




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()
__SCREAMING_SNAKE_CASE      :				Optional[int]				      = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      :				Dict				      = {
    """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_     (					lowerCamelCase:    Any       ,			lowerCamelCase:    Dict       ,			lowerCamelCase:    Any       ,			lowerCamelCase:    Optional[Any]       ,			lowerCamelCase:    List[Any]  )   ->     Union[str, Any]:


      '''simple docstring'''




      for attribute in key.split('.'  ):
            lowerCamelCase__ 					=			getattr(lowerCamelCase       ,			lowerCamelCase  )

      if weight_type is not None:
            lowerCamelCase__ 					=			getattr(lowerCamelCase       ,			lowerCamelCase  ).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_     (					lowerCamelCase:    int       ,			lowerCamelCase:    Tuple       ,			lowerCamelCase:    int  )   ->     List[str]:


      '''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(
                      lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			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(lowerCamelCase  )[0].split('.'  )[-2]
                                    if not layer_index.isnumeric():
                                          continue
                                    lowerCamelCase__ 					=			mapped_key.replace('*'       ,			lowerCamelCase  )
                              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(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
                        continue
            if not is_used:
                  unused_weights.append(lowerCamelCase  )

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

def      lowerCAmelCase_     (					lowerCamelCase:    List[str]       ,			lowerCamelCase:    str       ,			lowerCamelCase:    Any       ,			lowerCamelCase:    str       ,			lowerCamelCase:    List[str]  )   ->     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(lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    Optional[Any]       ,			lowerCamelCase:    Optional[Any]  )   ->     str:


      '''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__ 					=			0.02
      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(lowerCamelCase  )
      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_     (					lowerCamelCase:    List[str]       ,			lowerCamelCase:    List[Any]       ,			lowerCamelCase:    List[str]=None       ,			lowerCamelCase:    List[str]=None       ,			lowerCamelCase:    Optional[Any]=True  )   ->     Optional[int]:


      '''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(lowerCamelCase  )
      else:
            lowerCamelCase__ 					=			convert_config(model[0]       ,			lowerCamelCase  )
      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=lowerCamelCase       ,			return_attention_mask=lowerCamelCase       ,			)

      if is_finetuned:
            if dict_path:
                  lowerCamelCase__ 					=			Dictionary.load(lowerCamelCase  )

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

            lowerCamelCase__ 					=			SEWDForCTC(lowerCamelCase  )
      else:
            lowerCamelCase__ 					=			SEWDModel(lowerCamelCase  )
            feature_extractor.save_pretrained(lowerCamelCase  )

      recursively_load_weights(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )

      hf_model.save_pretrained(lowerCamelCase  )


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE      :				Dict				      = 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"""
      )
      __SCREAMING_SNAKE_CASE      :				Any				      = parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )





def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if right <= left:
            return
      lowerCamelCase__ 					=			lowerCamelCase__ 					=			left
      lowerCamelCase__ 					=			right
      lowerCamelCase__ 					=			sorting[left]
      while i <= b:
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[a]
                  a += 1
                  i += 1
            elif sorting[i] > pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[b]
                  b -= 1
            else:
                  i += 1
      quick_sort_apartition(lowerCamelCase       ,			lowerCamelCase       ,			a - 1  )
      quick_sort_apartition(lowerCamelCase       ,			b + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if left < right:
            lowerCamelCase__ 					=			lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			pivot_index - 1  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			pivot_index + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     int:


      '''simple docstring'''




      lowerCamelCase__ 					=			sorting[right]
      lowerCamelCase__ 					=			left
      for i in range(lowerCamelCase       ,			lowerCamelCase  ):
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[store_index]
                  store_index += 1
      lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[store_index], sorting[right]
      return store_index

def      lowerCAmelCase_     (					lowerCamelCase:    list  )   ->     list:


      '''simple docstring'''




      if len(lowerCamelCase  ) <= 1:
            return sorting
      return (
          three_way_radix_quicksort([i for i in sorting if i < sorting[0]]  )
          + [i for i in sorting if i == sorting[0]]
          + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]  )
      )


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)

      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =input("""Enter numbers separated by a comma:\n""").strip()
      __SCREAMING_SNAKE_CASE      :				str				      =[int(item) for item in user_input.split(""",""")]
      quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
      print(unsorted)