
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()
_snake_case		  =   logging.get_logger(__name__)

_snake_case		  =   {
    "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_       (			A			:      Dict		,					A			:      Optional[Any]		,					A			:      Tuple		,					A			:      Tuple		,					A			:      Any						):




      """simple docstring"""






      for attribute in key.split('''.'''						):
            lowerCAmelCase_						=      getattr(A_		,					A_						)

      if weight_type is not None:
            lowerCAmelCase_						=      getattr(A_		,					A_						).shape
      else:
            lowerCAmelCase_						=      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":
            lowerCAmelCase_						=      value
      elif weight_type == "weight_g":
            lowerCAmelCase_						=      value
      elif weight_type == "weight_v":
            lowerCAmelCase_						=      value
      elif weight_type == "bias":
            lowerCAmelCase_						=      value
      else:
            lowerCAmelCase_						=      value

      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'						)
def lowerCamelCase_       (			A			:      List[str]		,					A			:      str		,					A			:      Tuple						):




      """simple docstring"""
      lowerCAmelCase_						=      []
      lowerCAmelCase_						=      fairseq_model.state_dict()

      lowerCAmelCase_						=      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowerCAmelCase_						=      False
            if "conv_layers" in name:
                  load_conv_layer(
                      A_		,					A_		,					A_		,					A_		,					hf_model.config.feat_extract_norm == '''group'''		,					)
                  lowerCAmelCase_						=      True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowerCAmelCase_						=      "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]:
                              lowerCAmelCase_						=      True
                              if "*" in mapped_key:
                                    lowerCAmelCase_						=      name.split(A_						)[0].split('''.'''						)[-2]
                                    if not layer_index.isnumeric():
                                          continue
                                    lowerCAmelCase_						=      mapped_key.replace('''*'''		,					A_						)
                              if "weight_g" in name:
                                    lowerCAmelCase_						=      "weight_g"
                              elif "weight_v" in name:
                                    lowerCAmelCase_						=      "weight_v"
                              elif "weight" in name:
                                    lowerCAmelCase_						=      "weight"
                              elif "bias" in name:
                                    lowerCAmelCase_						=      "bias"
                              else:
                                    lowerCAmelCase_						=      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 lowerCamelCase_       (			A			:      Union[str, Any]		,					A			:      List[Any]		,					A			:      Tuple		,					A			:      List[str]		,					A			:      Dict						):




      """simple docstring"""
      lowerCAmelCase_						=      full_name.split('''conv_layers.'''						)[-1]
      lowerCAmelCase_						=      name.split('''.'''						)
      lowerCAmelCase_						=      int(items[0]						)
      lowerCAmelCase_						=      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.'
                  )
                  lowerCAmelCase_						=      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.'
                  )
                  lowerCAmelCase_						=      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."
                  )
                  lowerCAmelCase_						=      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.'
                  )
                  lowerCAmelCase_						=      value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
      else:
            unused_weights.append(A_						)
def lowerCamelCase_       (			A			:      List[str]		,					A			:      Tuple						):




      """simple docstring"""
      lowerCAmelCase_						=      SEWDConfig()
      if is_finetuned:
            lowerCAmelCase_						=      model.wav_encoder.wav_model.cfg
      else:
            lowerCAmelCase_						=      model.cfg

