'''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()
_lowercase		:				str	       =		logging.get_logger("transformers.models.encodec")

_lowercase		:				Union[str, Any]	       =		{
    "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",
}
_lowercase		:				Dict	       =		{
    "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",
}
_lowercase		:				Dict	       =		{
    "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",
}
_lowercase		:				List[str]	       =		{
    "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",
}
_lowercase		:				List[Any]	       =		{
    "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",
}
_lowercase		:				Tuple	       =		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_lowercase		:				str	       =		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_lowercase		:				Dict	       =		[]
_lowercase		:				Any	       =		[]




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[Any]   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      Optional[Any]   ,   UpperCAmelCase__ :      List[str]				)    ->   Optional[int]:
							for attribute in key.split("""."""				):
														lowercase_      :    List[str]       		= getattr(UpperCAmelCase__   ,   UpperCAmelCase__				)

							if weight_type is not None:
														lowercase_      :    Optional[Any]       		= getattr(UpperCAmelCase__   ,   UpperCAmelCase__				).shape
							else:
														lowercase_      :    Any       		= 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":
														lowercase_      :    List[str]       		= value
							elif weight_type == "weight_g":
														lowercase_      :    Union[str, Any]       		= value
							elif weight_type == "weight_v":
														lowercase_      :    str       		= value
							elif weight_type == "bias":
														lowercase_      :    Dict       		= value
							elif weight_type == "running_mean":
														lowercase_      :    int       		= value
							elif weight_type == "running_var":
														lowercase_      :    Any       		= value
							elif weight_type == "num_batches_tracked":
														lowercase_      :    Union[str, Any]       		= value
							elif weight_type == "weight_ih_l0":
														lowercase_      :    Dict       		= value
							elif weight_type == "weight_hh_l0":
														lowercase_      :    Optional[int]       		= value
							elif weight_type == "bias_ih_l0":
														lowercase_      :    Optional[int]       		= value
							elif weight_type == "bias_hh_l0":
														lowercase_      :    Optional[int]       		= value
							elif weight_type == "weight_ih_l1":
														lowercase_      :    Union[str, Any]       		= value
							elif weight_type == "weight_hh_l1":
														lowercase_      :    Optional[Any]       		= value
							elif weight_type == "bias_ih_l1":
														lowercase_      :    List[Any]       		= value
							elif weight_type == "bias_hh_l1":
														lowercase_      :    Any       		= value
							else:
														lowercase_      :    Any       		= value

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




def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Dict				)    ->   str:
							for key in ignore_keys:
														if key.endswith(""".*"""				):
																					if name.startswith(key[:-1]				):
																												return True
														elif ".*." in key:
																					lowercase_		,		lowercase_      :    Dict       		= key.split(""".*."""				)
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[str]   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      Union[str, Any]				)    ->   Any:
							lowercase_      :    Optional[int]       		= []

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

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

														lowercase_      :    Optional[Any]       		= False
														for key, mapped_key in MAPPING.items():
																					if "*" in key:
																												lowercase_		,		lowercase_      :    Union[str, Any]       		= key.split(""".*."""				)
																												if prefix in name and suffix in name:
																																			lowercase_      :    Tuple       		= suffix

