
"""simple docstring"""





import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
_a								=						logging.get_logger("""transformers.models.encodec""")

_a								=						{
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
_a								=						{
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
_a								=						{
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
_a								=						{
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
_a								=						{
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
_a								=						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_a								=						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_a								=						[]
_a								=						[]



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					for attribute in key.split('''.'''      ):
										_UpperCamelCase									=    getattr(__snake_case,	__snake_case      )

					if weight_type is not None:
										_UpperCamelCase									=    getattr(__snake_case,	__snake_case      ).shape
					else:
										_UpperCamelCase									=    hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    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
					elif weight_type == "running_mean":
										_UpperCamelCase									=    value
					elif weight_type == "running_var":
										_UpperCamelCase									=    value
					elif weight_type == "num_batches_tracked":
										_UpperCamelCase									=    value
					elif weight_type == "weight_ih_l0":
										_UpperCamelCase									=    value
					elif weight_type == "weight_hh_l0":
										_UpperCamelCase									=    value
					elif weight_type == "bias_ih_l0":
										_UpperCamelCase									=    value
					elif weight_type == "bias_hh_l0":
										_UpperCamelCase									=    value
					elif weight_type == "weight_ih_l1":
										_UpperCamelCase									=    value
					elif weight_type == "weight_hh_l1":
										_UpperCamelCase									=    value
					elif weight_type == "bias_ih_l1":
										_UpperCamelCase									=    value
					elif weight_type == "bias_hh_l1":
										_UpperCamelCase									=    value
					else:
										_UpperCamelCase									=    value

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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					for key in ignore_keys:
										if key.endswith('''.*'''      ):
															if name.startswith(key[:-1]      ):
																				return True
										elif ".*." in key:
															_UpperCamelCase					,    _UpperCamelCase									=    key.split('''.*.'''      )
															if prefix in name and suffix in name:
																				return True
										elif key in name:
															return True
					return False



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    []

					if model_name == "encodec_24khz" or "encodec_32khz":
										_UpperCamelCase									=    MAPPING_24K
					elif model_name == "encodec_48khz":
										_UpperCamelCase									=    MAPPING_48K
					else:
										raise ValueError(F'''Unsupported model: {model_name}'''      )

					for name, value in orig_dict.items():
										if should_ignore(__snake_case,	__snake_case      ):
															logger.info(F'''{name} was ignored'''      )
															continue

										_UpperCamelCase									=    False
										for key, mapped_key in MAPPING.items():
															if "*" in key:
																				_UpperCamelCase					,    _UpperCamelCase									=    key.split('''.*.'''      )
																				if prefix in name and suffix in name:
																									_UpperCamelCase									=    suffix

															if key in name:
																				# HACK otherwise .embed gets initialized with .embed_avg too
																				if key.endswith('''embed'''      ) and name.endswith('''embed_avg'''      ):
																									continue

																				_UpperCamelCase									=    True
																				if "*" in mapped_key:
																									_UpperCamelCase									=    name.split(__snake_case      )[0].split('''.'''      )[-2]
																									_UpperCamelCase									=    mapped_key.replace('''*''',	__snake_case      )
																				if "weight_g" in name:
																									_UpperCamelCase									=    '''weight_g'''
																				elif "weight_v" in name:
																									_UpperCamelCase									=    '''weight_v'''
																				elif "weight_ih_l0" in name:
																									_UpperCamelCase									=    '''weight_ih_l0'''
																				elif "weight_hh_l0" in name:
																									_UpperCamelCase									=    '''weight_hh_l0'''
																				elif "bias_ih_l0" in name:
																									_UpperCamelCase									=    '''bias_ih_l0'''
																				elif "bias_hh_l0" in name:
																									_UpperCamelCase									=    '''bias_hh_l0'''
																				elif "weight_ih_l1" in name:
																									_UpperCamelCase									=    '''weight_ih_l1'''
																				elif "weight_hh_l1" in name:
																									_UpperCamelCase									=    '''weight_hh_l1'''
																				elif "bias_ih_l1" in name:
																									_UpperCamelCase									=    '''bias_ih_l1'''
																				elif "bias_hh_l1" in name:
																									_UpperCamelCase									=    '''bias_hh_l1'''
																				elif "bias" in name:
																									_UpperCamelCase									=    '''bias'''
																				elif "weight" in name:
																									_UpperCamelCase									=    '''weight'''
																				elif "running_mean" in name:
																									_UpperCamelCase									=    '''running_mean'''
																				elif "running_var" in name:
																									_UpperCamelCase									=    '''running_var'''
																				elif "num_batches_tracked" in name:
																									_UpperCamelCase									=    '''num_batches_tracked'''
																				else:
																									_UpperCamelCase									=    None
																				set_recursively(__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )
															continue
										if not is_used:
															unused_weights.append(__snake_case      )

