


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__lowerCamelCase :  List[Any]										=  logging.get_logger(__name__)

__lowerCamelCase :  int										=  {
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
__lowerCamelCase :  Union[str, Any]										=  [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]



def 	A_       (  _lowerCAmelCase  )		->				List[str]:
 UpperCamelCase			:      Any 			=     {}
 with open(_lowerCamelCase							,     "r"  ) as file:
  for line_number, line in enumerate(_lowerCamelCase  ):
   UpperCamelCase			:      Dict 			=     line.strip()
   if line:
    UpperCamelCase			:      Optional[int] 			=     line.split()
    UpperCamelCase			:      Dict 			=     line_number
    UpperCamelCase			:      Dict 			=     words[0]
    UpperCamelCase			:      List[str] 			=     value
 return result



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Tuple:


 for attribute in key.split("."  ):
  UpperCamelCase			:      Dict 			=     getattr(_lowerCamelCase							,     _lowerCamelCase  )

 UpperCamelCase			:      str 			=     None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(_lowerCamelCase  ):
   UpperCamelCase			:      List[str] 			=     PARAM_MAPPING[full_name.split("."  )[-1]]
   UpperCamelCase			:      Union[str, Any] 			=     "param"

 if weight_type is not None and weight_type != "param":
  UpperCamelCase			:      Optional[int] 			=     getattr(_lowerCamelCase							,     _lowerCamelCase  ).shape
 elif weight_type is not None and weight_type == "param":
  UpperCamelCase			:      str 			=     hf_pointer
  for attribute in hf_param_name.split("."  ):
   UpperCamelCase			:      Optional[Any] 			=     getattr(_lowerCamelCase							,     _lowerCamelCase  )
  UpperCamelCase			:      int 			=     shape_pointer.shape

  # let's reduce dimension
  UpperCamelCase			:      List[Any] 			=     value[0]
 else:
  UpperCamelCase			:      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":
  UpperCamelCase			:      int 			=     value
 elif weight_type == "weight_g":
  UpperCamelCase			:      List[Any] 			=     value
 elif weight_type == "weight_v":
  UpperCamelCase			:      List[Any] 			=     value
 elif weight_type == "bias":
  UpperCamelCase			:      Optional[int] 			=     value
 elif weight_type == "param":
  for attribute in hf_param_name.split("."  ):
   UpperCamelCase			:      List[str] 			=     getattr(_lowerCamelCase							,     _lowerCamelCase  )
  UpperCamelCase			:      Dict 			=     value
 else:
  UpperCamelCase			:      Any 			=     value

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



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				str:
 UpperCamelCase			:      Optional[int] 			=     None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(_lowerCamelCase  ):
   UpperCamelCase			:      Union[str, Any] 			=     PARAM_MAPPING[full_name.split("."  )[-1]]
   UpperCamelCase			:      Dict 			=     "param"

 if weight_type is not None and weight_type != "param":
  UpperCamelCase			:      Any 			=     ".".join([key, weight_type]  )
 elif weight_type is not None and weight_type == "param":
  UpperCamelCase			:      List[str] 			=     ".".join([key, hf_param_name]  )
 else:
  UpperCamelCase			:      Optional[Any] 			=     key

 UpperCamelCase			:      int 			=     value if "lm_head" in full_key else value[0]


__lowerCamelCase :  Optional[Any]										=  {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase=None							,     _lowerCAmelCase=None  )		->				Tuple:
 UpperCamelCase			:      List[str] 			=     False
 for key, mapped_key in MAPPING.items():
  UpperCamelCase			:      Optional[Any] 			=     "wav2vec2." + 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			:      List[Any] 			=     True
   if "*" in mapped_key:
    UpperCamelCase			:      Any 			=     name.split(_lowerCamelCase  )[0].split("."  )[-2]
    UpperCamelCase			:      Dict 			=     mapped_key.replace("*"							,     _lowerCamelCase  )
   if "weight_g" in name:
    UpperCamelCase			:      List[str] 			=     "weight_g"
   elif "weight_v" in name:
    UpperCamelCase			:      str 			=     "weight_v"
   elif "bias" in name:
    UpperCamelCase			:      Optional[Any] 			=     "bias"
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    UpperCamelCase			:      List[Any] 			=     "weight"
   else:
    UpperCamelCase			:      Dict 			=     None
   if hf_dict is not None:
    rename_dict(_lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase  )
   else:
    set_recursively(_lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase  )
   return is_used
 return is_used



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Any:
 UpperCamelCase			:      List[Any] 			=     []
 UpperCamelCase			:      Optional[int] 			=     fairseq_model.state_dict()

 UpperCamelCase			:      List[Any] 			=     hf_model.wavaveca.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCamelCase			:      List[Any] 			=     False
  if "conv_layers" in name:
   load_conv_layer(
       _lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase							,     hf_model.config.feat_extract_norm == "group"							,     )
   UpperCamelCase			:      List[str] 			=     True
  else:
   UpperCamelCase			:      Any 			=     load_wavaveca_layer(_lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase  )
  if not is_used:
   unused_weights.append(_lowerCamelCase  )

