
"""simple docstring"""





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()
_a									=				logging.get_logger(__name__)

_a									=				{
    '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',
}
_a									=				[
    'lm_head',
]







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   for attribute in key.split("."    ):
      UpperCAmelCase_   :   Any     	=       getattr(__lowerCamelCase,						__lowerCamelCase    )

   if weight_type is not None:
      UpperCAmelCase_   :   int     	=       getattr(__lowerCamelCase,						__lowerCamelCase    ).shape
   else:
      UpperCAmelCase_   :   Union[str, 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":
      UpperCAmelCase_   :   Tuple     	=       value
   elif weight_type == "weight_g":
      UpperCAmelCase_   :   Optional[int]     	=       value
   elif weight_type == "weight_v":
      UpperCAmelCase_   :   str     	=       value
   elif weight_type == "bias":
      UpperCAmelCase_   :   Any     	=       value
   else:
      UpperCAmelCase_   :   List[str]     	=       value

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







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   List[Any]     	=       []
   UpperCAmelCase_   :   Any     	=       fairseq_model.state_dict()

   if not is_headless:
      UpperCAmelCase_   :   Any     	=       hf_model.dataavec_audio.feature_extractor
      UpperCAmelCase_   :   int     	=       hf_model.dataavec_audio.encoder.pos_conv_embed

   else:
      UpperCAmelCase_   :   Optional[Any]     	=       hf_model.feature_extractor
      UpperCAmelCase_   :   List[Any]     	=       hf_model.encoder.pos_conv_embed

   for name, value in fairseq_dict.items():
      UpperCAmelCase_   :   Any     	=       False
      if "conv_layers" in name:
         load_conv_layer(
             __lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						)
         UpperCAmelCase_   :   Tuple     	=       True
      elif "pos_conv" in name:
         load_pos_conv_layer(
             __lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						)
         UpperCAmelCase_   :   Dict     	=       True
      else:
         for key, mapped_key in MAPPING.items():
            if not is_headless:
               UpperCAmelCase_   :   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]:
               UpperCAmelCase_   :   Optional[Any]     	=       True
               if "*" in mapped_key:
                  UpperCAmelCase_   :   Any     	=       name.split(__lowerCamelCase    )[0].split("."    )[-2]
                  UpperCAmelCase_   :   Tuple     	=       mapped_key.replace("*",						__lowerCamelCase    )
               if "weight_g" in name:
                  UpperCAmelCase_   :   Optional[int]     	=       "weight_g"
               elif "weight_v" in name:
                  UpperCAmelCase_   :   str     	=       "weight_v"
               elif "bias" in name:
                  UpperCAmelCase_   :   Optional[int]     	=       "bias"
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  UpperCAmelCase_   :   Any     	=       "weight"
               else:
                  UpperCAmelCase_   :   Union[str, 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       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       path.split("."    )
   return reduce(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Any     	=       access_by_string(__lowerCamelCase,						__lowerCamelCase    )
   UpperCAmelCase_   :   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."""    )
   UpperCAmelCase_   :   str     	=       fsq_value
   logger.info(f"""{full_name} was correctly initialized from {hf_weight_path}."""    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       full_name.split("conv_layers."    )[-1]
   UpperCAmelCase_   :   Union[str, Any]     	=       name.split("."    )
   UpperCAmelCase_   :   Tuple     	=       int(items[0]    )
   UpperCAmelCase_   :   Union[str, Any]     	=       int(items[1]    )

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

   set_weights(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						f"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       full_name.split("pos_conv."    )[-1]
   UpperCAmelCase_   :   Any     	=       name.split("."    )
   UpperCAmelCase_   :   List[Any]     	=       int(items[0]    )
   UpperCAmelCase_   :   str     	=       int(items[1]    )

