





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__lowercase							:Optional[Any]							=	logging.get_logger(__name__)

__lowercase							:Any							=	{
    "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    UpperCAmelCase				(  _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						for attribute in key.split("."  ):
												SCREAMING_SNAKE_CASE__  :					Dict =       getattr(_lowerCamelCase      , _lowerCamelCase  )

						if weight_type is not None:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       getattr(_lowerCamelCase      , _lowerCamelCase  ).shape
						else:
												SCREAMING_SNAKE_CASE__  :					Tuple =       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__  :					Dict =       value
						elif weight_type == "weight_g":
												SCREAMING_SNAKE_CASE__  :					Any =       value
						elif weight_type == "weight_v":
												SCREAMING_SNAKE_CASE__  :					Dict =       value
						elif weight_type == "bias":
												SCREAMING_SNAKE_CASE__  :					Any =       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    UpperCAmelCase				(  _lowerCamelCase   :	str      , _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       []
						SCREAMING_SNAKE_CASE__  :					int =       fairseq_model.state_dict()

						SCREAMING_SNAKE_CASE__  :					Tuple =       hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												SCREAMING_SNAKE_CASE__  :					Optional[int] =       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase      , hf_model.config.feat_extract_norm == "group"      , )
																		SCREAMING_SNAKE_CASE__  :					Optional[int] =       True
												else:
																		for key, mapped_key in MAPPING.items():
																								SCREAMING_SNAKE_CASE__  :					Dict =       mapped_key

																								if key in name:
																														SCREAMING_SNAKE_CASE__  :					int =       True
																														if "*" in mapped_key:
																																				SCREAMING_SNAKE_CASE__  :					Any =       name.split(_lowerCamelCase  )[0].split("."  )[-2]
																																				SCREAMING_SNAKE_CASE__  :					int =       mapped_key.replace("*"      , _lowerCamelCase  )
																														if "weight_g" in name:
																																				SCREAMING_SNAKE_CASE__  :					Dict =       """weight_g"""
																														elif "weight_v" in name:
																																				SCREAMING_SNAKE_CASE__  :					Dict =       """weight_v"""
																														elif "weight" in name:
																																				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       """weight"""
																														elif "bias" in name:
																																				SCREAMING_SNAKE_CASE__  :					Optional[int] =       """bias"""
																														else:
																																				SCREAMING_SNAKE_CASE__  :					List[str] =       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    UpperCAmelCase				(  _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	List[Any]      , _lowerCamelCase   :	List[str]      , _lowerCamelCase   :	str  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       full_name.split("conv_layers."  )[-1]
						SCREAMING_SNAKE_CASE__  :					int =       name.split("."  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       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__  :					int =       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__  :					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__  :					int =       value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
						else:
												unused_weights.append(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       HubertConfig()
						SCREAMING_SNAKE_CASE__  :					List[str] =       model.config

						SCREAMING_SNAKE_CASE__  :					int =       fs_config.activation_dropout
						SCREAMING_SNAKE_CASE__  :					Tuple =       False
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       fs_config.attention_dropout
						SCREAMING_SNAKE_CASE__  :					str =       False
						SCREAMING_SNAKE_CASE__  :					List[Any] =       eval(fs_config.extractor_conv_feature_layers  )
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       [x[0] for x in conv_layers]
						SCREAMING_SNAKE_CASE__  :					str =       [x[1] for x in conv_layers]
						SCREAMING_SNAKE_CASE__  :					str =       [x[2] for x in conv_layers]
						SCREAMING_SNAKE_CASE__  :					Any =       """gelu"""
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       False
						SCREAMING_SNAKE_CASE__  :					Tuple =       0.0
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       0.0
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       fs_config.activation_fn
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       fs_config.dropout
						SCREAMING_SNAKE_CASE__  :					Any =       fs_config.encoder_embed_dim
						SCREAMING_SNAKE_CASE__  :					List[Any] =       0.0_2
						SCREAMING_SNAKE_CASE__  :					List[Any] =       fs_config.encoder_ffn_embed_dim
						SCREAMING_SNAKE_CASE__  :					List[Any] =       1E-5
						SCREAMING_SNAKE_CASE__  :					Dict =       0.0
						SCREAMING_SNAKE_CASE__  :					List[Any] =       fs_config.encoder_attention_heads
						SCREAMING_SNAKE_CASE__  :					List[str] =       fs_config.conv_pos_groups
						SCREAMING_SNAKE_CASE__  :					List[Any] =       fs_config.conv_pos
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       len(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       fs_config.encoder_layers

						return config







@torch.no_grad()
def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	str=None  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       distilhubert().model.model

						if config_path is not None:
												SCREAMING_SNAKE_CASE__  :					List[str] =       HubertConfig.from_pretrained(_lowerCamelCase  )
						else:
												SCREAMING_SNAKE_CASE__  :					List[str] =       convert_config(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       model.eval()

						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       WavaVecaFeatureExtractor(
						    feature_size=1      , sampling_rate=16_000      , padding_value=0      , do_normalize=_lowerCamelCase      , return_attention_mask=_lowerCamelCase      , )
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       HubertModel(_lowerCamelCase  )

						recursively_load_weights(_lowerCamelCase      , _lowerCamelCase  )

						feature_extractor.save_pretrained(_lowerCamelCase  )
						hf_model.save_pretrained(_lowerCamelCase  )


if __name__ == "__main__":
				__lowercase							:int							=	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")
				__lowercase							:List[str]							=	parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a 			=							{
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a 			=							[
     'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTV2ForImageClassification',
     'MobileViTV2ForSemanticSegmentation',
     'MobileViTV2Model',
     'MobileViTV2PreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 a 			=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


