

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	for attribute in key.split('.'      ):
		__lowerCamelCase    :    Any 	=			getattr(lowerCamelCase__     ,			lowerCamelCase__      )

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

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					int:
	for key in ignore_keys:
		if key.endswith('.*'      ):
			if name.startswith(key[:-1]      ):
				return True
		elif ".*." in key:
			__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			key.split('.*.'      )
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Any:
	__lowerCamelCase    :    Any 	=			[]

	if model_name == "encodec_24khz" or "encodec_32khz":
		__lowerCamelCase    :    str 	=			MAPPING_24K
	elif model_name == "encodec_48khz":
		__lowerCamelCase    :    Union[str, Any] 	=			MAPPING_48K
	else:
		raise ValueError(F"Unsupported model: {model_name}"      )

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

		__lowerCamelCase    :    Optional[int] 	=			False
		for key, mapped_key in MAPPING.items():
			if "*" in key:
				__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			key.split('.*.'      )
				if prefix in name and suffix in name:
					__lowerCamelCase    :    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

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

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






@torch.no_grad()
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			)  ->					Optional[int]:
	if config_path is not None:
		__lowerCamelCase    :    int 	=			EncodecConfig.from_pretrained(lowerCamelCase__      )
	else:
		__lowerCamelCase    :    Union[str, Any] 	=			EncodecConfig()

	if model_name == "encodec_24khz":
		pass  # config is already correct
	elif model_name == "encodec_32khz":
		__lowerCamelCase    :    str 	=			[8, 5, 4, 4]
		__lowerCamelCase    :    Union[str, Any] 	=			[2.2]
		__lowerCamelCase    :    List[str] 	=			6_4
		__lowerCamelCase    :    Tuple 	=			3_2_0_0_0
		__lowerCamelCase    :    Union[str, Any] 	=			2_0_4_8
		__lowerCamelCase    :    List[str] 	=			False
		__lowerCamelCase    :    Tuple 	=			False
		__lowerCamelCase    :    List[Any] 	=			False
	elif model_name == "encodec_48khz":
		__lowerCamelCase    :    List[Any] 	=			[8, 5, 4, 2]
		__lowerCamelCase    :    str 	=			[3.0, 6.0, 12.0, 24.0]
		__lowerCamelCase    :    List[str] 	=			4_8_0_0_0
		__lowerCamelCase    :    Dict 	=			2
		__lowerCamelCase    :    int 	=			False
		__lowerCamelCase    :    Tuple 	=			'time_group_norm'
		__lowerCamelCase    :    int 	=			True
		__lowerCamelCase    :    Any 	=			1.0
		__lowerCamelCase    :    Optional[Any] 	=			0.01
	else:
		raise ValueError(F"Unknown model name: {model_name}"      )

	__lowerCamelCase    :    Dict 	=			EncodecModel(lowerCamelCase__      )

	__lowerCamelCase    :    List[str] 	=			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(lowerCamelCase__      )

	__lowerCamelCase    :    str 	=			torch.load(lowerCamelCase__      )
	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
		__lowerCamelCase    :    str 	=			original_checkpoint['best_state']
	recursively_load_weights(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	model.save_pretrained(lowerCamelCase__      )

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


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,
					)







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


a		=[
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None      )  ->					Optional[int]:
	__lowerCamelCase    :    int 	=			True
	while ask_again:
		__lowerCamelCase    :    Dict 	=			input(lowerCamelCase__      )
		try:
			if default is not None and len(lowerCamelCase__      ) == 0:
				return default
			return convert_value(lowerCamelCase__      ) if convert_value is not None else result
		except Exception:
			if error_message is not None:
				print(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=[]     ,			lowerCamelCase__=None     ,			lowerCamelCase__=0      )  ->					str:
	__lowerCamelCase    :    Union[str, Any] 	=			BulletMenu(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			menu.run(default_choice=lowerCamelCase__      )
	return convert_value(lowerCamelCase__      ) if convert_value is not None else result






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    List[str] 	=			int(lowerCamelCase__      )
	return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Union[str, Any]:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return DynamoBackend(DYNAMO_BACKENDS[value]      ).value






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	return {"yes": True, "no": False}[value.lower()]






class 	A_		(	argparse.RawDescriptionHelpFormatter    ):
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    int 	=			super()._format_usage(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			usage.replace('<command> [<args>] '       ,'')
								return usage




