

'''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 a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''






	for attribute in key.split('.'	):
		snake_case_      		= getattr(a__			, a__	)

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

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





def a(lowercase__			, lowercase__	):





	'''simple docstring'''






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





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= []

	if model_name == "encodec_24khz" or "encodec_32khz":
		snake_case_      		= MAPPING_24K
	elif model_name == "encodec_48khz":
		snake_case_      		= MAPPING_48K
	else:
		raise ValueError(f"""Unsupported model: {model_name}"""	)

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

		snake_case_      		= False
		for key, mapped_key in MAPPING.items():
			if "*" in key:
				snake_case_		,       snake_case_      		= key.split('.*.'	)
				if prefix in name and suffix in name:
					snake_case_      		= suffix

			if key in name:
				# HACK otherwise .embed gets initialized with .embed_avg too
				if key.endswith('embed'	) and name.endswith('embed_avg'	):
					continue

				snake_case_      		= True
				if "*" in mapped_key:
					snake_case_      		= name.split(a__	)[0].split('.'	)[-2]
					snake_case_      		= mapped_key.replace('*'			, a__	)
				if "weight_g" in name:
					snake_case_      		= 'weight_g'
				elif "weight_v" in name:
					snake_case_      		= 'weight_v'
				elif "weight_ih_l0" in name:
					snake_case_      		= 'weight_ih_l0'
				elif "weight_hh_l0" in name:
					snake_case_      		= 'weight_hh_l0'
				elif "bias_ih_l0" in name:
					snake_case_      		= 'bias_ih_l0'
				elif "bias_hh_l0" in name:
					snake_case_      		= 'bias_hh_l0'
				elif "weight_ih_l1" in name:
					snake_case_      		= 'weight_ih_l1'
				elif "weight_hh_l1" in name:
					snake_case_      		= 'weight_hh_l1'
				elif "bias_ih_l1" in name:
					snake_case_      		= 'bias_ih_l1'
				elif "bias_hh_l1" in name:
					snake_case_      		= 'bias_hh_l1'
				elif "bias" in name:
					snake_case_      		= 'bias'
				elif "weight" in name:
					snake_case_      		= 'weight'
				elif "running_mean" in name:
					snake_case_      		= 'running_mean'
				elif "running_var" in name:
					snake_case_      		= 'running_var'
				elif "num_batches_tracked" in name:
					snake_case_      		= 'num_batches_tracked'
				else:
					snake_case_      		= None
				set_recursively(a__			, a__			, a__			, a__			, a__	)
			continue
		if not is_used:
			unused_weights.append(a__	)

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





@torch.no_grad()
def a(lowercase__			, lowercase__			, lowercase__			, lowercase__=None			, lowercase__=None			, ):





	'''simple docstring'''






	if config_path is not None:
		snake_case_      		= EncodecConfig.from_pretrained(a__	)
	else:
		snake_case_      		= EncodecConfig()

	if model_name == "encodec_24khz":
		pass  # config is already correct
	elif model_name == "encodec_32khz":
		snake_case_      		= [8, 5, 4, 4]
		snake_case_      		= [2.2]
		snake_case_      		= 64
		snake_case_      		= 32000
		snake_case_      		= 2048
		snake_case_      		= False
		snake_case_      		= False
		snake_case_      		= False
	elif model_name == "encodec_48khz":
		snake_case_      		= [8, 5, 4, 2]
		snake_case_      		= [3.0, 6.0, 12.0, 24.0]
		snake_case_      		= 48000
		snake_case_      		= 2
		snake_case_      		= False
		snake_case_      		= 'time_group_norm'
		snake_case_      		= True
		snake_case_      		= 1.0
		snake_case_      		= 0.01
	else:
		raise ValueError(f"""Unknown model name: {model_name}"""	)

	snake_case_      		= EncodecModel(a__	)

	snake_case_      		= 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(a__	)

	snake_case_      		= torch.load(a__	)
	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
		snake_case_      		= original_checkpoint['best_state']
	recursively_load_weights(a__			, a__			, a__	)
	model.save_pretrained(a__	)

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


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,
		)



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= UNetaDModel(
				    block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=3 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
				return model


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= VQModel(
				    block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=3 ,				)
				return model


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= CLIPTextConfig(
				    bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=10_00 ,				)
				return CLIPTextModel(__UpperCamelCase							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= DDIMScheduler()
				snake_case_      		= self.dummy_vq_model

				snake_case_      		= LDMPipeline(unet=__UpperCamelCase ,				vqvae=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				ldm.to(__UpperCamelCase							)
				ldm.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=2 ,				output_type='numpy'							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=2 ,				output_type='numpy' ,				return_dict=__UpperCamelCase							)[0]

				snake_case_      		= image[0, -3:, -3:, -1]
				snake_case_      		= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				snake_case_      		= np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]							)
				snake_case_      		= 1E-2 if torch_device != 'mps' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < tolerance







@slow
@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'							)
				ldm.to(__UpperCamelCase							)
				ldm.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=5 ,				output_type='numpy'							).images

				snake_case_      		= image[0, -3:, -3:, -1]

				assert image.shape == (1, 2_56, 2_56, 3)
				snake_case_      		= np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]							)
				snake_case_      		= 1E-2 if torch_device != 'mps' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
