

'''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()
A_				  =							logging.get_logger(__name__)

A_				  =							{
    "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_     (       snake_case					,				snake_case					,				snake_case					,				snake_case					,				snake_case       ):
  for attribute in key.split("."       ):
    SCREAMING_SNAKE_CASE:List[Any]  					=    getattr(__lowerCamelCase					,				__lowerCamelCase       )

  if weight_type is not None:
    SCREAMING_SNAKE_CASE:str  					=    getattr(__lowerCamelCase					,				__lowerCamelCase       ).shape
  else:
    SCREAMING_SNAKE_CASE:List[str]  					=    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[Any]  					=    value
  elif weight_type == "weight_v":
    SCREAMING_SNAKE_CASE:str  					=    value
  elif weight_type == "bias":
    SCREAMING_SNAKE_CASE:List[str]  					=    value
  else:
    SCREAMING_SNAKE_CASE:Dict  					=    value

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





def       A_     (       snake_case					,				snake_case					,				snake_case       ):
  SCREAMING_SNAKE_CASE:Tuple  					=    []
  SCREAMING_SNAKE_CASE:Dict  					=    fairseq_model.state_dict()

  SCREAMING_SNAKE_CASE:int  					=    hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    SCREAMING_SNAKE_CASE:int  					=    False
    if "conv_layers" in name:
      load_conv_layer(
          __lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase					,				hf_model.config.feat_extract_norm == "group"					,				)
      SCREAMING_SNAKE_CASE:List[str]  					=    True
    else:
      for key, mapped_key in MAPPING.items():
        SCREAMING_SNAKE_CASE:List[str]  					=    "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]:
          SCREAMING_SNAKE_CASE:List[Any]  					=    True
          if "*" in mapped_key:
            SCREAMING_SNAKE_CASE:List[Any]  					=    name.split(__lowerCamelCase       )[0].split("."       )[-2]
            SCREAMING_SNAKE_CASE:Union[str, Any]  					=    mapped_key.replace("*"					,				__lowerCamelCase       )
          if "weight_g" in name:
            SCREAMING_SNAKE_CASE:str  					=    "weight_g"
          elif "weight_v" in name:
            SCREAMING_SNAKE_CASE:Tuple  					=    "weight_v"
          elif "weight" in name:
            SCREAMING_SNAKE_CASE:Optional[Any]  					=    "weight"
          elif "bias" in name:
            SCREAMING_SNAKE_CASE:Optional[int]  					=    "bias"
          else:
            SCREAMING_SNAKE_CASE:Tuple  					=    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_     (       snake_case					,				snake_case					,				snake_case					,				snake_case					,				snake_case       ):
  SCREAMING_SNAKE_CASE:Optional[int]  					=    full_name.split("conv_layers."       )[-1]
  SCREAMING_SNAKE_CASE:str  					=    name.split("."       )
  SCREAMING_SNAKE_CASE:str  					=    int(items[0]       )
  SCREAMING_SNAKE_CASE:Optional[int]  					=    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:str  					=    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:int  					=    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_     (       snake_case					,				snake_case       ):
  SCREAMING_SNAKE_CASE:Optional[Any]  					=    SEWConfig()
  if is_finetuned:
    SCREAMING_SNAKE_CASE:Dict  					=    model.wav_encoder.wav_model.cfg
  else:
    SCREAMING_SNAKE_CASE:List[str]  					=    model.cfg

  SCREAMING_SNAKE_CASE:Any  					=    fs_config.conv_bias
  SCREAMING_SNAKE_CASE:Optional[int]  					=    eval(fs_config.conv_feature_layers       )
  SCREAMING_SNAKE_CASE:Optional[int]  					=    [x[0] for x in conv_layers]
  SCREAMING_SNAKE_CASE:Dict  					=    [x[1] for x in conv_layers]
  SCREAMING_SNAKE_CASE:Union[str, Any]  					=    [x[2] for x in conv_layers]
  SCREAMING_SNAKE_CASE:Optional[int]  					=    "gelu"
  SCREAMING_SNAKE_CASE:Union[str, 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:Tuple  					=    fs_config.encoder_embed_dim
  SCREAMING_SNAKE_CASE:Union[str, Any]  					=    0.02
  SCREAMING_SNAKE_CASE:Tuple  					=    fs_config.encoder_ffn_embed_dim
  SCREAMING_SNAKE_CASE:List[Any]  					=    1e-5
  SCREAMING_SNAKE_CASE:Optional[Any]  					=    fs_config.encoder_layerdrop
  SCREAMING_SNAKE_CASE:Optional[int]  					=    fs_config.encoder_attention_heads
  SCREAMING_SNAKE_CASE:Optional[Any]  					=    fs_config.conv_pos_groups
  SCREAMING_SNAKE_CASE:str  					=    fs_config.conv_pos
  SCREAMING_SNAKE_CASE:Union[str, Any]  					=    len(__lowerCamelCase       )
  SCREAMING_SNAKE_CASE:List[str]  					=    fs_config.encoder_layers
  SCREAMING_SNAKE_CASE:Any  					=    fs_config.squeeze_factor

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

  SCREAMING_SNAKE_CASE:Optional[Any]  					=    "Wav2Vec2FeatureExtractor"
  SCREAMING_SNAKE_CASE:List[str]  					=    "Wav2Vec2CTCTokenizer"

  return config





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

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

  if config_path is not None:
    SCREAMING_SNAKE_CASE:Optional[Any]  					=    SEWConfig.from_pretrained(__lowerCamelCase       )
  else:
    SCREAMING_SNAKE_CASE:Union[str, Any]  					=    convert_config(model[0]					,				__lowerCamelCase       )
  SCREAMING_SNAKE_CASE:List[str]  					=    model[0].eval()

  SCREAMING_SNAKE_CASE:Optional[int]  					=    True if config.feat_extract_norm == "layer" else False
  SCREAMING_SNAKE_CASE:List[str]  					=    WavaVecaFeatureExtractor(
      feature_size=1					,				sampling_rate=16000					,				padding_value=0					,				do_normalize=__lowerCamelCase					,				return_attention_mask=__lowerCamelCase					,				)

  if is_finetuned:
    if dict_path:
      SCREAMING_SNAKE_CASE:List[Any]  					=    Dictionary.load(__lowerCamelCase       )

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

    SCREAMING_SNAKE_CASE:List[str]  					=    SEWForCTC(__lowerCamelCase       )
  else:
    SCREAMING_SNAKE_CASE:List[Any]  					=    SEWModel(__lowerCamelCase       )
    feature_extractor.save_pretrained(__lowerCamelCase       )

  recursively_load_weights(__lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase       )

  hf_model.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
    A_				  =							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"
    )
    A_				  =							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 sqrt
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 1_00_00_00		)     ->					int:
  _snake_case					     =       0
  _snake_case					     =       0
  _snake_case					     =       42

  while num_cuboids <= limit:
    max_cuboid_size += 1
    for sum_shortest_sides in range(2 ,					2 * max_cuboid_size + 1		):
      if sqrt(sum_shortest_sides**2 + max_cuboid_size**2		).is_integer():
        num_cuboids += (
            min(__lowerCamelCase ,					sum_shortest_sides // 2		)
            - max(1 ,					sum_shortest_sides - max_cuboid_size		)
            + 1
        )

  return max_cuboid_size


if __name__ == "__main__":
 print(F"{solution() = }")



