
"""simple docstring"""


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

__A       						=       {
    '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(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> List[str]:
	for attribute in key.split("""."""       ):
		_lowerCAmelCase								=getattr(UpperCamelCase_    , UpperCamelCase_       )

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

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



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> Tuple:
	_lowerCAmelCase								=[]
	_lowerCAmelCase								=fairseq_model.state_dict()

	_lowerCAmelCase								=hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_lowerCAmelCase								=False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , hf_model.config.feat_extract_norm == """group"""    , )
			_lowerCAmelCase								=True
		else:
			for key, mapped_key in MAPPING.items():
				_lowerCAmelCase								=mapped_key

				if key in name:
					_lowerCAmelCase								=True
					if "*" in mapped_key:
						_lowerCAmelCase								=name.split(UpperCamelCase_       )[0].split("""."""       )[-2]
						_lowerCAmelCase								=mapped_key.replace("""*"""    , UpperCamelCase_       )
					if "weight_g" in name:
						_lowerCAmelCase								="""weight_g"""
					elif "weight_v" in name:
						_lowerCAmelCase								="""weight_v"""
					elif "weight" in name:
						_lowerCAmelCase								="""weight"""
					elif "bias" in name:
						_lowerCAmelCase								="""bias"""
					else:
						_lowerCAmelCase								=None
					set_recursively(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_       )
				continue
		if not is_used:
			unused_weights.append(UpperCamelCase_       )

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



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



def     _lowerCamelCase(__UpperCamelCase       )				-> Tuple:
	_lowerCAmelCase								=HubertConfig()
	_lowerCAmelCase								=model.config

	_lowerCAmelCase								=fs_config.activation_dropout
	_lowerCAmelCase								=False
	_lowerCAmelCase								=fs_config.attention_dropout
	_lowerCAmelCase								=False
	_lowerCAmelCase								=eval(fs_config.extractor_conv_feature_layers       )
	_lowerCAmelCase								=[x[0] for x in conv_layers]
	_lowerCAmelCase								=[x[1] for x in conv_layers]
	_lowerCAmelCase								=[x[2] for x in conv_layers]
	_lowerCAmelCase								="""gelu"""
	_lowerCAmelCase								="""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
	_lowerCAmelCase								=False
	_lowerCAmelCase								=0.0
	_lowerCAmelCase								=0.0
	_lowerCAmelCase								=fs_config.activation_fn
	_lowerCAmelCase								=fs_config.dropout
	_lowerCAmelCase								=fs_config.encoder_embed_dim
	_lowerCAmelCase								=0.02
	_lowerCAmelCase								=fs_config.encoder_ffn_embed_dim
	_lowerCAmelCase								=1E-5
	_lowerCAmelCase								=0.0
	_lowerCAmelCase								=fs_config.encoder_attention_heads
	_lowerCAmelCase								=fs_config.conv_pos_groups
	_lowerCAmelCase								=fs_config.conv_pos
	_lowerCAmelCase								=len(UpperCamelCase_       )
	_lowerCAmelCase								=fs_config.encoder_layers

	return config



@torch.no_grad()
def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase=None       )				-> str:
	_lowerCAmelCase								=distilhubert().model.model

	if config_path is not None:
		_lowerCAmelCase								=HubertConfig.from_pretrained(UpperCamelCase_       )
	else:
		_lowerCAmelCase								=convert_config(UpperCamelCase_       )
	_lowerCAmelCase								=model.eval()

	_lowerCAmelCase								=WavaVecaFeatureExtractor(
	    feature_size=1    , sampling_rate=16000    , padding_value=0    , do_normalize=UpperCamelCase_    , return_attention_mask=UpperCamelCase_    , )
	_lowerCAmelCase								=HubertModel(UpperCamelCase_       )

	recursively_load_weights(UpperCamelCase_    , UpperCamelCase_       )

	feature_extractor.save_pretrained(UpperCamelCase_       )
	hf_model.save_pretrained(UpperCamelCase_       )


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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
			__A       						=       parser.parse_args()
			convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)










def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						list:
       UpperCamelCase_      				=     sorted(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[2]    ,						reverse=UpperCamelCase_     )

       # Create a list of size equal to the maximum deadline
       # and initialize it with -1
       UpperCamelCase_      				=     max(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[1]     )[1]
       UpperCamelCase_      				=     [-1] * max_deadline

       # Finding the maximum profit and the count of jobs
       UpperCamelCase_      				=     0
       UpperCamelCase_      				=     0
       for job in jobs:
              # Find a free time slot for this job
              # (Note that we start from the last possible slot)
              for i in range(job[1] - 1    ,						-1    ,						-1     ):
                     if time_slots[i] == -1:
                            UpperCamelCase_      				=     job[0]
                            count += 1
                            max_profit += job[2]
                            break
       return [count, max_profit]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