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



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case=None,	__snake_case=None,	)				->						Dict:


					"""simple docstring"""





					if config_path is not None:
										_UpperCamelCase									=    EncodecConfig.from_pretrained(__snake_case      )
					else:
										_UpperCamelCase									=    EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										_UpperCamelCase									=    [8, 5, 4, 4]
										_UpperCamelCase									=    [2.2]
										_UpperCamelCase									=    64
										_UpperCamelCase									=    3_20_00
										_UpperCamelCase									=    20_48
										_UpperCamelCase									=    False
										_UpperCamelCase									=    False
										_UpperCamelCase									=    False
					elif model_name == "encodec_48khz":
										_UpperCamelCase									=    [8, 5, 4, 2]
										_UpperCamelCase									=    [3.0, 6.0, 12.0, 24.0]
										_UpperCamelCase									=    4_80_00
										_UpperCamelCase									=    2
										_UpperCamelCase									=    False
										_UpperCamelCase									=    '''time_group_norm'''
										_UpperCamelCase									=    True
										_UpperCamelCase									=    1.0
										_UpperCamelCase									=    0.01
					else:
										raise ValueError(F'''Unknown model name: {model_name}'''      )

					_UpperCamelCase									=    EncodecModel(__snake_case      )

					_UpperCamelCase									=    EncodecFeatureExtractor(
					    feature_size=config.audio_channels,	sampling_rate=config.sampling_rate,	chunk_length_s=config.chunk_length_s,	overlap=config.overlap,	)
					feature_extractor.save_pretrained(__snake_case      )

					_UpperCamelCase									=    torch.load(__snake_case      )
					if "best_state" in original_checkpoint:
										# we might have a training state saved, in which case discard the yaml results and just retain the weights
										_UpperCamelCase									=    original_checkpoint['''best_state''']
					recursively_load_weights(__snake_case,	__snake_case,	__snake_case      )
					model.save_pretrained(__snake_case      )

					if repo_id:
										print('''Pushing to the hub...'''      )
										feature_extractor.push_to_hub(__snake_case      )
										model.push_to_hub(__snake_case      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser()
				parser.add_argument(
				    """--model""",
				    default="""encodec_24khz""",
				    type=str,
				    help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
				)
				parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
				parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
				parser.add_argument(
				    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
				)
				parser.add_argument(
				    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
				)

				_a								=						parser.parse_args()
				convert_checkpoint(
				    args.model,
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)








"""simple docstring"""





import functools
import operator

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


_a								=						logging.get_logger(__name__)

