

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       :				str         =					logging.get_logger(__name__)

lowerCAmelCase       :				Tuple         =					{
    "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 				A_					(							a   ,				a   ,				a   ,				a   ,				a					):

      """simple docstring"""



      for attribute in key.split('.'					):
            SCREAMING_SNAKE_CASE_			:					str          =       getattr(_lowerCAmelCase   ,				_lowerCAmelCase					)

      if weight_type is not None:
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       getattr(_lowerCAmelCase   ,				_lowerCAmelCase					).shape
      else:
            SCREAMING_SNAKE_CASE_			:					int          =       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_			:					Any          =       value
      elif weight_type == "weight_g":
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       value
      elif weight_type == "weight_v":
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       value
      elif weight_type == "bias":
            SCREAMING_SNAKE_CASE_			:					str          =       value
      else:
            SCREAMING_SNAKE_CASE_			:					Tuple          =       value

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

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       []
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       fairseq_model.state_dict()

      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					int          =       full_name.split('conv_layers.'					)[-1]
      SCREAMING_SNAKE_CASE_			:					List[str]          =       name.split('.'					)
      SCREAMING_SNAKE_CASE_			:					str          =       int(items[0]					)
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       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_			:					Union[str, Any]          =       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_			:					List[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:
                  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_			:					Union[str, Any]          =       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_			:					List[str]          =       value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
      else:
            unused_weights.append(_lowerCAmelCase					)

def 				A_					(							a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       SEWDConfig()
      if is_finetuned:
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       model.wav_encoder.wav_model.cfg
      else:
            SCREAMING_SNAKE_CASE_			:					Dict          =       model.cfg

      SCREAMING_SNAKE_CASE_			:					str          =       fs_config.conv_bias
      SCREAMING_SNAKE_CASE_			:					Dict          =       eval(fs_config.conv_feature_layers					)
      SCREAMING_SNAKE_CASE_			:					List[str]          =       [x[0] for x in conv_layers]
      SCREAMING_SNAKE_CASE_			:					Any          =       [x[1] for x in conv_layers]
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       [x[2] for x in conv_layers]
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       '''gelu'''
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
      SCREAMING_SNAKE_CASE_			:					Dict          =       0.0
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       fs_config.activation_fn.name
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       fs_config.encoder_embed_dim
      SCREAMING_SNAKE_CASE_			:					str          =       0.02
      SCREAMING_SNAKE_CASE_			:					int          =       fs_config.encoder_ffn_embed_dim
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       1e-5
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       fs_config.encoder_layerdrop
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       fs_config.encoder_attention_heads
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       fs_config.conv_pos_groups
      SCREAMING_SNAKE_CASE_			:					Tuple          =       fs_config.conv_pos
      SCREAMING_SNAKE_CASE_			:					Any          =       len(_lowerCAmelCase					)
      SCREAMING_SNAKE_CASE_			:					Any          =       fs_config.encoder_layers
      SCREAMING_SNAKE_CASE_			:					int          =       fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      SCREAMING_SNAKE_CASE_			:					Any          =       fs_config.max_position_embeddings
      SCREAMING_SNAKE_CASE_			:					str          =       fs_config.position_buckets
      SCREAMING_SNAKE_CASE_			:					Tuple          =       fs_config.share_att_key
      SCREAMING_SNAKE_CASE_			:					str          =       fs_config.relative_attention
      SCREAMING_SNAKE_CASE_			:					Dict          =       fs_config.position_biased_input
      SCREAMING_SNAKE_CASE_			:					Any          =       tuple(fs_config.pos_att_type.split('|'					)					)
      SCREAMING_SNAKE_CASE_			:					Any          =       fs_config.norm_rel_ebd

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            SCREAMING_SNAKE_CASE_			:					List[str]          =       model.cfg
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       fs_config.final_dropout
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       fs_config.layerdrop
      SCREAMING_SNAKE_CASE_			:					Any          =       fs_config.activation_dropout
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      SCREAMING_SNAKE_CASE_			:					Tuple          =       fs_config.attention_dropout
      SCREAMING_SNAKE_CASE_			:					Dict          =       fs_config.dropout_input
      SCREAMING_SNAKE_CASE_			:					Dict          =       fs_config.dropout
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       fs_config.mask_channel_length
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       fs_config.mask_channel_prob
      SCREAMING_SNAKE_CASE_			:					Any          =       fs_config.mask_length
      SCREAMING_SNAKE_CASE_			:					Dict          =       fs_config.mask_prob

      SCREAMING_SNAKE_CASE_			:					Any          =       '''Wav2Vec2FeatureExtractor'''
      SCREAMING_SNAKE_CASE_			:					int          =       '''Wav2Vec2CTCTokenizer'''

      return config

@torch.no_grad()
def 				A_					(							a   ,				a   ,				a=None   ,				a=None   ,				a=True					):

      """simple docstring"""



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

      if config_path is not None:
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       SEWDConfig.from_pretrained(_lowerCAmelCase					)
      else:
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       convert_config(model[0]   ,				_lowerCAmelCase					)
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       model[0].eval()

      SCREAMING_SNAKE_CASE_			:					Any          =       True if config.feat_extract_norm == '''layer''' else False
      SCREAMING_SNAKE_CASE_			:					Dict          =       WavaVecaFeatureExtractor(
          feature_size=1   ,				sampling_rate=1_6_0_0_0   ,				padding_value=0   ,				do_normalize=_lowerCAmelCase   ,				return_attention_mask=_lowerCAmelCase   ,				)

      if is_finetuned:
            if dict_path:
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       Dictionary.load(_lowerCAmelCase					)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  SCREAMING_SNAKE_CASE_			:					List[Any]          =       target_dict.pad_index
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       target_dict.bos_index
                  SCREAMING_SNAKE_CASE_			:					List[str]          =       target_dict.pad_index
                  SCREAMING_SNAKE_CASE_			:					Tuple          =       target_dict.bos_index
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       target_dict.eos_index
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       len(target_dict.symbols					)
                  SCREAMING_SNAKE_CASE_			:					int          =       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					)
                  SCREAMING_SNAKE_CASE_			:					List[str]          =       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   ,				)
                  SCREAMING_SNAKE_CASE_			:					Dict          =       WavaVecaProcessor(feature_extractor=_lowerCAmelCase   ,				tokenizer=_lowerCAmelCase					)
                  processor.save_pretrained(_lowerCAmelCase					)

            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       SEWDForCTC(_lowerCAmelCase					)
      else:
            SCREAMING_SNAKE_CASE_			:					int          =       SEWDModel(_lowerCAmelCase					)
            feature_extractor.save_pretrained(_lowerCAmelCase					)

      recursively_load_weights(_lowerCAmelCase   ,				_lowerCAmelCase   ,				_lowerCAmelCase					)

      hf_model.save_pretrained(_lowerCAmelCase					)


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       :				int         =					parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )





from math import factorial

def 				A_					(							a   ,				a					):

      """simple docstring"""



      return factorial(a					) / (factorial(a					) * factorial(n - r					))

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       0

      for i in range(1   ,				1_0_1					):
            for j in range(1   ,				i + 1					):
                  if combinations(a   ,				a					) > 1e6:
                        total += 1
      return total


if __name__ == "__main__":
    print(solution())






