





'''simple docstring'''

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()
a        =						logging.get_logger(__name__)

a        =						{
    "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",
}
a        =						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]

def 						__magic_name__       (							__UpperCAmelCase      )						->					Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       {}
   with open(__UpperCAmelCase							,	"""r"""      ) as file:
      for line_number, line in enumerate(__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       line.strip()
         if line:
            __SCREAMING_SNAKE_CASE      							=       line.split()
            __SCREAMING_SNAKE_CASE      							=       line_number
            __SCREAMING_SNAKE_CASE      							=       words[0]
            __SCREAMING_SNAKE_CASE      							=       value
   return result

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   for attribute in key.split("""."""      ):
      __SCREAMING_SNAKE_CASE      							=       getattr(__UpperCAmelCase							,	__UpperCAmelCase      )

   __SCREAMING_SNAKE_CASE      							=       None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       PARAM_MAPPING[full_name.split("""."""      )[-1]]
         __SCREAMING_SNAKE_CASE      							=       """param"""

   if weight_type is not None and weight_type != "param":
      __SCREAMING_SNAKE_CASE      							=       getattr(__UpperCAmelCase							,	__UpperCAmelCase      ).shape
   elif weight_type is not None and weight_type == "param":
      __SCREAMING_SNAKE_CASE      							=       hf_pointer
      for attribute in hf_param_name.split("""."""      ):
         __SCREAMING_SNAKE_CASE      							=       getattr(__UpperCAmelCase							,	__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       shape_pointer.shape

      # let's reduce dimension
      __SCREAMING_SNAKE_CASE      							=       value[0]
   else:
      __SCREAMING_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":
      __SCREAMING_SNAKE_CASE      							=       value
   elif weight_type == "weight_g":
      __SCREAMING_SNAKE_CASE      							=       value
   elif weight_type == "weight_v":
      __SCREAMING_SNAKE_CASE      							=       value
   elif weight_type == "bias":
      __SCREAMING_SNAKE_CASE      							=       value
   elif weight_type == "param":
      for attribute in hf_param_name.split("""."""      ):
         __SCREAMING_SNAKE_CASE      							=       getattr(__UpperCAmelCase							,	__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       value
   else:
      __SCREAMING_SNAKE_CASE      							=       value

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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       PARAM_MAPPING[full_name.split("""."""      )[-1]]
         __SCREAMING_SNAKE_CASE      							=       """param"""

   if weight_type is not None and weight_type != "param":
      __SCREAMING_SNAKE_CASE      							=       """.""".join([key, weight_type]      )
   elif weight_type is not None and weight_type == "param":
      __SCREAMING_SNAKE_CASE      							=       """.""".join([key, hf_param_name]      )
   else:
      __SCREAMING_SNAKE_CASE      							=       key

   __SCREAMING_SNAKE_CASE      							=       value if """lm_head""" in full_key else value[0]


a        =						{
    "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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=None							,	__UpperCAmelCase=None      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       False
   for key, mapped_key in MAPPING.items():
      __SCREAMING_SNAKE_CASE      							=       """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]:
         __SCREAMING_SNAKE_CASE      							=       True
         if "*" in mapped_key:
            __SCREAMING_SNAKE_CASE      							=       name.split(__UpperCAmelCase      )[0].split("""."""      )[-2]
            __SCREAMING_SNAKE_CASE      							=       mapped_key.replace("""*"""							,	__UpperCAmelCase      )
         if "weight_g" in name:
            __SCREAMING_SNAKE_CASE      							=       """weight_g"""
         elif "weight_v" in name:
            __SCREAMING_SNAKE_CASE      							=       """weight_v"""
         elif "bias" in name:
            __SCREAMING_SNAKE_CASE      							=       """bias"""
         elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __SCREAMING_SNAKE_CASE      							=       """weight"""
         else:
            __SCREAMING_SNAKE_CASE      							=       None
         if hf_dict is not None:
            rename_dict(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )
         else:
            set_recursively(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )
         return is_used
   return is_used

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   __SCREAMING_SNAKE_CASE      							=       fairseq_model.state_dict()

   __SCREAMING_SNAKE_CASE      							=       hf_model.wavaveca.feature_extractor

   for name, value in fairseq_dict.items():
      __SCREAMING_SNAKE_CASE      							=       False
      if "conv_layers" in name:
         load_conv_layer(
             __UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	hf_model.config.feat_extract_norm == """group"""							,	)
         __SCREAMING_SNAKE_CASE      							=       True
      else:
         __SCREAMING_SNAKE_CASE      							=       load_wavaveca_layer(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )
      if not is_used:
         unused_weights.append(__UpperCAmelCase      )