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

																												lowercase_      :    int       		= True
																												if "*" in mapped_key:
																																			lowercase_      :    str       		= name.split(UpperCAmelCase__				)[0].split("""."""				)[-2]
																																			lowercase_      :    str       		= mapped_key.replace("""*"""   ,   UpperCAmelCase__				)
																												if "weight_g" in name:
																																			lowercase_      :    str       		= """weight_g"""
																												elif "weight_v" in name:
																																			lowercase_      :    Optional[int]       		= """weight_v"""
																												elif "weight_ih_l0" in name:
																																			lowercase_      :    Union[str, Any]       		= """weight_ih_l0"""
																												elif "weight_hh_l0" in name:
																																			lowercase_      :    str       		= """weight_hh_l0"""
																												elif "bias_ih_l0" in name:
																																			lowercase_      :    Dict       		= """bias_ih_l0"""
																												elif "bias_hh_l0" in name:
																																			lowercase_      :    Optional[Any]       		= """bias_hh_l0"""
																												elif "weight_ih_l1" in name:
																																			lowercase_      :    Dict       		= """weight_ih_l1"""
																												elif "weight_hh_l1" in name:
																																			lowercase_      :    Any       		= """weight_hh_l1"""
																												elif "bias_ih_l1" in name:
																																			lowercase_      :    int       		= """bias_ih_l1"""
																												elif "bias_hh_l1" in name:
																																			lowercase_      :    Union[str, Any]       		= """bias_hh_l1"""
																												elif "bias" in name:
																																			lowercase_      :    str       		= """bias"""
																												elif "weight" in name:
																																			lowercase_      :    Optional[Any]       		= """weight"""
																												elif "running_mean" in name:
																																			lowercase_      :    Union[str, Any]       		= """running_mean"""
																												elif "running_var" in name:
																																			lowercase_      :    Dict       		= """running_var"""
																												elif "num_batches_tracked" in name:
																																			lowercase_      :    Tuple       		= """num_batches_tracked"""
																												else:
																																			lowercase_      :    Any       		= None
																												set_recursively(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)
																					continue
														if not is_used:
																					unused_weights.append(UpperCAmelCase__				)

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




@torch.no_grad()
def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      Tuple=None   ,   UpperCAmelCase__ :      Tuple=None   ,   )    ->   str:
							if config_path is not None:
														lowercase_      :    Union[str, Any]       		= EncodecConfig.from_pretrained(UpperCAmelCase__				)
							else:
														lowercase_      :    Optional[int]       		= EncodecConfig()

							if model_name == "encodec_24khz":
														pass  # config is already correct
							elif model_name == "encodec_32khz":
														lowercase_      :    List[str]       		= [8, 5, 4, 4]
														lowercase_      :    str       		= [2.2]
														lowercase_      :    Optional[Any]       		= 64
														lowercase_      :    int       		= 32000
														lowercase_      :    int       		= 2048
														lowercase_      :    List[Any]       		= False
														lowercase_      :    Union[str, Any]       		= False
														lowercase_      :    Tuple       		= False
							elif model_name == "encodec_48khz":
														lowercase_      :    int       		= [8, 5, 4, 2]
														lowercase_      :    str       		= [3.0, 6.0, 12.0, 24.0]
														lowercase_      :    List[Any]       		= 48000
														lowercase_      :    Tuple       		= 2
														lowercase_      :    List[str]       		= False
														lowercase_      :    int       		= """time_group_norm"""
														lowercase_      :    str       		= True
														lowercase_      :    List[str]       		= 1.0
														lowercase_      :    str       		= 0.01
							else:
														raise ValueError(F'''Unknown model name: {model_name}'''				)

							lowercase_      :    Tuple       		= EncodecModel(UpperCAmelCase__				)

							lowercase_      :    Tuple       		= 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(UpperCAmelCase__				)

							lowercase_      :    Union[str, Any]       		= torch.load(UpperCAmelCase__				)
							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
														lowercase_      :    Any       		= original_checkpoint["""best_state"""]
							recursively_load_weights(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)
							model.save_pretrained(UpperCAmelCase__				)

							if repo_id:
														print("""Pushing to the hub..."""				)
														feature_extractor.push_to_hub(UpperCAmelCase__				)
														model.push_to_hub(UpperCAmelCase__				)


if __name__ == "__main__":
						_lowercase		:				int	       =		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."
						)

						_lowercase		:				Any	       =		parser.parse_args()
						convert_checkpoint(
						    args.model,
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.push_to_hub,
						)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase		:				Dict	       =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Dict	       =		["BloomTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Dict	       =		[
						    "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BloomForCausalLM",
						    "BloomModel",
						    "BloomPreTrainedModel",
						    "BloomForSequenceClassification",
						    "BloomForTokenClassification",
						    "BloomForQuestionAnswering",
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Optional[int]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





