



'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__a  			=						{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__a  			=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Tuple:
      for attribute in key.split("""."""						):
            snake_case__    :       Optional[int]       		= getattr(_lowerCAmelCase	,  _lowerCAmelCase						)

      if weight_type is not None:
            snake_case__    :       Any       		= getattr(_lowerCAmelCase	,  _lowerCAmelCase						).shape
      else:
            snake_case__    :       Tuple       		= 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__    :       Optional[int]       		= value
      elif weight_type == "weight_g":
            snake_case__    :       int       		= value
      elif weight_type == "weight_v":
            snake_case__    :       Tuple       		= value
      elif weight_type == "bias":
            snake_case__    :       List[Any]       		= value
      elif weight_type == "running_mean":
            snake_case__    :       Tuple       		= value
      elif weight_type == "running_var":
            snake_case__    :       List[str]       		= value
      elif weight_type == "num_batches_tracked":
            snake_case__    :       List[str]       		= value
      elif weight_type == "inv_freq":
            snake_case__    :       Tuple       		= value
      else:
            snake_case__    :       Optional[int]       		= value

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





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Tuple:
      snake_case__    :       Dict       		= []
      snake_case__    :       str       		= fairseq_model.state_dict()

      snake_case__    :       List[str]       		= hf_model.wavaveca_conformer.feature_extractor

      for name, value in fairseq_dict.items():
            snake_case__    :       List[Any]       		= False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  hf_model.config.feat_extract_norm == """group"""	,  )
                  snake_case__    :       Tuple       		= True
            else:
                  for key, mapped_key in MAPPING.items():
                        snake_case__    :       List[Any]       		= """wav2vec2_conformer.""" + 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]:
                              snake_case__    :       Any       		= True
                              if "*" in mapped_key:
                                    snake_case__    :       List[str]       		= name.split(_lowerCAmelCase						)[0].split("""."""						)[-2]
                                    snake_case__    :       Dict       		= mapped_key.replace("""*"""	,  _lowerCAmelCase						)
                              if "pos_bias_u" in name:
                                    snake_case__    :       Optional[int]       		= None
                              elif "pos_bias_v" in name:
                                    snake_case__    :       Union[str, Any]       		= None
                              elif "weight_g" in name:
                                    snake_case__    :       Tuple       		= """weight_g"""
                              elif "weight_v" in name:
                                    snake_case__    :       int       		= """weight_v"""
                              elif "bias" in name:
                                    snake_case__    :       Optional[Any]       		= """bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    snake_case__    :       List[Any]       		= """weight"""
                              elif "running_mean" in name:
                                    snake_case__    :       Any       		= """running_mean"""
                              elif "inv_freq" in name:
                                    snake_case__    :       Any       		= """inv_freq"""
                              elif "running_var" in name:
                                    snake_case__    :       List[Any]       		= """running_var"""
                              elif "num_batches_tracked" in name:
                                    snake_case__    :       str       		= """num_batches_tracked"""
                              else:
                                    snake_case__    :       int       		= 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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					List[Any]:
      snake_case__    :       Tuple       		= full_name.split("""conv_layers."""						)[-1]
      snake_case__    :       str       		= name.split("""."""						)
      snake_case__    :       int       		= int(items[0]						)
      snake_case__    :       Optional[int]       		= 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__    :       Union[str, 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."						)
                  snake_case__    :       List[Any]       		= 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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."						)
                  snake_case__    :       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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."						)
                  snake_case__    :       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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=True						)						->					List[str]:
      if config_path is not None:
            snake_case__    :       Union[str, Any]       		= WavaVecaConformerConfig.from_pretrained(_lowerCAmelCase	,  hidden_act="""swish"""						)
      else:
            snake_case__    :       Optional[int]       		= WavaVecaConformerConfig()

      if "rope" in checkpoint_path:
            snake_case__    :       str       		= """rotary"""

      if is_finetuned:
            if dict_path:
                  snake_case__    :       Optional[int]       		= Dictionary.load(_lowerCAmelCase						)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  snake_case__    :       Optional[Any]       		= target_dict.pad_index
                  snake_case__    :       List[Any]       		= target_dict.bos_index
                  snake_case__    :       str       		= target_dict.eos_index
                  snake_case__    :       Dict       		= len(target_dict.symbols						)
                  snake_case__    :       int       		= os.path.join(_lowerCAmelCase	,  """vocab.json"""						)
                  if not os.path.isdir(_lowerCAmelCase						):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowerCAmelCase						)						)
                        return
                  os.makedirs(_lowerCAmelCase	,  exist_ok=_lowerCAmelCase						)
                  snake_case__    :       int       		= target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  snake_case__    :       Dict       		= 0
                  snake_case__    :       Optional[Any]       		= 1
                  with open(_lowerCAmelCase	,  """w"""	,  encoding="""utf-8"""						) as vocab_handle:
                        json.dump(_lowerCAmelCase	,  _lowerCAmelCase						)
                  snake_case__    :       int       		= WavaVecaCTCTokenizer(
                      _lowerCAmelCase	,  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=_lowerCAmelCase	,  )
                  snake_case__    :       Optional[Any]       		= True if config.feat_extract_norm == """layer""" else False
                  snake_case__    :       Any       		= WavaVecaFeatureExtractor(
                      feature_size=1	,  sampling_rate=16_000	,  padding_value=0	,  do_normalize=_lowerCAmelCase	,  return_attention_mask=_lowerCAmelCase	,  )
                  snake_case__    :       Optional[int]       		= WavaVecaProcessor(feature_extractor=_lowerCAmelCase	,  tokenizer=_lowerCAmelCase						)
                  processor.save_pretrained(_lowerCAmelCase						)

            snake_case__    :       Dict       		= WavaVecaConformerForCTC(_lowerCAmelCase						)
      else:
            snake_case__    :       int       		= WavaVecaConformerForPreTraining(_lowerCAmelCase						)

      if is_finetuned:
            snake_case__		,							snake_case__		,							snake_case__    :       Dict       		= fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]	,  arg_overrides={"""data""": """/""".join(dict_path.split("""/"""						)[:-1]						)}						)
      else:
            snake_case__    :       Union[str, Any]       		= argparse.Namespace(task="""audio_pretraining"""						)
            snake_case__    :       Union[str, Any]       		= fairseq.tasks.setup_task(_lowerCAmelCase						)

            snake_case__		,							snake_case__		,							snake_case__    :       int       		= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,  task=_lowerCAmelCase						)

      snake_case__    :       str       		= model[0].eval()

      recursively_load_weights(_lowerCAmelCase	,  _lowerCAmelCase	,  not is_finetuned						)

      hf_wavavec.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_conformer_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


__a  			=						{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

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