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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       full_name.split("""conv_layers."""      )[-1]
   __SCREAMING_SNAKE_CASE      							=       name.split("""."""      )
   __SCREAMING_SNAKE_CASE      							=       int(items[0]      )
   __SCREAMING_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."""      )
         __SCREAMING_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."""      )
         __SCREAMING_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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""      )
         __SCREAMING_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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""      )
         __SCREAMING_SNAKE_CASE      							=       value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
   else:
      unused_weights.append(__UpperCAmelCase      )

@torch.no_grad()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=None							,	__UpperCAmelCase=None							,	__UpperCAmelCase=True							,	__UpperCAmelCase=False      )						->					List[Any]:

   '''simple docstring'''







   if config_path is not None:
      __SCREAMING_SNAKE_CASE      							=       WavaVecaConfig.from_pretrained(__UpperCAmelCase      )
   else:
      __SCREAMING_SNAKE_CASE      							=       WavaVecaConfig()

   if is_seq_class:
      __SCREAMING_SNAKE_CASE      							=       read_txt_into_dict(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       idalabel
      __SCREAMING_SNAKE_CASE      							=       WavaVecaForSequenceClassification(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       WavaVecaFeatureExtractor(
          feature_size=1							,	sampling_rate=16000							,	padding_value=0							,	do_normalize=__UpperCAmelCase							,	return_attention_mask=__UpperCAmelCase							,	)
      feature_extractor.save_pretrained(__UpperCAmelCase      )

   elif is_finetuned:
      if dict_path:
         __SCREAMING_SNAKE_CASE      							=       Dictionary.load(__UpperCAmelCase      )

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

         # fairseq has the <pad> and <s> switched
         __SCREAMING_SNAKE_CASE      							=       0
         __SCREAMING_SNAKE_CASE      							=       1
         with open(__UpperCAmelCase							,	"""w"""							,	encoding="""utf-8"""      ) as vocab_handle:
            json.dump(__UpperCAmelCase							,	__UpperCAmelCase      )
         __SCREAMING_SNAKE_CASE      							=       WavaVecaCTCTokenizer(
             __UpperCAmelCase							,	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=__UpperCAmelCase							,	)
         __SCREAMING_SNAKE_CASE      							=       True if config.feat_extract_norm == """layer""" else False
         __SCREAMING_SNAKE_CASE      							=       WavaVecaFeatureExtractor(
             feature_size=1							,	sampling_rate=16000							,	padding_value=0							,	do_normalize=__UpperCAmelCase							,	return_attention_mask=__UpperCAmelCase							,	)
         __SCREAMING_SNAKE_CASE      							=       WavaVecaProcessor(feature_extractor=__UpperCAmelCase							,	tokenizer=__UpperCAmelCase      )
         processor.save_pretrained(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE      							=       WavaVecaForCTC(__UpperCAmelCase      )
   else:
      __SCREAMING_SNAKE_CASE      							=       WavaVecaForPreTraining(__UpperCAmelCase      )

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

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,	task=__UpperCAmelCase      )

   __SCREAMING_SNAKE_CASE      							=       model[0].eval()

   recursively_load_weights(__UpperCAmelCase							,	__UpperCAmelCase							,	not is_finetuned      )

   hf_wavavec.save_pretrained(__UpperCAmelCase      )


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"
    )
    parser.add_argument(
        "--is_seq_class",
        action="store_true",
        help="Whether the model to convert is a fine-tuned sequence classification model or not",
    )
    a        =						parser.parse_args()

    a        =						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,
    )












'''simple docstring'''

import flax.linen as nn
import jax
import jax.numpy as jnp







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[Any]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       hidden_states.shape
      __SCREAMING_SNAKE_CASE      							=       jax.image.resize(
          lowerCamelCase				,shape=(batch, height * 2, width * 2, channels)				,method="""nearest"""				,)
      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(2, 2)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : int	      =	None
   __UpperCamelCase   : float	      =	0.0
   __UpperCamelCase   : bool	      =	None
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.in_channels if self.out_channels is None else self.out_channels

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       nn.Dense(lowerCamelCase				,dtype=self.dtype    )

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Dropout(self.dropout_prob    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

      __SCREAMING_SNAKE_CASE      							=       None
      if use_nin_shortcut:
         __SCREAMING_SNAKE_CASE      							=       nn.Conv(
             lowerCamelCase				,kernel_size=(1, 1)				,strides=(1, 1)				,padding="""VALID"""				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			Union[str, Any]=True    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       hidden_states
      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       self.time_emb_proj(nn.swish(lowerCamelCase    )    )
      __SCREAMING_SNAKE_CASE      							=       jnp.expand_dims(jnp.expand_dims(lowerCamelCase				,1    )				,1    )
      __SCREAMING_SNAKE_CASE      							=       hidden_states + temb

      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.dropout(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      if self.conv_shortcut is not None:
         __SCREAMING_SNAKE_CASE      							=       self.conv_shortcut(lowerCamelCase    )

      return hidden_states + residual