   UpperCAmelCase_   :   Tuple     	=       name.split("."    )[-1]
   if type_id != 0:
      unused_weights.append(__lowerCamelCase    )
      return
   else:
      UpperCAmelCase_   :   Tuple     	=       "conv"

   set_weights(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						f"""layers.{layer_id}.{layer_type}.{weight_type}"""    )







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase=None,						__lowerCamelCase=None,						__lowerCamelCase=True    ):
   if config_path is not None:
      UpperCAmelCase_   :   Dict     	=       DataaVecAudioConfig.from_pretrained(__lowerCamelCase    )
   else:
      UpperCAmelCase_   :   Dict     	=       DataaVecAudioConfig()

   if not is_finetuned:
      # Modify final_proj layer name
      UpperCAmelCase_   :   int     	=       DataaVecAudioModel(__lowerCamelCase    )
      UpperCAmelCase_   :   List[str]     	=       os.path.dirname(__lowerCamelCase    )

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

   def load_dataavec(__lowerCamelCase    ):
      UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       fairseq.checkpoint_utils.load_model_ensemble_and_task([path]    )
      return model[0].eval()

   UpperCAmelCase_   :   Union[str, Any]     	=       load_dataavec(__lowerCamelCase    )

   recursively_load_weights(__lowerCamelCase,						__lowerCamelCase,						not is_finetuned    )

   UpperCAmelCase_   :   int     	=       WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"    )

   UpperCAmelCase_   :   Any     	=       load_dataset("patrickvonplaten/librispeech_asr_dummy",						"clean",						split="validation"    )
   UpperCAmelCase_   :   Optional[Any]     	=       [x["array"] for x in ds[:4]["audio"]]

   UpperCAmelCase_   :   str     	=       processor(__lowerCamelCase,						return_tensors="pt",						padding=__lowerCamelCase    )

   UpperCAmelCase_   :   List[str]     	=       inputs.input_values
   UpperCAmelCase_   :   Union[str, Any]     	=       inputs.attention_mask
   #    input_values = inputs.input_values[:, :-1]
   #    attention_mask = inputs.attention_mask[:, :-1]

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

      UpperCAmelCase_   :   int     	=       torch.argmax(__lowerCamelCase,						dim=-1    )
      UpperCAmelCase_   :   Any     	=       processor.batch_decode(__lowerCamelCase    )

      print(f"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""    )
   else:
      UpperCAmelCase_   :   Tuple     	=       model(source=__lowerCamelCase,						padding_mask=(1 - attention_mask),						mask=__lowerCamelCase,						features_only=__lowerCamelCase    )[
          "layer_results"
      ][-1][0].transpose(0,						1    )
      UpperCAmelCase_   :   Union[str, Any]     	=       hf_wavavec(__lowerCamelCase,						attention_mask=__lowerCamelCase    )["last_hidden_state"]

   print(our_output.shape,						their_output.shape    )
   UpperCAmelCase_   :   Tuple     	=       torch.max(torch.abs(our_output - their_output    )    ).item()
   print(f"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
   UpperCAmelCase_   :   Optional[Any]     	=       torch.allclose(__lowerCamelCase,						__lowerCamelCase,						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(__lowerCamelCase    )

   if is_finetuned:
      processor.save_pretrained(__lowerCamelCase    )
   else:
      processor.feature_extractor.save_pretrained(__lowerCamelCase    )


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_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_tf_available, is_torch_available


UpperCAmelCase__			:  Dict					     =     {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Union[str, Any]					     =     [
      """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """HubertForCTC""",
      """HubertForSequenceClassification""",
      """HubertModel""",
      """HubertPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  List[Any]					     =     [
      """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFHubertForCTC""",
      """TFHubertModel""",
      """TFHubertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_hubert import (
        HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        HubertForCTC,
        HubertForSequenceClassification,
        HubertModel,
        HubertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_hubert import (
        TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFHubertForCTC,
        TFHubertModel,
        TFHubertPreTrainedModel,
    )


else:
  import sys

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




