




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_	 =		logging.get_logger(__name__)

lowerCamelCase_	 =		{
    '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 				SCREAMING_SNAKE_CASE_				(							__A       :				Dict ,  __A       :				int ,  __A       :				Tuple ,  __A       :				Dict ,  __A       :				int						)				->				str:
   for attribute in key.split("."						):
      _SCREAMING_SNAKE_CASE =   getattr(__A ,  __A						)

   if weight_type is not None:
      _SCREAMING_SNAKE_CASE =   getattr(__A ,  __A						).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 				SCREAMING_SNAKE_CASE_				(							__A       :				int ,  __A       :				str ,  __A       :				List[str]						)				->				Tuple:
   _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(
             __A ,  __A ,  __A ,  __A ,  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(__A						)[0].split("."						)[-2]
                  if not layer_index.isnumeric():
                     continue
                  _SCREAMING_SNAKE_CASE =   mapped_key.replace("*" ,  __A						)
               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(__A ,  __A ,  __A ,  __A ,  __A						)
            continue
      if not is_used:
         unused_weights.append(__A						)

   logger.warning(f"""Unused weights: {unused_weights}"""						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int ,  __A       :				Tuple ,  __A       :				Optional[int] ,  __A       :				Any ,  __A       :				Union[str, Any]						)				->				List[str]:
   _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(__A						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple ,  __A       :				Dict						)				->				int:
   _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 =   0.0_2
   _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(__A						)
   _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 				SCREAMING_SNAKE_CASE_				(							__A       :				str ,  __A       :				str ,  __A       :				Dict=None ,  __A       :				Dict=None ,  __A       :				str=True						)				->				Any:

   if is_finetuned:
      _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, _SCREAMING_SNAKE_CASE, _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(__A						)
   else:
      _SCREAMING_SNAKE_CASE =   convert_config(model[0] ,  __A						)
   _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=__A ,  return_attention_mask=__A ,  )

   if is_finetuned:
      if dict_path:
         _SCREAMING_SNAKE_CASE =   Dictionary.load(__A						)

         # 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(__A ,  "vocab.json"						)
         if not os.path.isdir(__A						):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__A						)						)
            return
         os.makedirs(__A ,  exist_ok=__A						)
         with open(__A ,  "w" ,  encoding="utf-8"						) as vocab_handle:
            json.dump(target_dict.indices ,  __A						)
         _SCREAMING_SNAKE_CASE =   WavaVecaCTCTokenizer(
             __A ,  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=__A ,  )
         _SCREAMING_SNAKE_CASE =   WavaVecaProcessor(feature_extractor=__A ,  tokenizer=__A						)
         processor.save_pretrained(__A						)

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

   recursively_load_weights(__A ,  __A ,  __A						)

   hf_model.save_pretrained(__A						)


if __name__ == "__main__":
 lowerCamelCase_	 =		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_	 =		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'''



lowerCamelCase_	 =		(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
lowerCamelCase_	 =		(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False

   # Initialization of sum and number of digits.
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   n
   # Calculation of digits of the number
   while temp > 0:
      number_of_digits += 1
      temp //= 10
   # Dividing number into separate digits and find Armstrong number
   _SCREAMING_SNAKE_CASE =   n
   while temp > 0:
      _SCREAMING_SNAKE_CASE =   temp % 10
      total += rem**number_of_digits
      temp //= 10
   return n == total
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False

   # Init a "histogram" of the digits
   _SCREAMING_SNAKE_CASE =   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   n
   while temp > 0:
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   divmod(__A ,  10						)
      digit_histogram[rem] += 1
      digit_total += 1

   for cnt, i in zip(__A ,  range(len(__A						)						)						):
      total += cnt * i**digit_total

   return n == total
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False
   _SCREAMING_SNAKE_CASE =   len(str(__A						)						)  # the power that all digits will be raised to
   # check if sum of each digit multiplied expo times is equal to number
   return n == sum(int(__A						) ** expo for i in str(__A						)						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   int(input("Enter an integer to see if it is an Armstrong number: "						).strip()						)
   print(f"""{num} is {"" if armstrong_number(__A						) else "not "}an Armstrong number."""						)
   print(f"""{num} is {"" if narcissistic_number(__A						) else "not "}an Armstrong number."""						)
   print(f"""{num} is {"" if pluperfect_number(__A						) else "not "}an Armstrong number."""						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

