

'''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()
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   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			Dict:
					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    ,__UpperCamelCase	)       ->			List[str]:
					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    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			Union[str, Any]:
					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    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase=None    ,__UpperCamelCase=None    ,)       ->			Any:
					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_20_00
										lowerCamelCase_  				=      20_48
										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_80_00
										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__":
				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,
				)











'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    UNetaDModel
 __a      :    ScoreSdeVeScheduler
 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       super().__init__()
       self.register_modules(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 20_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "pil"      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				self.unet.config.sample_size
       UpperCamelCase_					: int            =				(batch_size, 3, img_size, img_size)

       UpperCamelCase_					: List[Any]            =				self.unet

       UpperCamelCase_					: Union[str, Any]            =				randn_tensor(__lowerCAmelCase      ,  generator=__lowerCAmelCase			) * self.scheduler.init_noise_sigma
       UpperCamelCase_					: Union[str, Any]            =				sample.to(self.device			)

       self.scheduler.set_timesteps(__lowerCAmelCase			)
       self.scheduler.set_sigmas(__lowerCAmelCase			)

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
             UpperCamelCase_					: Union[str, Any]            =				self.scheduler.sigmas[i] * torch.ones(shape[0]      ,  device=self.device			)

             # correction step
             for _ in range(self.scheduler.config.correct_steps			):
                   UpperCamelCase_					: List[str]            =				self.unet(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
                   UpperCamelCase_					: List[str]            =				self.scheduler.step_correct(__lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

             # prediction step
             UpperCamelCase_					: List[Any]            =				model(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
             UpperCamelCase_					: Tuple            =				self.scheduler.step_pred(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			)

             UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				output.prev_sample, output.prev_sample_mean

       UpperCamelCase_					: Tuple            =				sample_mean.clamp(0      ,  1			)
       UpperCamelCase_					: str            =				sample.cpu().permute(0      ,  2      ,  3      ,  1			).numpy()
       if output_type == "pil":
             UpperCamelCase_					: Dict            =				self.numpy_to_pil(__lowerCAmelCase			)

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowerCAmelCase			)






