

'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase				:   Any      				=  logging.get_logger(__name__)

lowerCamelCase				:   Union[str, Any]      				=  {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}


def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  Union[str, Any]							,				_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  Dict       )   ->    Optional[int]:
     """simple docstring"""




     for attribute in key.split('.'       ):
          _SCREAMING_SNAKE_CASE												=getattr(UpperCamelCase__							,				UpperCamelCase__       )

     if weight_type is not None:
          _SCREAMING_SNAKE_CASE												=getattr(UpperCamelCase__							,				UpperCamelCase__       ).shape
     else:
          _SCREAMING_SNAKE_CASE												=hf_pointer.shape

     assert hf_shape == value.shape, (
         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
     else:
          _SCREAMING_SNAKE_CASE												=value

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


def  _lowerCAmelCase      (		_UpperCamelCase		:  Any							,				_UpperCamelCase		:  Any							,				_UpperCamelCase		:  Any       )   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=[]
     _SCREAMING_SNAKE_CASE												=fairseq_model.state_dict()

     _SCREAMING_SNAKE_CASE												=hf_model.sew_d.feature_extractor if is_finetuned else hf_model.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:
               for key, mapped_key in MAPPING.items():
                    _SCREAMING_SNAKE_CASE												="sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") 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]
                              if not layer_index.isnumeric():
                                   continue
                              _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 "weight" in name:
                              _SCREAMING_SNAKE_CASE												="weight"
                         elif "bias" in name:
                              _SCREAMING_SNAKE_CASE												="bias"
                         else:
                              _SCREAMING_SNAKE_CASE												=None
                         set_recursively(UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__       )
                    continue
          if not is_used:
               unused_weights.append(UpperCamelCase__       )

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


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[str]							,				_UpperCamelCase		:  str							,				_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  List[str]       )   ->    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:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   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:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   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:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   f"{full_name} has size {value.shape}, but {feature_extractor[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:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   f"{full_name} has size {value.shape}, but"
                   f" {feature_extractor[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__       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  Any       )   ->    List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=SEWDConfig()
     if is_finetuned:
          _SCREAMING_SNAKE_CASE												=model.wav_encoder.wav_model.cfg
     else:
          _SCREAMING_SNAKE_CASE												=model.cfg

     _SCREAMING_SNAKE_CASE												=fs_config.conv_bias
     _SCREAMING_SNAKE_CASE												=eval(fs_config.conv_feature_layers       )
     _SCREAMING_SNAKE_CASE												=[x[0] for x in conv_layers]
     _SCREAMING_SNAKE_CASE												=[x[1] for x in conv_layers]
     _SCREAMING_SNAKE_CASE												=[x[2] for x in conv_layers]
     _SCREAMING_SNAKE_CASE												="gelu"
     _SCREAMING_SNAKE_CASE												="layer" if fs_config.extractor_mode == "layer_norm" else "group"
     _SCREAMING_SNAKE_CASE												=0.0
     _SCREAMING_SNAKE_CASE												=fs_config.activation_fn.name
     _SCREAMING_SNAKE_CASE												=fs_config.encoder_embed_dim
     _SCREAMING_SNAKE_CASE												=0.02
     _SCREAMING_SNAKE_CASE												=fs_config.encoder_ffn_embed_dim
     _SCREAMING_SNAKE_CASE												=1E-5
     _SCREAMING_SNAKE_CASE												=fs_config.encoder_layerdrop
     _SCREAMING_SNAKE_CASE												=fs_config.encoder_attention_heads
     _SCREAMING_SNAKE_CASE												=fs_config.conv_pos_groups
     _SCREAMING_SNAKE_CASE												=fs_config.conv_pos
     _SCREAMING_SNAKE_CASE												=len(UpperCamelCase__       )
     _SCREAMING_SNAKE_CASE												=fs_config.encoder_layers
     _SCREAMING_SNAKE_CASE												=fs_config.squeeze_factor
     # DeBERTa-specific parameters:
     _SCREAMING_SNAKE_CASE												=fs_config.max_position_embeddings
     _SCREAMING_SNAKE_CASE												=fs_config.position_buckets
     _SCREAMING_SNAKE_CASE												=fs_config.share_att_key
     _SCREAMING_SNAKE_CASE												=fs_config.relative_attention
     _SCREAMING_SNAKE_CASE												=fs_config.position_biased_input
     _SCREAMING_SNAKE_CASE												=tuple(fs_config.pos_att_type.split('|'       )       )
     _SCREAMING_SNAKE_CASE												=fs_config.norm_rel_ebd

