


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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict     ,					UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Optional[int]     ,					UpperCamelCase		:      str     ,					UpperCamelCase		:      List[Any]	)     ->	Any:



	"""simple docstring"""






	for attribute in key.split("""."""	):
		a_     			=   getattr(UpperCAmelCase_     ,					UpperCAmelCase_	)

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

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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      Union[str, Any]	)     ->	int:



	"""simple docstring"""






	a_     			=   []
	a_     			=   fairseq_model.state_dict()

	a_     			=   hf_model.feature_extractor

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

				if key in name:
					a_     			=   True
					if "*" in mapped_key:
						a_     			=   name.split(UpperCAmelCase_	)[0].split("""."""	)[-2]
						a_     			=   mapped_key.replace("""*"""     ,					UpperCAmelCase_	)
					if "weight_g" in name:
						a_     			=   'weight_g'
					elif "weight_v" in name:
						a_     			=   'weight_v'
					elif "weight" in name:
						a_     			=   'weight'
					elif "bias" in name:
						a_     			=   'bias'
					else:
						a_     			=   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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Union[str, Any]     ,					UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      int     ,					UpperCamelCase		:      int	)     ->	Dict:



	"""simple docstring"""






	a_     			=   full_name.split("""conv_layers."""	)[-1]
	a_     			=   name.split("""."""	)
	a_     			=   int(items[0]	)
	a_     			=   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."""
			)
			a_     			=   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."""
			)
			a_     			=   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."
			)
			a_     			=   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."""
			)
			a_     			=   value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
	else:
		unused_weights.append(UpperCAmelCase_	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple	)     ->	List[str]:



	"""simple docstring"""






	a_     			=   HubertConfig()
	a_     			=   model.config

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

	return config

@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str     ,					UpperCamelCase		:      Dict=None	)     ->	int:



	"""simple docstring"""






	a_     			=   distilhubert().model.model

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

	a_     			=   WavaVecaFeatureExtractor(
	    feature_size=1     ,					sampling_rate=1_6000     ,					padding_value=0     ,					do_normalize=UpperCAmelCase_     ,					return_attention_mask=UpperCAmelCase_     ,					)
	a_     			=   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)


from __future__ import annotations






def        lowerCamelCase_  (				UpperCAmelCase_				:  int						)    ->					list[int]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    [0] * max_one

	for i in range(0				,							UpperCAmelCase_						):
		_UpperCamelCase :		List[Any]								=    i

	for i in range(2				,							UpperCAmelCase_						):
		if totients[i] == i:
			for j in range(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						):
				totients[j] -= totients[j] // i

	return totients






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					bool:




	'''simple docstring'''




	return sorted(str(UpperCAmelCase_						)						) == sorted(str(UpperCAmelCase_						)						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int = 1_0_0_0_0_0_0_0						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		List[Any]								=    1  # i
	_UpperCamelCase :		List[str]								=    0  # φ(i)
	_UpperCamelCase :		str								=    get_totients(max_n + 1						)

	for i in range(2				,							max_n + 1						):
		_UpperCamelCase :		List[str]								=    totients[i]

		if i * min_denominator < min_numerator * t and has_same_digits(UpperCAmelCase_				,							UpperCAmelCase_						):
			_UpperCamelCase :		Optional[int]								=    i
			_UpperCamelCase :		Tuple								=    t

	return min_numerator


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




