'''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()
lowerCAmelCase_ : Any						  =      logging.get_logger(__name__)

lowerCAmelCase_ : str						  =      {
    '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 __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Tuple				)	->							Tuple:
      '''simple docstring'''


      for attribute in key.split("."				):
            lowercase_     =      getattr(_SCREAMING_SNAKE_CASE ,		_SCREAMING_SNAKE_CASE				)

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

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


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						List[str]				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      []
      lowercase_     =      fairseq_model.state_dict()

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

      for name, value in fairseq_dict.items():
            lowercase_     =      False
            if "conv_layers" in name:
                  load_conv_layer(
                      _SCREAMING_SNAKE_CASE ,		_SCREAMING_SNAKE_CASE ,		_SCREAMING_SNAKE_CASE ,		_SCREAMING_SNAKE_CASE ,		hf_model.config.feat_extract_norm == "group" ,		)
                  lowercase_     =      True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowercase_     =      "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]:
                              lowercase_     =      True
                              if "*" in mapped_key:
                                    lowercase_     =      name.split(_SCREAMING_SNAKE_CASE				)[0].split("."				)[-2]
                                    lowercase_     =      mapped_key.replace("*" ,		_SCREAMING_SNAKE_CASE				)
                              if "weight_g" in name:
                                    lowercase_     =      "weight_g"
                              elif "weight_v" in name:
                                    lowercase_     =      "weight_v"
                              elif "weight" in name:
                                    lowercase_     =      "weight"
                              elif "bias" in name:
                                    lowercase_     =      "bias"
                              else:
                                    lowercase_     =      None
                              set_recursively(_SCREAMING_SNAKE_CASE ,		_SCREAMING_SNAKE_CASE ,		_SCREAMING_SNAKE_CASE ,		_SCREAMING_SNAKE_CASE ,		_SCREAMING_SNAKE_CASE				)
                        continue
            if not is_used:
                  unused_weights.append(_SCREAMING_SNAKE_CASE				)

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


def __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						int				)	->							Optional[Any]:
      '''simple docstring'''


      lowercase_     =      full_name.split("conv_layers."				)[-1]
      lowercase_     =      name.split("."				)
      lowercase_     =      int(items[0]				)
      lowercase_     =      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.'
                  )
                  lowercase_     =      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.'
                  )
                  lowercase_     =      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."
                  )
                  lowercase_     =      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.'
                  )
                  lowercase_     =      value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
      else:
            unused_weights.append(_SCREAMING_SNAKE_CASE				)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Optional[int]				)	->							Optional[Any]:
      '''simple docstring'''


      lowercase_     =      SEWConfig()
      if is_finetuned:
            lowercase_     =      model.wav_encoder.wav_model.cfg
      else:
            lowercase_     =      model.cfg

      lowercase_     =      fs_config.conv_bias
      lowercase_     =      eval(fs_config.conv_feature_layers				)
      lowercase_     =      [x[0] for x in conv_layers]
      lowercase_     =      [x[1] for x in conv_layers]
      lowercase_     =      [x[2] for x in conv_layers]
      lowercase_     =      "gelu"
      lowercase_     =      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
      lowercase_     =      0.0
      lowercase_     =      fs_config.activation_fn.name
      lowercase_     =      fs_config.encoder_embed_dim
      lowercase_     =      0.0_2
      lowercase_     =      fs_config.encoder_ffn_embed_dim
      lowercase_     =      1e-5
      lowercase_     =      fs_config.encoder_layerdrop
      lowercase_     =      fs_config.encoder_attention_heads
      lowercase_     =      fs_config.conv_pos_groups
      lowercase_     =      fs_config.conv_pos
      lowercase_     =      len(_SCREAMING_SNAKE_CASE				)
      lowercase_     =      fs_config.encoder_layers
      lowercase_     =      fs_config.squeeze_factor

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

      lowercase_     =      "Wav2Vec2FeatureExtractor"
      lowercase_     =      "Wav2Vec2CTCTokenizer"

      return config


@torch.no_grad()
def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						str=None ,		__lowerCamelCase			:						Optional[int]=None ,		__lowerCamelCase			:						Union[str, Any]=True				)	->							List[Any]:
      '''simple docstring'''


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

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

      lowercase_     =      True if config.feat_extract_norm == "layer" else False
      lowercase_     =      WavaVecaFeatureExtractor(
          feature_size=1 ,		sampling_rate=16_000 ,		padding_value=0 ,		do_normalize=_SCREAMING_SNAKE_CASE ,		return_attention_mask=_SCREAMING_SNAKE_CASE ,		)

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

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowercase_     =      target_dict.pad_index
                  lowercase_     =      target_dict.bos_index
                  lowercase_     =      target_dict.pad_index
                  lowercase_     =      target_dict.bos_index
                  lowercase_     =      target_dict.eos_index
                  lowercase_     =      len(target_dict.symbols				)
                  lowercase_     =      os.path.join(_SCREAMING_SNAKE_CASE ,		"vocab.json"				)
                  if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_SCREAMING_SNAKE_CASE				)				)
                        return
                  os.makedirs(_SCREAMING_SNAKE_CASE ,		exist_ok=_SCREAMING_SNAKE_CASE				)
                  with open(_SCREAMING_SNAKE_CASE ,		"w" ,		encoding="utf-8"				) as vocab_handle:
                        json.dump(target_dict.indices ,		_SCREAMING_SNAKE_CASE				)
                  lowercase_     =      WavaVecaCTCTokenizer(
                      _SCREAMING_SNAKE_CASE ,		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=_SCREAMING_SNAKE_CASE ,		)
                  lowercase_     =      WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE ,		tokenizer=_SCREAMING_SNAKE_CASE				)
                  processor.save_pretrained(_SCREAMING_SNAKE_CASE				)

            lowercase_     =      SEWForCTC(_SCREAMING_SNAKE_CASE				)
      else:
            lowercase_     =      SEWModel(_SCREAMING_SNAKE_CASE				)
            feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE				)

      recursively_load_weights(_SCREAMING_SNAKE_CASE ,		_SCREAMING_SNAKE_CASE ,		_SCREAMING_SNAKE_CASE				)

      hf_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
       lowerCAmelCase_ : 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_ : Any						  =      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'''

def __a (	__lowerCamelCase			:						int = 200				)	->							int:
      '''simple docstring'''


      lowercase_     =      [1, 2, 5, 10, 20, 50, 100, 200]
      lowercase_     =      [0] * (pence + 1)
      lowercase_     =      1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(__lowerCamelCase ,		pence + 1 ,		1				):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73_682