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



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     full_name.split("conv_layers."  )[-1]
 UpperCamelCase			:      Optional[int] 			=     name.split("."  )
 UpperCamelCase			:      str 			=     int(items[0]  )
 UpperCamelCase			:      Union[str, Any] 			=     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."""  )
   UpperCamelCase			:      List[str] 			=     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."""  )
   UpperCamelCase			:      str 			=     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."""  )
   UpperCamelCase			:      Dict 			=     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."""  )
   UpperCamelCase			:      List[Any] 			=     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 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase=None							,     _lowerCAmelCase=None							,     _lowerCAmelCase=True							,     _lowerCAmelCase=False  )		->				Any:


 if config_path is not None:
  UpperCamelCase			:      Optional[int] 			=     WavaVecaConfig.from_pretrained(_lowerCamelCase  )
 else:
  UpperCamelCase			:      List[str] 			=     WavaVecaConfig()

 if is_seq_class:
  UpperCamelCase			:      Dict 			=     read_txt_into_dict(_lowerCamelCase  )
  UpperCamelCase			:      str 			=     idalabel
  UpperCamelCase			:      Optional[int] 			=     WavaVecaForSequenceClassification(_lowerCamelCase  )
  UpperCamelCase			:      Union[str, Any] 			=     WavaVecaFeatureExtractor(
      feature_size=1							,     sampling_rate=1_6000							,     padding_value=0							,     do_normalize=_lowerCamelCase							,     return_attention_mask=_lowerCamelCase							,     )
  feature_extractor.save_pretrained(_lowerCamelCase  )

 elif is_finetuned:
  if dict_path:
   UpperCamelCase			:      Tuple 			=     Dictionary.load(_lowerCamelCase  )

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   UpperCamelCase			:      Union[str, Any] 			=     target_dict.pad_index
   UpperCamelCase			:      List[Any] 			=     target_dict.bos_index
   UpperCamelCase			:      Dict 			=     target_dict.eos_index
   UpperCamelCase			:      List[str] 			=     len(target_dict.symbols  )
   UpperCamelCase			:      Optional[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  )
   UpperCamelCase			:      Any 			=     target_dict.indices

   # fairseq has the <pad> and <s> switched
   UpperCamelCase			:      List[str] 			=     0
   UpperCamelCase			:      List[Any] 			=     1
   with open(_lowerCamelCase							,     "w"							,     encoding="utf-8"  ) as vocab_handle:
    json.dump(_lowerCamelCase							,     _lowerCamelCase  )
   UpperCamelCase			:      Any 			=     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							,     )
   UpperCamelCase			:      int 			=     True if config.feat_extract_norm == "layer" else False
   UpperCamelCase			:      List[str] 			=     WavaVecaFeatureExtractor(
       feature_size=1							,     sampling_rate=1_6000							,     padding_value=0							,     do_normalize=_lowerCamelCase							,     return_attention_mask=_lowerCamelCase							,     )
   UpperCamelCase			:      Any 			=     WavaVecaProcessor(feature_extractor=_lowerCamelCase							,     tokenizer=_lowerCamelCase  )
   processor.save_pretrained(_lowerCamelCase  )

  UpperCamelCase			:      int 			=     WavaVecaForCTC(_lowerCamelCase  )
 else:
  UpperCamelCase			:      List[Any] 			=     WavaVecaForPreTraining(_lowerCamelCase  )

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

  UpperCamelCase			:      Any 			=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,     task=_lowerCamelCase  )

 UpperCamelCase			:      Dict 			=     model[0].eval()

 recursively_load_weights(_lowerCamelCase							,     _lowerCamelCase							,     not is_finetuned  )

 hf_wavavec.save_pretrained(_lowerCamelCase  )


if __name__ == "__main__":
    __lowerCamelCase :  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"""
    )
    parser.add_argument(
        """--is_seq_class""",
        action="""store_true""",
        help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
    )
    __lowerCamelCase :  List[str]										=  parser.parse_args()

    __lowerCamelCase :  Dict										=  not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase :  int										=  {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  Dict										=  ["""ConvBertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  int										=  [
        """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvBertForMaskedLM""",
        """ConvBertForMultipleChoice""",
        """ConvBertForQuestionAnswering""",
        """ConvBertForSequenceClassification""",
        """ConvBertForTokenClassification""",
        """ConvBertLayer""",
        """ConvBertModel""",
        """ConvBertPreTrainedModel""",
        """load_tf_weights_in_convbert""",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  str										=  [
        """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFConvBertForMaskedLM""",
        """TFConvBertForMultipleChoice""",
        """TFConvBertForQuestionAnswering""",
        """TFConvBertForSequenceClassification""",
        """TFConvBertForTokenClassification""",
        """TFConvBertLayer""",
        """TFConvBertModel""",
        """TFConvBertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
    from .tokenization_convbert import ConvBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_convbert_fast import ConvBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convbert import (
            CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvBertForMaskedLM,
            ConvBertForMultipleChoice,
            ConvBertForQuestionAnswering,
            ConvBertForSequenceClassification,
            ConvBertForTokenClassification,
            ConvBertLayer,
            ConvBertModel,
            ConvBertPreTrainedModel,
            load_tf_weights_in_convbert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convbert import (
            TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFConvBertForMaskedLM,
            TFConvBertForMultipleChoice,
            TFConvBertForQuestionAnswering,
            TFConvBertForSequenceClassification,
            TFConvBertForTokenClassification,
            TFConvBertLayer,
            TFConvBertModel,
            TFConvBertPreTrainedModel,
        )


else:
    import sys

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