

'''simple docstring'''
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase	 =			{
    """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__    (  _SCREAMING_SNAKE_CASE					:    Tuple     ,							_SCREAMING_SNAKE_CASE					:    Dict     ,							_SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    Any							)			-> Optional[Any]:


	"""simple docstring"""





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

	if weight_type is not None:
		UpperCAmelCase_			:	int      =   getattr(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							).shape
	else:
		UpperCAmelCase_			:	List[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":
		UpperCAmelCase_			:	List[Any]      =   value
	elif weight_type == "weight_g":
		UpperCAmelCase_			:	Tuple      =   value
	elif weight_type == "weight_v":
		UpperCAmelCase_			:	int      =   value
	elif weight_type == "bias":
		UpperCAmelCase_			:	Dict      =   value
	else:
		UpperCAmelCase_			:	List[Any]      =   value

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


def       a__    (  _SCREAMING_SNAKE_CASE					:    Tuple     ,							_SCREAMING_SNAKE_CASE					:    Optional[Any]							)			-> Dict:


	"""simple docstring"""





	UpperCAmelCase_			:	Tuple      =   []
	UpperCAmelCase_			:	int      =   fairseq_model.state_dict()

	UpperCAmelCase_			:	Optional[Any]      =   hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		UpperCAmelCase_			:	List[str]      =   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"     ,							)
			UpperCAmelCase_			:	List[Any]      =   True
		else:
			for key, mapped_key in MAPPING.items():
				UpperCAmelCase_			:	Tuple      =   mapped_key

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


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   full_name.split("conv_layers."							)[-1]
	UpperCAmelCase_			:	Tuple      =   name.split("."							)
	UpperCAmelCase_			:	Dict      =   int(items[0]							)
	UpperCAmelCase_			:	Any      =   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_			:	str      =   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_			:	Optional[int]      =   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_			:	Union[str, 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_			:	Tuple      =   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       a__    (  _SCREAMING_SNAKE_CASE					:    Any							)			-> List[str]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[Any]      =   HubertConfig()
	UpperCAmelCase_			:	Dict      =   model.config

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

	return config


@torch.no_grad()
def       a__    (  _SCREAMING_SNAKE_CASE					:    List[Any]     ,							_SCREAMING_SNAKE_CASE					:    str=None							)			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Union[str, Any]      =   distilhubert().model.model

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

	UpperCAmelCase_			:	List[Any]      =   WavaVecaFeatureExtractor(
	    feature_size=1     ,							sampling_rate=1_60_00     ,							padding_value=0     ,							do_normalize=_SCREAMING_SNAKE_CASE     ,							return_attention_mask=_SCREAMING_SNAKE_CASE     ,							)
	UpperCAmelCase_			:	str      =   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__":
					_lowerCamelCase	 =			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""")
					_lowerCamelCase	 =			parser.parse_args()
					convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''
import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowerCamelCase	 =			[(1,)]


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   bool(os.getenv("RUN_SLOW"     ,							0							)							)
	if run_slow:
		assert True
	else:
		assert False, "should have been skipped"


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   bool(os.getenv("RUN_SLOW"     ,							0							)							)
	if run_slow and torch_device == "cuda":
		assert True
	else:
		assert False, "should have been skipped"
@require_torch
class  _snake_case						(unittest.TestCase):






			@slow
			@require_torch_gpu
			def 						UpperCamelCase__				(   self			):
				check_slow_torch_cuda()






			@require_torch_gpu
			@slow
			def 						UpperCamelCase__				(   self			):
				check_slow_torch_cuda()






			@parameterized.expand(_snake_case			)
			@slow
			def 						UpperCamelCase__				(   self       ,_snake_case=None			):
				check_slow()




@slow
@require_torch_gpu
def       a__    (  )			-> List[Any]:


	"""simple docstring"""





	check_slow_torch_cuda()


@require_torch_gpu
@slow
def       a__    (  )			-> List[str]:


	"""simple docstring"""





	check_slow_torch_cuda()


@slow
@pytest.mark.parametrize("param"     ,							[1]							)
def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> List[Any]:


	"""simple docstring"""





	check_slow()





@pytest.mark.parametrize("param"     ,							[1]							)
@slow
def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Any:


	"""simple docstring"""





	check_slow()




