'''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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__											=     {
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    '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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				str:
      for attribute in key.split("""."""					):
            UpperCamelCase       						=      getattr(__UpperCamelCase	,		__UpperCamelCase					)

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

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







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				str:
      UpperCamelCase       						=      []
      UpperCamelCase       						=      fairseq_model.state_dict()

      UpperCamelCase       						=      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCamelCase       						=      False
            if "conv_layers" in name:
                  load_conv_layer(
                      __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		hf_model.config.feat_extract_norm == """group"""	,		)
                  UpperCamelCase       						=      True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCamelCase       						=      """sew.""" + 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]:
                              UpperCamelCase       						=      True
                              if "*" in mapped_key:
                                    UpperCamelCase       						=      name.split(__UpperCamelCase					)[0].split("""."""					)[-2]
                                    UpperCamelCase       						=      mapped_key.replace("""*"""	,		__UpperCamelCase					)
                              if "weight_g" in name:
                                    UpperCamelCase       						=      """weight_g"""
                              elif "weight_v" in name:
                                    UpperCamelCase       						=      """weight_v"""
                              elif "weight" in name:
                                    UpperCamelCase       						=      """weight"""
                              elif "bias" in name:
                                    UpperCamelCase       						=      """bias"""
                              else:
                                    UpperCamelCase       						=      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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				List[Any]:
      UpperCamelCase       						=      full_name.split("""conv_layers."""					)[-1]
      UpperCamelCase       						=      name.split("""."""					)
      UpperCamelCase       						=      int(items[0]					)
      UpperCamelCase       						=      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."
                  )
                  UpperCamelCase       						=      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."
                  )
                  UpperCamelCase       						=      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."
                  )
                  UpperCamelCase       						=      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."
                  )
                  UpperCamelCase       						=      value
                  logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
      else:
            unused_weights.append(__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
      UpperCamelCase       						=      SEWConfig()
      if is_finetuned:
            UpperCamelCase       						=      model.wav_encoder.wav_model.cfg
      else:
            UpperCamelCase       						=      model.cfg

      UpperCamelCase       						=      fs_config.conv_bias
      UpperCamelCase       						=      eval(fs_config.conv_feature_layers					)
      UpperCamelCase       						=      [x[0] for x in conv_layers]
      UpperCamelCase       						=      [x[1] for x in conv_layers]
      UpperCamelCase       						=      [x[2] for x in conv_layers]
      UpperCamelCase       						=      """gelu"""
      UpperCamelCase       						=      """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
      UpperCamelCase       						=      0.0
      UpperCamelCase       						=      fs_config.activation_fn.name
      UpperCamelCase       						=      fs_config.encoder_embed_dim
      UpperCamelCase       						=      0.02
      UpperCamelCase       						=      fs_config.encoder_ffn_embed_dim
      UpperCamelCase       						=      1E-5
      UpperCamelCase       						=      fs_config.encoder_layerdrop
      UpperCamelCase       						=      fs_config.encoder_attention_heads
      UpperCamelCase       						=      fs_config.conv_pos_groups
      UpperCamelCase       						=      fs_config.conv_pos
      UpperCamelCase       						=      len(__UpperCamelCase					)
      UpperCamelCase       						=      fs_config.encoder_layers
      UpperCamelCase       						=      fs_config.squeeze_factor

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

      UpperCamelCase       						=      """Wav2Vec2FeatureExtractor"""
      UpperCamelCase       						=      """Wav2Vec2CTCTokenizer"""

      return config







@torch.no_grad()
def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		__UpperCamelCase=True					)->				List[str]:

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

      if config_path is not None:
            UpperCamelCase       						=      SEWConfig.from_pretrained(__UpperCamelCase					)
      else:
            UpperCamelCase       						=      convert_config(model[0]	,		__UpperCamelCase					)
      UpperCamelCase       						=      model[0].eval()

      UpperCamelCase       						=      True if config.feat_extract_norm == """layer""" else False
      UpperCamelCase       						=      WavaVecaFeatureExtractor(
          feature_size=1	,		sampling_rate=16000	,		padding_value=0	,		do_normalize=__UpperCamelCase	,		return_attention_mask=__UpperCamelCase	,		)

      if is_finetuned:
            if dict_path:
                  UpperCamelCase       						=      Dictionary.load(__UpperCamelCase					)

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

            UpperCamelCase       						=      SEWForCTC(__UpperCamelCase					)
      else:
            UpperCamelCase       						=      SEWModel(__UpperCamelCase					)
            feature_extractor.save_pretrained(__UpperCamelCase					)

      recursively_load_weights(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)

      hf_model.save_pretrained(__UpperCamelCase					)


if __name__ == "__main__":
     SCREAMING_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'
     )
     SCREAMING_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
     )








'''simple docstring'''

from math import factorial







def 					lowercase__				(   __UpperCamelCase = 20					)->				int:
      UpperCamelCase       						=      2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
      # 2, 3,...
      UpperCamelCase       						=      n // 2

      return int(factorial(__UpperCamelCase					) / (factorial(__UpperCamelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          print(solution(2_0))
     else:
          try:
               SCREAMING_SNAKE_CASE__											=     int(sys.argv[1])
               print(solution(n))
          except ValueError:
               print('Invalid entry - please enter a number.')
