





"""simple docstring"""




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

UpperCamelCase_	 ={
    """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 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						for attribute in key.split('''.'''  ):
												_UpperCamelCase     :   Tuple      =       getattr(_lowercase			,		_lowercase  )

						if weight_type is not None:
												_UpperCamelCase     :   Tuple      =       getattr(_lowercase			,		_lowercase  ).shape
						else:
												_UpperCamelCase     :   Any      =       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     :   Tuple      =       value
						elif weight_type == "weight_g":
												_UpperCamelCase     :   int      =       value
						elif weight_type == "weight_v":
												_UpperCamelCase     :   Dict      =       value
						elif weight_type == "bias":
												_UpperCamelCase     :   Optional[int]      =       value
						else:
												_UpperCamelCase     :   str      =       value

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





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Union[str, Any]      =       []
						_UpperCamelCase     :   Any      =       fairseq_model.state_dict()

						_UpperCamelCase     :   Any      =       hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCamelCase     :   Any      =       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		hf_model.config.feat_extract_norm == '''group'''			,		)
																		_UpperCamelCase     :   Optional[int]      =       True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCamelCase     :   List[Any]      =       mapped_key

																								if key in name:
																														_UpperCamelCase     :   Union[str, Any]      =       True
																														if "*" in mapped_key:
																																				_UpperCamelCase     :   Tuple      =       name.split(_lowercase  )[0].split('''.'''  )[-2]
																																				_UpperCamelCase     :   Optional[Any]      =       mapped_key.replace('''*'''			,		_lowercase  )
																														if "weight_g" in name:
																																				_UpperCamelCase     :   List[Any]      =       '''weight_g'''
																														elif "weight_v" in name:
																																				_UpperCamelCase     :   Dict      =       '''weight_v'''
																														elif "weight" in name:
																																				_UpperCamelCase     :   List[Any]      =       '''weight'''
																														elif "bias" in name:
																																				_UpperCamelCase     :   List[str]      =       '''bias'''
																														else:
																																				_UpperCamelCase     :   Dict      =       None
																														set_recursively(_lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  )
																								continue
												if not is_used:
																		unused_weights.append(_lowercase  )

						logger.warning(F"""Unused weights: {unused_weights}"""  )





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Any      =       full_name.split('''conv_layers.'''  )[-1]
						_UpperCamelCase     :   Union[str, Any]      =       name.split('''.'''  )
						_UpperCamelCase     :   int      =       int(items[0]  )
						_UpperCamelCase     :   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."""
																		)
																		_UpperCamelCase     :   Optional[Any]      =       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     :   Tuple      =       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     :   Optional[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."""
																		)
																		_UpperCamelCase     :   str      =       value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
						else:
												unused_weights.append(_lowercase  )





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   Tuple      =       HubertConfig()
						_UpperCamelCase     :   Optional[int]      =       model.config

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

						return config





@torch.no_grad()
def 			a_							(				_lowercase			,		_lowercase=None  ):
						_UpperCamelCase     :   Optional[Any]      =       distilhubert().model.model

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

						_UpperCamelCase     :   int      =       WavaVecaFeatureExtractor(
						    feature_size=1			,		sampling_rate=1_6000			,		padding_value=0			,		do_normalize=_lowercase			,		return_attention_mask=_lowercase			,		)
						_UpperCamelCase     :   Optional[Any]      =       HubertModel(_lowercase  )

						recursively_load_weights(_lowercase			,		_lowercase  )

						feature_extractor.save_pretrained(_lowercase  )
						hf_model.save_pretrained(_lowercase  )


if __name__ == "__main__":
						UpperCamelCase_	 =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""")
						UpperCamelCase_	 =parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase								=      logging.get_logger(__name__)

__lowerCamelCase								=      {
    '''MCG-NJU/videomae-base''': '''https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json''',
}


class 	snake_case_			(lowercase__	):




       """simple docstring"""






       _lowerCamelCase           =					"""videomae"""



       def __init__(  self  ,lowercase=224  ,lowercase=16  ,lowercase=3  ,lowercase=16  ,lowercase=2  ,lowercase=768  ,lowercase=12  ,lowercase=12  ,lowercase=3072  ,lowercase="gelu"  ,lowercase=0.0  ,lowercase=0.0  ,lowercase=0.02  ,lowercase=1E-12  ,lowercase=True  ,lowercase=True  ,lowercase=6  ,lowercase=384  ,lowercase=4  ,lowercase=1536  ,lowercase=True  ,**lowercase  ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_	:						Optional[int]					   =		image_size
             UpperCAmelCase_	:						Optional[Any]					   =		patch_size
             UpperCAmelCase_	:						Tuple					   =		num_channels
             UpperCAmelCase_	:						Optional[int]					   =		num_frames
             UpperCAmelCase_	:						str					   =		tubelet_size

             UpperCAmelCase_	:						Any					   =		hidden_size
             UpperCAmelCase_	:						List[str]					   =		num_hidden_layers
             UpperCAmelCase_	:						List[str]					   =		num_attention_heads
             UpperCAmelCase_	:						Union[str, Any]					   =		intermediate_size
             UpperCAmelCase_	:						List[str]					   =		hidden_act
             UpperCAmelCase_	:						Tuple					   =		hidden_dropout_prob
             UpperCAmelCase_	:						Optional[Any]					   =		attention_probs_dropout_prob
             UpperCAmelCase_	:						Optional[Any]					   =		initializer_range
             UpperCAmelCase_	:						Dict					   =		layer_norm_eps
             UpperCAmelCase_	:						Tuple					   =		qkv_bias
             UpperCAmelCase_	:						int					   =		use_mean_pooling

             UpperCAmelCase_	:						List[Any]					   =		decoder_num_attention_heads
             UpperCAmelCase_	:						Optional[int]					   =		decoder_hidden_size
             UpperCAmelCase_	:						Any					   =		decoder_num_hidden_layers
             UpperCAmelCase_	:						Dict					   =		decoder_intermediate_size
             UpperCAmelCase_	:						str					   =		norm_pix_loss
