

'''simple docstring'''






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

snake_case_       =       {
    'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
    'mask_emb': 'masked_spec_embed',
}


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[Any]  ,  SCREAMING_SNAKE_CASE_     :	Optional[int]  ,  SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	Tuple    )							->   str:
      """simple docstring"""

      for attribute in key.split("."    ):
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       getattr(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )

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

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


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[Any]  ,  SCREAMING_SNAKE_CASE_     :	Dict    )							->   Tuple:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			str     =       []
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       fairseq_model.state_dict()

      SCREAMING_SNAKE_CASE_      :			List[str]     =       hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       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"  ,  )
                  SCREAMING_SNAKE_CASE_      :			Tuple     =       True
            else:
                  for key, mapped_key in MAPPING.items():
                        SCREAMING_SNAKE_CASE_      :			int     =       mapped_key

                        if key in name:
                              SCREAMING_SNAKE_CASE_      :			str     =       True
                              if "*" in mapped_key:
                                    SCREAMING_SNAKE_CASE_      :			List[str]     =       name.split(SCREAMING_SNAKE_CASE_    )[0].split("."    )[-2]
                                    SCREAMING_SNAKE_CASE_      :			Tuple     =       mapped_key.replace("*"  ,  SCREAMING_SNAKE_CASE_    )
                              if "weight_g" in name:
                                    SCREAMING_SNAKE_CASE_      :			List[Any]     =       "weight_g"
                              elif "weight_v" in name:
                                    SCREAMING_SNAKE_CASE_      :			Tuple     =       "weight_v"
                              elif "weight" in name:
                                    SCREAMING_SNAKE_CASE_      :			str     =       "weight"
                              elif "bias" in name:
                                    SCREAMING_SNAKE_CASE_      :			str     =       "bias"
                              else:
                                    SCREAMING_SNAKE_CASE_      :			int     =       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 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	List[str]  ,  SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	Optional[int]  ,  SCREAMING_SNAKE_CASE_     :	Union[str, Any]  ,  SCREAMING_SNAKE_CASE_     :	List[str]    )							->   Optional[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       full_name.split("conv_layers."    )[-1]
      SCREAMING_SNAKE_CASE_      :			str     =       name.split("."    )
      SCREAMING_SNAKE_CASE_      :			Any     =       int(items[0]    )
      SCREAMING_SNAKE_CASE_      :			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_      :			Tuple     =       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_      :			str     =       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_      :			Optional[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[Any]     =       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 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	List[str]    )							->   Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       HubertConfig()
      SCREAMING_SNAKE_CASE_      :			Dict     =       model.config

      SCREAMING_SNAKE_CASE_      :			Any     =       fs_config.activation_dropout
      SCREAMING_SNAKE_CASE_      :			Any     =       False
      SCREAMING_SNAKE_CASE_      :			Tuple     =       fs_config.attention_dropout
      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       False
      SCREAMING_SNAKE_CASE_      :			str     =       eval(fs_config.extractor_conv_feature_layers    )
      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       [x[0] for x in conv_layers]
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       [x[1] for x in conv_layers]
      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       [x[2] for x in conv_layers]
      SCREAMING_SNAKE_CASE_      :			int     =       "gelu"
      SCREAMING_SNAKE_CASE_      :			Any     =       "layer" if fs_config.extractor_mode == "layer_norm" else "group"
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       False
      SCREAMING_SNAKE_CASE_      :			List[str]     =       0.0
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       0.0
      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       fs_config.activation_fn
      SCREAMING_SNAKE_CASE_      :			Dict     =       fs_config.dropout
      SCREAMING_SNAKE_CASE_      :			List[str]     =       fs_config.encoder_embed_dim
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       0.02
      SCREAMING_SNAKE_CASE_      :			str     =       fs_config.encoder_ffn_embed_dim
      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       1E-5
      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       0.0
      SCREAMING_SNAKE_CASE_      :			Any     =       fs_config.encoder_attention_heads
      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       fs_config.conv_pos_groups
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       fs_config.conv_pos
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       len(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       fs_config.encoder_layers

      return config


@torch.no_grad()
def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[Any]  ,  SCREAMING_SNAKE_CASE_     :	int=None    )							->   int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Dict     =       distilhubert().model.model

      if config_path is not None:
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_    )
      else:
            SCREAMING_SNAKE_CASE_      :			int     =       convert_config(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			Tuple     =       model.eval()

      SCREAMING_SNAKE_CASE_      :			int     =       WavaVecaFeatureExtractor(
          feature_size=1  ,  sampling_rate=1_6_0_0_0  ,  padding_value=0  ,  do_normalize=SCREAMING_SNAKE_CASE_  ,  return_attention_mask=SCREAMING_SNAKE_CASE_  ,  )
      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       HubertModel(SCREAMING_SNAKE_CASE_    )

      recursively_load_weights(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )

      feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_    )
      hf_model.save_pretrained(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
      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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
      snake_case_       =       parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





'''simple docstring'''






from __future__ import annotations


def 					_snake_case   (     A ,    A       )						->    list[int]:
							lowerCAmelCase__          =	{}
							for index, val in enumerate(A       ):
														lowerCAmelCase__          =	target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														lowerCAmelCase__          =	index
							return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f"""{two_sum([2, 7, 11, 15], 9) = }""")