMING_SNAKE_CASE			:   int	  = torch.max(torch.abs(our_output - their_output      )      ).item()
      print(F"max_absolute_diff = {max_absolute_diff}"      )  # ~ 1e-7
      SCREAMING_SNAKE_CASE			:   int	  = torch.allclose(_A    ,     _A    ,     atol=1e-3      )
      print("""Do both models output the same tensors?"""    ,     """🔥""" if success else """💩"""      )
      if not success:
            raise Exception("""Something went wRoNg"""      )

      hf_wavavec.save_pretrained(_A      )

      if is_finetuned:
            processor.save_pretrained(_A      )
      else:
            processor.feature_extractor.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__			:  str					     =     parser.parse_args()
  convert_wavaveca_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__			:  Optional[Any]					     =     {
    """configuration_musicgen""": [
        """MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MusicgenConfig""",
        """MusicgenDecoderConfig""",
    ],
    """processing_musicgen""": ["""MusicgenProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Any					     =     [
      """MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MusicgenForConditionalGeneration""",
      """MusicgenForCausalLM""",
      """MusicgenModel""",
      """MusicgenPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_musicgen import (
      MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MusicgenConfig,
      MusicgenDecoderConfig,
  )
  from .processing_musicgen import MusicgenProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_musicgen import (
        MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
        MusicgenForCausalLM,
        MusicgenForConditionalGeneration,
        MusicgenModel,
        MusicgenPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__			:  Dict					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




