




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 (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Any:

    """simple docstring"""


    for attribute in key.split('''.'''   ):
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   )

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

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase			=    []
    __UpperCamelCase			=    fairseq_model.state_dict()

    __UpperCamelCase			=    hf_model.feature_extractor

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

                if key in name:
                    __UpperCamelCase			=    True
                    if "*" in mapped_key:
                        __UpperCamelCase			=    name.split(lowercase_   )[0].split('''.'''   )[-2]
                        __UpperCamelCase			=    mapped_key.replace('''*'''       ,  lowercase_   )
                    if "weight_g" in name:
                        __UpperCamelCase			=    '''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase			=    '''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase			=    '''weight'''
                    elif "bias" in name:
                        __UpperCamelCase			=    '''bias'''
                    else:
                        __UpperCamelCase			=    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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    HubertConfig()
    __UpperCamelCase			=    model.config

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

    return config




@torch.no_grad()
def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_=None   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    distilhubert().model.model

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

    __UpperCamelCase			=    WavaVecaFeatureExtractor(
        feature_size=1       ,  sampling_rate=1_60_00       ,  padding_value=0       ,  do_normalize=lowercase_       ,  return_attention_mask=lowercase_       ,  )
    __UpperCamelCase			=    HubertModel(lowercase_   )

    recursively_load_weights(lowercase_       ,  lowercase_   )

    feature_extractor.save_pretrained(lowercase_   )
    hf_model.save_pretrained(lowercase_   )


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)










import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
			pass

if is_speech_available():
			from transformers import EncodecFeatureExtractor, MusicgenProcessor


_A							     =       random.Random()

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Any=1.0     ,					UpperCamelCase		:      Union[str, Any]=None     ,					UpperCamelCase		:      Union[str, Any]=None	)     ->	List[Any]:



	"""simple docstring"""






	if rng is None:
		a_     			=   global_rng

	a_     			=   []
	for batch_idx in range(shape[0]	):
		values.append([]	)
		for _ in range(shape[1]	):
			values[-1].append(rng.random() * scale	)

	return values

@require_torch
@require_sentencepiece
class 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								a_     			=   """facebook/musicgen-small"""
								a_     			=   tempfile.mkdtemp()





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return TaTokenizer.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								shutil.rmtree(self.tmpdirname    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_tokenizer()
								a_     			=   self.get_feature_extractor()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								processor.save_pretrained(self.tmpdirname    )
								a_     			=   MusicgenProcessor.from_pretrained(self.tmpdirname    )

								self.assertEqual(processor.tokenizer.get_vocab()			,       tokenizer.get_vocab()    )
								self.assertIsInstance(processor.tokenizer			,       _SCREAMING_SNAKE_CASE    )

								self.assertEqual(processor.feature_extractor.to_json_string()			,       feature_extractor.to_json_string()    )
								self.assertIsInstance(processor.feature_extractor			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   MusicgenProcessor(tokenizer=self.get_tokenizer()			,       feature_extractor=self.get_feature_extractor()    )
								processor.save_pretrained(self.tmpdirname    )

								a_     			=   self.get_tokenizer(bos_token="""(BOS)"""			,       eos_token="""(EOS)"""    )
								a_     			=   self.get_feature_extractor(do_normalize=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

								a_     			=   MusicgenProcessor.from_pretrained(
								    self.tmpdirname			,       bos_token="""(BOS)"""			,       eos_token="""(EOS)"""			,       do_normalize=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

								self.assertEqual(processor.tokenizer.get_vocab()			,       tokenizer_add_kwargs.get_vocab()    )
								self.assertIsInstance(processor.tokenizer			,       _SCREAMING_SNAKE_CASE    )

								self.assertEqual(processor.feature_extractor.to_json_string()			,       feature_extractor_add_kwargs.to_json_string()    )
								self.assertIsInstance(processor.feature_extractor			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   floats_list((3, 1000)    )

								a_     			=   feature_extractor(_SCREAMING_SNAKE_CASE			,       return_tensors="""np"""    )
								a_     			=   processor(_SCREAMING_SNAKE_CASE			,       return_tensors="""np"""    )

								for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()			,       input_processor[key].sum()			,       delta=1E-2    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   """This is a test string"""

								a_     			=   processor(text=_SCREAMING_SNAKE_CASE    )

								a_     			=   tokenizer(_SCREAMING_SNAKE_CASE    )

								for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]			,       encoded_processor[key]    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								a_     			=   processor.batch_decode(sequences=_SCREAMING_SNAKE_CASE    )
								a_     			=   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    )

								self.assertListEqual(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								self.assertListEqual(
								    processor.model_input_names			,       feature_extractor.model_input_names			,       msg="""`processor` and `feature_extractor` model input names do not match"""			,       )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor(padding_side="""left"""    )
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   [floats_list((1, x)    )[0] for x in range(5			,       20			,       5    )]
								a_     			=   processor(_SCREAMING_SNAKE_CASE    ).padding_mask

								a_     			=   np.asarray(floats_list((3, 20)    )    )[:, None, :]
								a_     			=   processor.batch_decode(_SCREAMING_SNAKE_CASE			,       padding_mask=_SCREAMING_SNAKE_CASE    )

								self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

								for audio in decoded_audios:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       np.ndarray    )

								self.assertTrue(decoded_audios[0].shape == (1, 10)    )
								self.assertTrue(decoded_audios[1].shape == (1, 15)    )
								self.assertTrue(decoded_audios[2].shape == (1, 20)    )