_a								=						{
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'wavlm'



		def __init__(   self							,						__a=32							,						__a=7_68							,						__a=12							,						__a=12							,						__a=30_72							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=0.1							,						__a=0.0							,						__a=0.1							,						__a=0.1							,						__a=0.02							,						__a=1e-5							,						__a="group"							,						__a="gelu"							,						__a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)							,						__a=(5, 2, 2, 2, 2, 2, 2)							,						__a=(10, 3, 3, 3, 3, 2, 2)							,						__a=False							,						__a=1_28							,						__a=16							,						__a=3_20							,						__a=8_00							,						__a=False							,						__a=True							,						__a=0.05							,						__a=10							,						__a=2							,						__a=0.0							,						__a=10							,						__a=3_20							,						__a=2							,						__a=0.1							,						__a=1_00							,						__a=2_56							,						__a=2_56							,						__a=0.1							,						__a="mean"							,						__a=False							,						__a=False							,						__a=2_56							,						__a=(5_12, 5_12, 5_12, 5_12, 15_00)							,						__a=(5, 3, 3, 1, 1)							,						__a=(1, 2, 3, 1, 1)							,						__a=5_12							,						__a=80							,						__a=0							,						__a=1							,						__a=2							,						__a=False							,						__a=3							,						__a=2							,						__a=3							,						__a=None							,						**__a							,						)			->			Union[str, Any]:






							'''simple docstring'''
							super().__init__(**__a							,						pad_token_id=__a							,						bos_token_id=__a							,						eos_token_id=__a)
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    feat_extract_norm
							_UpperCamelCase									=    feat_extract_activation
							_UpperCamelCase									=    list(__a)
							_UpperCamelCase									=    list(__a)
							_UpperCamelCase									=    list(__a)
							_UpperCamelCase									=    conv_bias
							_UpperCamelCase									=    num_buckets
							_UpperCamelCase									=    max_bucket_distance
							_UpperCamelCase									=    num_conv_pos_embeddings
							_UpperCamelCase									=    num_conv_pos_embedding_groups
							_UpperCamelCase									=    len(self.conv_dim)
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    hidden_dropout
							_UpperCamelCase									=    attention_dropout
							_UpperCamelCase									=    activation_dropout
							_UpperCamelCase									=    feat_proj_dropout
							_UpperCamelCase									=    final_dropout
							_UpperCamelCase									=    layerdrop
							_UpperCamelCase									=    layer_norm_eps
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    num_ctc_classes
							_UpperCamelCase									=    vocab_size
							_UpperCamelCase									=    do_stable_layer_norm
							_UpperCamelCase									=    use_weighted_layer_sum
							_UpperCamelCase									=    classifier_proj_size

							if (
							    (len(self.conv_stride) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel) != self.num_feat_extract_layers)
							    or (len(self.conv_dim) != self.num_feat_extract_layers)
							):
												raise ValueError(
												    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
												    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
												    F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
												    F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							_UpperCamelCase									=    apply_spec_augment
							_UpperCamelCase									=    mask_time_prob
							_UpperCamelCase									=    mask_time_length
							_UpperCamelCase									=    mask_time_min_masks
							_UpperCamelCase									=    mask_feature_prob
							_UpperCamelCase									=    mask_feature_length

							# parameters for pretraining with codevector quantized representations
							_UpperCamelCase									=    num_codevectors_per_group
							_UpperCamelCase									=    num_codevector_groups
							_UpperCamelCase									=    contrastive_logits_temperature
							_UpperCamelCase									=    num_negatives
							_UpperCamelCase									=    codevector_dim
							_UpperCamelCase									=    proj_codevector_dim
							_UpperCamelCase									=    diversity_loss_weight

							# ctc loss
							_UpperCamelCase									=    ctc_loss_reduction
							_UpperCamelCase									=    ctc_zero_infinity

							# adapter
							_UpperCamelCase									=    add_adapter
							_UpperCamelCase									=    adapter_kernel_size
							_UpperCamelCase									=    adapter_stride
							_UpperCamelCase									=    num_adapter_layers
							_UpperCamelCase									=    output_hidden_size or hidden_size

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							_UpperCamelCase									=    classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							_UpperCamelCase									=    list(__a)
							_UpperCamelCase									=    list(__a)
							_UpperCamelCase									=    list(__a)
							_UpperCamelCase									=    xvector_output_dim




		@property
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							return functools.reduce(operator.mul							,						self.conv_stride							,						1)






