






from typing import TYPE_CHECKING

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


__snake_case    :    List[str]         =     {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Tuple         =     ["""MvpTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Dict         =     [
					    """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MvpForCausalLM""",
					    """MvpForConditionalGeneration""",
					    """MvpForQuestionAnswering""",
					    """MvpForSequenceClassification""",
					    """MvpModel""",
					    """MvpPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

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













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()
__snake_case    :    int         =     logging.get_logger("""transformers.models.encodec""")

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Optional[int]		)  ->   List[str]:

						"""simple docstring"""



						for attribute in key.split('.'		):
												lowerCAmelCase__    						=						getattr(UpperCamelCase_    ,       UpperCamelCase_		)

						if weight_type is not None:
												lowerCAmelCase__    						=						getattr(UpperCamelCase_    ,       UpperCamelCase_		).shape
						else:
												lowerCAmelCase__    						=						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__    						=						value
						elif weight_type == "weight_g":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_v":
												lowerCAmelCase__    						=						value
						elif weight_type == "bias":
												lowerCAmelCase__    						=						value
						elif weight_type == "running_mean":
												lowerCAmelCase__    						=						value
						elif weight_type == "running_var":
												lowerCAmelCase__    						=						value
						elif weight_type == "num_batches_tracked":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_ih_l0":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_hh_l0":
												lowerCAmelCase__    						=						value
						elif weight_type == "bias_ih_l0":
												lowerCAmelCase__    						=						value
						elif weight_type == "bias_hh_l0":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_ih_l1":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_hh_l1":
												lowerCAmelCase__    						=						value
						elif weight_type == "bias_ih_l1":
												lowerCAmelCase__    						=						value
						elif weight_type == "bias_hh_l1":
												lowerCAmelCase__    						=						value
						else:
												lowerCAmelCase__    						=						value

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					List[str]		)  ->   Tuple:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					str		)  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]

						if model_name == "encodec_24khz" or "encodec_32khz":
												lowerCAmelCase__    						=						MAPPING_24K
						elif model_name == "encodec_48khz":
												lowerCAmelCase__    						=						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

												lowerCAmelCase__    						=						False
												for key, mapped_key in MAPPING.items():
																		if "*" in key:
																								lowerCAmelCase__						,       lowerCAmelCase__    						=						key.split('.*.'		)
																								if prefix in name and suffix in name:
																														lowerCAmelCase__    						=						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__    						=						True
																								if "*" in mapped_key:
																														lowerCAmelCase__    						=						name.split(UpperCamelCase_		)[0].split('.'		)[-2]
																														lowerCAmelCase__    						=						mapped_key.replace('*'    ,       UpperCamelCase_		)
																								if "weight_g" in name:
																														lowerCAmelCase__    						=						'weight_g'
																								elif "weight_v" in name:
																														lowerCAmelCase__    						=						'weight_v'
																								elif "weight_ih_l0" in name:
																														lowerCAmelCase__    						=						'weight_ih_l0'
																								elif "weight_hh_l0" in name:
																														lowerCAmelCase__    						=						'weight_hh_l0'
																								elif "bias_ih_l0" in name:
																														lowerCAmelCase__    						=						'bias_ih_l0'
																								elif "bias_hh_l0" in name:
																														lowerCAmelCase__    						=						'bias_hh_l0'
																								elif "weight_ih_l1" in name:
																														lowerCAmelCase__    						=						'weight_ih_l1'
																								elif "weight_hh_l1" in name:
																														lowerCAmelCase__    						=						'weight_hh_l1'
																								elif "bias_ih_l1" in name:
																														lowerCAmelCase__    						=						'bias_ih_l1'
																								elif "bias_hh_l1" in name:
																														lowerCAmelCase__    						=						'bias_hh_l1'
																								elif "bias" in name:
																														lowerCAmelCase__    						=						'bias'
																								elif "weight" in name:
																														lowerCAmelCase__    						=						'weight'
																								elif "running_mean" in name:
																														lowerCAmelCase__    						=						'running_mean'
																								elif "running_var" in name:
																														lowerCAmelCase__    						=						'running_var'
																								elif "num_batches_tracked" in name:
																														lowerCAmelCase__    						=						'num_batches_tracked'
																								else:
																														lowerCAmelCase__    						=						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 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Optional[int]=None    ,       UpperCamelCase_       :					List[Any]=None    ,       )  ->   Dict:

						"""simple docstring"""



						if config_path is not None:
												lowerCAmelCase__    						=						EncodecConfig.from_pretrained(UpperCamelCase_		)
						else:
												lowerCAmelCase__    						=						EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												lowerCAmelCase__    						=						[8, 5, 4, 4]
												lowerCAmelCase__    						=						[2.2]
												lowerCAmelCase__    						=						64
												lowerCAmelCase__    						=						3_2000
												lowerCAmelCase__    						=						2048
												lowerCAmelCase__    						=						False
												lowerCAmelCase__    						=						False
												lowerCAmelCase__    						=						False
						elif model_name == "encodec_48khz":
												lowerCAmelCase__    						=						[8, 5, 4, 2]
												lowerCAmelCase__    						=						[3.0, 6.0, 12.0, 24.0]
												lowerCAmelCase__    						=						4_8000
												lowerCAmelCase__    						=						2
												lowerCAmelCase__    						=						False
												lowerCAmelCase__    						=						'time_group_norm'
												lowerCAmelCase__    						=						True
												lowerCAmelCase__    						=						1.0
												lowerCAmelCase__    						=						0.01
						else:
												raise ValueError(F"Unknown model name: {model_name}"		)

						lowerCAmelCase__    						=						EncodecModel(UpperCamelCase_		)

						lowerCAmelCase__    						=						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_		)

						lowerCAmelCase__    						=						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
												lowerCAmelCase__    						=						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__":
					__snake_case    :    Optional[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."""
					)

					__snake_case    :    Dict         =     parser.parse_args()
					convert_checkpoint(
					    args.model,
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)





