
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__							:							List[str]									=			logging.get_logger("transformers.models.encodec")

lowercase__							:							Dict									=			{
    "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__							:							Tuple									=			{
    "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__							:							Union[str, Any]									=			{
    "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__							:							Optional[Any]									=			{
    "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__							:							Dict									=			{
    "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__							:							Optional[int]									=			[]
lowercase__							:							Tuple									=			[]




def 	A_ (							snake_case							:   Optional[int] ,      snake_case							:   Tuple ,      snake_case							:   str ,      snake_case							:   str ,      snake_case							:   str ):



						'''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 	A_ (							snake_case							:   str ,      snake_case							:   Dict ):



						'''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 	A_ (							snake_case							:   List[Any] ,      snake_case							:   Any ,      snake_case							:   int ):



						'''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 	A_ (							snake_case							:   Optional[Any] ,      snake_case							:   List[Any] ,      snake_case							:   Optional[int] ,      snake_case							:   Dict=None ,      snake_case							:   int=None ,      ):



						'''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       =			32000
												__UpperCamelCase       =			2048
												__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       =			48000
												__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__":
				lowercase__							:							Tuple									=			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__							:							Tuple									=			parser.parse_args()
				convert_checkpoint(
				    args.model,
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													super().__init__()

													# make sure scheduler can always be converted to DDIM
													__UpperCamelCase       =			DDIMScheduler.from_config(scheduler.config			)

													self.register_modules(unet=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_			)



							@torch.no_grad()
							def __call__(  self     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = 50     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "pil"     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Union[ImagePipelineOutput, Tuple]:





													'''simple docstring'''






													if isinstance(self.unet.config.sample_size     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			(
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			__UpperCamelCase       =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) and len(SCREAMING_SNAKE_CASE_			) != batch_size:
																			raise ValueError(
																			    F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_			)}, but requested an effective batch"
																			    F" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

													__UpperCamelCase       =			randn_tensor(SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=self.device     ,	dtype=self.unet.dtype			)

													# set step values
													self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

													for t in self.progress_bar(self.scheduler.timesteps			):
																			# 1. predict noise model_output
																			__UpperCamelCase       =			self.unet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			).sample

																			# 2. predict previous mean of image x_t-1 and add variance depending on eta
																			# eta corresponds to η in paper and should be between [0, 1]
																			# do x_t -> x_t-1
																			__UpperCamelCase       =			self.scheduler.step(
																			    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	eta=SCREAMING_SNAKE_CASE_     ,	use_clipped_model_output=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_			).prev_sample

													__UpperCamelCase       =			(image / 2 + 0.5).clamp(0     ,	1			)
													__UpperCamelCase       =			image.cpu().permute(0     ,	2     ,	3     ,	1			).numpy()
													if output_type == "pil":
																			__UpperCamelCase       =			self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)




