'''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()
SCREAMING_SNAKE_CASE_						 =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						 =      {
    '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',
}
SCREAMING_SNAKE_CASE_						 =      [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 			UpperCamelCase__       (       _lowercase     :					Union[str, Any] ,     _lowercase     :					List[str] ,     _lowercase     :					Dict ,     _lowercase     :					Tuple ,     _lowercase     :					Any  )				->						List[str]:
  for attribute in key.split("""."""  ):
    __UpperCAmelCase:       Optional[Any]		=			getattr(_lowercase ,     _lowercase  )

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

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






def 			UpperCamelCase__       (       _lowercase     :					int ,     _lowercase     :					Union[str, Any] ,     _lowercase     :					str  )				->						List[Any]:
  __UpperCAmelCase:       int		=			[]
  __UpperCAmelCase:       str		=			fairseq_model.state_dict()

  __UpperCAmelCase:       Dict		=			hf_model.wavaveca_conformer.feature_extractor

  for name, value in fairseq_dict.items():
    __UpperCAmelCase:       Any		=			False
    if "conv_layers" in name:
      load_conv_layer(
          _lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     hf_model.config.feat_extract_norm == """group""" ,     )
      __UpperCAmelCase:       Union[str, Any]		=			True
    else:
      for key, mapped_key in MAPPING.items():
        __UpperCAmelCase:       Optional[int]		=			"""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]:
          __UpperCAmelCase:       List[str]		=			True
          if "*" in mapped_key:
            __UpperCAmelCase:       str		=			name.split(_lowercase  )[0].split("""."""  )[-2]
            __UpperCAmelCase:       Any		=			mapped_key.replace("""*""" ,     _lowercase  )
          if "pos_bias_u" in name:
            __UpperCAmelCase:       Optional[Any]		=			None
          elif "pos_bias_v" in name:
            __UpperCAmelCase:       List[Any]		=			None
          elif "weight_g" in name:
            __UpperCAmelCase:       str		=			"""weight_g"""
          elif "weight_v" in name:
            __UpperCAmelCase:       List[str]		=			"""weight_v"""
          elif "bias" in name:
            __UpperCAmelCase:       List[Any]		=			"""bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __UpperCAmelCase:       Optional[Any]		=			"""weight"""
          elif "running_mean" in name:
            __UpperCAmelCase:       Union[str, Any]		=			"""running_mean"""
          elif "inv_freq" in name:
            __UpperCAmelCase:       Optional[int]		=			"""inv_freq"""
          elif "running_var" in name:
            __UpperCAmelCase:       str		=			"""running_var"""
          elif "num_batches_tracked" in name:
            __UpperCAmelCase:       Dict		=			"""num_batches_tracked"""
          else:
            __UpperCAmelCase:       Optional[Any]		=			None
          set_recursively(_lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     _lowercase  )
        continue
    if not is_used:
      unused_weights.append(_lowercase  )

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






def 			UpperCamelCase__       (       _lowercase     :					str ,     _lowercase     :					int ,     _lowercase     :					int ,     _lowercase     :					List[str] ,     _lowercase     :					Tuple  )				->						List[str]:
  __UpperCAmelCase:       List[str]		=			full_name.split("""conv_layers."""  )[-1]
  __UpperCAmelCase:       Dict		=			name.split("""."""  )
  __UpperCAmelCase:       Union[str, Any]		=			int(items[0]  )
  __UpperCAmelCase:       List[str]		=			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:       Dict		=			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:       Optional[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.'''  )
      __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:       str		=			value
      logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
  else:
    unused_weights.append(_lowercase  )






@torch.no_grad()
def 			UpperCamelCase__       (       _lowercase     :					Dict ,     _lowercase     :					List[str] ,     _lowercase     :					Any=None ,     _lowercase     :					str=None ,     _lowercase     :					Optional[int]=True  )				->						Tuple:
  if config_path is not None:
    __UpperCAmelCase:       Optional[int]		=			WavaVecaConformerConfig.from_pretrained(_lowercase ,     hidden_act="""swish"""  )
  else:
    __UpperCAmelCase:       Dict		=			WavaVecaConformerConfig()

  if "rope" in checkpoint_path:
    __UpperCAmelCase:       Dict		=			"""rotary"""

  if is_finetuned:
    if dict_path:
      __UpperCAmelCase:       Dict		=			Dictionary.load(_lowercase  )

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

      # fairseq has the <pad> and <s> switched
      __UpperCAmelCase:       Optional[Any]		=			0
      __UpperCAmelCase:       List[Any]		=			1
      with open(_lowercase ,     """w""" ,     encoding="""utf-8"""  ) as vocab_handle:
        json.dump(_lowercase ,     _lowercase  )
      __UpperCAmelCase:       Tuple		=			WavaVecaCTCTokenizer(
          _lowercase ,     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=_lowercase ,     )
      __UpperCAmelCase:       Any		=			True if config.feat_extract_norm == """layer""" else False
      __UpperCAmelCase:       int		=			WavaVecaFeatureExtractor(
          feature_size=1 ,     sampling_rate=1_6_0_0_0 ,     padding_value=0 ,     do_normalize=_lowercase ,     return_attention_mask=_lowercase ,     )
      __UpperCAmelCase:       List[Any]		=			WavaVecaProcessor(feature_extractor=_lowercase ,     tokenizer=_lowercase  )
      processor.save_pretrained(_lowercase  )

    __UpperCAmelCase:       List[Any]		=			WavaVecaConformerForCTC(_lowercase  )
  else:
    __UpperCAmelCase:       Dict		=			WavaVecaConformerForPreTraining(_lowercase  )

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

    __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase:       str		=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,     task=_lowercase  )

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

  recursively_load_weights(_lowercase ,     _lowercase ,     not is_finetuned  )

  hf_wavavec.save_pretrained(_lowercase  )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_						 =      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'
    )
    SCREAMING_SNAKE_CASE_						 =      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 dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							field(default="""language-modeling"""							,       metadata={"""include_in_asdict_even_if_is_default""": True})

				lowerCAmelCase_     			=							Features({"""text""": Value("""string""")})
				lowerCAmelCase_     			=							Features({})
				lowerCAmelCase_     			=							"text"







				@property
				def 						UpperCAmelCase_    (       self     )->		Dict[str, str]:







											'''simple docstring'''






											return {self.text_column: "text"}