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          _SCREAMING_SNAKE_CASE												=model.cfg
          _SCREAMING_SNAKE_CASE												=fs_config.final_dropout
          _SCREAMING_SNAKE_CASE												=fs_config.layerdrop
     _SCREAMING_SNAKE_CASE												=fs_config.activation_dropout
     _SCREAMING_SNAKE_CASE												=fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     _SCREAMING_SNAKE_CASE												=fs_config.attention_dropout
     _SCREAMING_SNAKE_CASE												=fs_config.dropout_input
     _SCREAMING_SNAKE_CASE												=fs_config.dropout
     _SCREAMING_SNAKE_CASE												=fs_config.mask_channel_length
     _SCREAMING_SNAKE_CASE												=fs_config.mask_channel_prob
     _SCREAMING_SNAKE_CASE												=fs_config.mask_length
     _SCREAMING_SNAKE_CASE												=fs_config.mask_prob

     _SCREAMING_SNAKE_CASE												="Wav2Vec2FeatureExtractor"
     _SCREAMING_SNAKE_CASE												="Wav2Vec2CTCTokenizer"

     return config


@torch.no_grad()
def  _lowerCAmelCase      (		_UpperCamelCase		:  Any							,				_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  Tuple=None							,				_UpperCamelCase		:  Union[str, Any]=None							,				_UpperCamelCase		:  List[Any]=True       )   ->    int:
     """simple docstring"""




     if is_finetuned:
          _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												=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

     if config_path is not None:
          _SCREAMING_SNAKE_CASE												=SEWDConfig.from_pretrained(UpperCamelCase__       )
     else:
          _SCREAMING_SNAKE_CASE												=convert_config(model[0]							,				UpperCamelCase__       )
     _SCREAMING_SNAKE_CASE												=model[0].eval()

     _SCREAMING_SNAKE_CASE												=True if config.feat_extract_norm == "layer" else False
     _SCREAMING_SNAKE_CASE												=WavaVecaFeatureExtractor(
         feature_size=1							,				sampling_rate=1_60_00							,				padding_value=0							,				do_normalize=UpperCamelCase__							,				return_attention_mask=UpperCamelCase__							,				)

     if 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.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__       )
               with open(UpperCamelCase__							,				'w'							,				encoding='utf-8'       ) as vocab_handle:
                    json.dump(target_dict.indices							,				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												=WavaVecaProcessor(feature_extractor=UpperCamelCase__							,				tokenizer=UpperCamelCase__       )
               processor.save_pretrained(UpperCamelCase__       )

          _SCREAMING_SNAKE_CASE												=SEWDForCTC(UpperCamelCase__       )
     else:
          _SCREAMING_SNAKE_CASE												=SEWDModel(UpperCamelCase__       )
          feature_extractor.save_pretrained(UpperCamelCase__       )

     recursively_load_weights(UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__       )

     hf_model.save_pretrained(UpperCamelCase__       )


if __name__ == "__main__":
     lowerCamelCase				:   Optional[int]      				=  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(
         "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
     )
     lowerCamelCase				:   List[str]      				=  parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )






'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase				:   Dict      				=  logging.get_logger(__name__)

lowerCamelCase				:   Dict      				=  {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}







class 							A__					( A__						):
  A__					=       'nystromformer'





  def __init__(	self   :			List[str]							,      _a   :			Dict=3_0000							,      _a   :			Dict=768							,      _a   :			Union[str, Any]=12							,      _a   :			Optional[int]=12							,      _a   :			Union[str, Any]=3072							,      _a   :			Optional[Any]="gelu_new"							,      _a   :			Dict=0.1							,      _a   :			Dict=0.1							,      _a   :			Union[str, Any]=510							,      _a   :			Any=2							,      _a   :			List[Any]=64							,      _a   :			int=64							,      _a   :			Optional[Any]=65							,      _a   :			str=False							,      _a   :			List[Any]=0.02							,      _a   :			List[str]=1e-5							,      _a   :			Optional[Any]=1							,      _a   :			Optional[Any]=0							,      _a   :			List[Any]=2							,      **_a   :			List[Any]							,      )			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=vocab_size
       _SCREAMING_SNAKE_CASE												=max_position_embeddings
       _SCREAMING_SNAKE_CASE												=hidden_size
       _SCREAMING_SNAKE_CASE												=num_hidden_layers
       _SCREAMING_SNAKE_CASE												=num_attention_heads
       _SCREAMING_SNAKE_CASE												=intermediate_size
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=type_vocab_size
       _SCREAMING_SNAKE_CASE												=segment_means_seq_len
       _SCREAMING_SNAKE_CASE												=num_landmarks
       _SCREAMING_SNAKE_CASE												=conv_kernel_size
       _SCREAMING_SNAKE_CASE												=inv_coeff_init_option
       _SCREAMING_SNAKE_CASE												=layer_norm_eps
       super().__init__(pad_token_id=_a							,      bos_token_id=_a							,      eos_token_id=_a							,      **_a				)
