


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()
lowerCAmelCase							=						logging.get_logger("""transformers.models.encodec""")

lowerCAmelCase							=						{
    """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""",
}
lowerCAmelCase							=						{
    """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""",
}
lowerCAmelCase							=						{
    """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""",
}
lowerCAmelCase							=						{
    """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""",
}
lowerCAmelCase							=						{
    """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""",
}
lowerCAmelCase							=						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowerCAmelCase							=						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowerCAmelCase							=						[]
lowerCAmelCase							=						[]




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)		->    Optional[int]:




						'''simple docstring'''







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

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







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




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			)		->    Optional[int]:




						'''simple docstring'''







						__UpperCAmelCase :       str  						=  []

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

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

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

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




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




						'''simple docstring'''







						if config_path is not None:
												__UpperCAmelCase :       int  						=  EncodecConfig.from_pretrained(lowercase_			)
						else:
												__UpperCAmelCase :       Dict  						=  EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												__UpperCAmelCase :       Optional[int]  						=  [8, 5, 4, 4]
												__UpperCAmelCase :       str  						=  [2.2]
												__UpperCAmelCase :       Optional[int]  						=  64
												__UpperCAmelCase :       str  						=  32000
												__UpperCAmelCase :       Optional[Any]  						=  2048
												__UpperCAmelCase :       Union[str, Any]  						=  False
												__UpperCAmelCase :       Optional[int]  						=  False
												__UpperCAmelCase :       Tuple  						=  False
						elif model_name == "encodec_48khz":
												__UpperCAmelCase :       Tuple  						=  [8, 5, 4, 2]
												__UpperCAmelCase :       str  						=  [3.0, 6.0, 1_2.0, 2_4.0]
												__UpperCAmelCase :       str  						=  48000
												__UpperCAmelCase :       List[Any]  						=  2
												__UpperCAmelCase :       Tuple  						=  False
												__UpperCAmelCase :       str  						=  '''time_group_norm'''
												__UpperCAmelCase :       Optional[Any]  						=  True
												__UpperCAmelCase :       Optional[int]  						=  1.0
												__UpperCAmelCase :       Optional[int]  						=  0.0_1
						else:
												raise ValueError(f"Unknown model name: {model_name}"			)

						__UpperCAmelCase :       str  						=  EncodecModel(lowercase_			)

						__UpperCAmelCase :       Optional[Any]  						=  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(lowercase_			)

						__UpperCAmelCase :       int  						=  torch.load(lowercase_			)
						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 :       Optional[int]  						=  original_checkpoint['''best_state''']
						recursively_load_weights(lowercase_			,					lowercase_			,					lowercase_			)
						model.save_pretrained(lowercase_			)

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


if __name__ == "__main__":
				lowerCAmelCase							=						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."""
				)

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





import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


lowerCAmelCase							=						"""sshleifer/bart-tiny-random"""
lowerCAmelCase							=						"""patrickvonplaten/t5-tiny-random"""




@require_torch
class        lowerCamelCase      (     unittest.TestCase   ):


	@cached_property
	def    A(						self):
							return AutoConfig.from_pretrained(lowercase__)


	def    A(						self):
							__UpperCAmelCase						,			*__UpperCAmelCase :       Dict  						=  create_student_by_copying_alternating_layers(lowercase__							,    tempfile.mkdtemp()							,    e=1							,    d=1)
							self.assertEqual(student.config.num_hidden_layers							,    1)


	def    A(						self):
							__UpperCAmelCase						,			*__UpperCAmelCase :       Union[str, Any]  						=  create_student_by_copying_alternating_layers(lowercase__							,    tempfile.mkdtemp()							,    e=1							,    d=lowercase__)


	def    A(						self):
							__UpperCAmelCase						,			*__UpperCAmelCase :       Tuple  						=  create_student_by_copying_alternating_layers(lowercase__							,    tempfile.mkdtemp()							,    e=1							,    d=lowercase__)
							self.assertEqual(student.config.encoder_layers							,    1)
							self.assertEqual(student.config.decoder_layers							,    self.teacher_config.encoder_layers)


	def    A(						self):
							__UpperCAmelCase						,			*__UpperCAmelCase :       Dict  						=  create_student_by_copying_alternating_layers(lowercase__							,    tempfile.mkdtemp()							,    e=1							,    d=1)
							self.assertEqual(student.config.encoder_layers							,    1)
							self.assertEqual(student.config.decoder_layers							,    1)


	def    A(						self):
							with self.assertRaises(lowercase__):
													create_student_by_copying_alternating_layers(lowercase__							,    tempfile.mkdtemp()							,    e=lowercase__							,    d=lowercase__)