      lowerCAmelCase_						=      fs_config.conv_bias
      lowerCAmelCase_						=      eval(fs_config.conv_feature_layers						)
      lowerCAmelCase_						=      [x[0] for x in conv_layers]
      lowerCAmelCase_						=      [x[1] for x in conv_layers]
      lowerCAmelCase_						=      [x[2] for x in conv_layers]
      lowerCAmelCase_						=      "gelu"
      lowerCAmelCase_						=      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
      lowerCAmelCase_						=      0.0
      lowerCAmelCase_						=      fs_config.activation_fn.name
      lowerCAmelCase_						=      fs_config.encoder_embed_dim
      lowerCAmelCase_						=      0.0_2
      lowerCAmelCase_						=      fs_config.encoder_ffn_embed_dim
      lowerCAmelCase_						=      1e-5
      lowerCAmelCase_						=      fs_config.encoder_layerdrop
      lowerCAmelCase_						=      fs_config.encoder_attention_heads
      lowerCAmelCase_						=      fs_config.conv_pos_groups
      lowerCAmelCase_						=      fs_config.conv_pos
      lowerCAmelCase_						=      len(A_						)
      lowerCAmelCase_						=      fs_config.encoder_layers
      lowerCAmelCase_						=      fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      lowerCAmelCase_						=      fs_config.max_position_embeddings
      lowerCAmelCase_						=      fs_config.position_buckets
      lowerCAmelCase_						=      fs_config.share_att_key
      lowerCAmelCase_						=      fs_config.relative_attention
      lowerCAmelCase_						=      fs_config.position_biased_input
      lowerCAmelCase_						=      tuple(fs_config.pos_att_type.split('''|'''						)						)
      lowerCAmelCase_						=      fs_config.norm_rel_ebd

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

      lowerCAmelCase_						=      "Wav2Vec2FeatureExtractor"
      lowerCAmelCase_						=      "Wav2Vec2CTCTokenizer"

      return config
@torch.no_grad()
def lowerCamelCase_       (			A			:      Tuple		,					A			:      Dict		,					A			:      Tuple=None		,					A			:      Tuple=None		,					A			:      int=True						):




      """simple docstring"""






      if is_finetuned:
            lowerCAmelCase_						=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]		,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
      else:
            lowerCAmelCase_						=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

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

      lowerCAmelCase_						=      True if config.feat_extract_norm == "layer" else False
      lowerCAmelCase_						=      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:
                  lowerCAmelCase_						=      Dictionary.load(A_						)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowerCAmelCase_						=      target_dict.pad_index
                  lowerCAmelCase_						=      target_dict.bos_index
                  lowerCAmelCase_						=      target_dict.pad_index
                  lowerCAmelCase_						=      target_dict.bos_index
                  lowerCAmelCase_						=      target_dict.eos_index
                  lowerCAmelCase_						=      len(target_dict.symbols						)
                  lowerCAmelCase_						=      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_						)
                  lowerCAmelCase_						=      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_		,					)
                  lowerCAmelCase_						=      WavaVecaProcessor(feature_extractor=A_		,					tokenizer=A_						)
                  processor.save_pretrained(A_						)

            lowerCAmelCase_						=      SEWDForCTC(A_						)
      else:
            lowerCAmelCase_						=      SEWDModel(A_						)
            feature_extractor.save_pretrained(A_						)

      recursively_load_weights(A_		,					A_		,					A_						)

      hf_model.save_pretrained(A_						)


if __name__ == "__main__":
 _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(
     "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 _snake_case		  =   parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )







from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap



@total_ordering
class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						None:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=word
         UpperCamelCase__ :			Dict 						=count




      def __eq__(		self   ,			__SCREAMING_SNAKE_CASE)					->						bool:







         """simple docstring"""







         if not isinstance(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
            return NotImplemented
         return self.count == other.count




      def __lt__(		self   ,			__SCREAMING_SNAKE_CASE)					->						bool:







         """simple docstring"""







         if not isinstance(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
            return NotImplemented
         return self.count < other.count




def 	_lowerCamelCase  (		A_  :	list[str]							,  A_  :	int      )			->			list[str]:

   '''simple docstring'''






   UpperCamelCase__ :			Heap[WordCount] 						=Heap()
   UpperCamelCase__ :			Any 						=Counter(A_      )
   heap.build_max_heap(
       [WordCount(A_							,  A_      ) for word, count in count_by_word.items()]      )
   return [heap.extract_max().word for _ in range(min(A_							,  len(A_      )      )      )]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

