


"""simple docstring"""




import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


__magic_name__						: Optional[int]    =				{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 1_3_1_0_7_2,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
}





def       a_ (	lowercase__ :Optional[int],			lowercase__ :Optional[int]     ):
			return torch.atana(lowercase__,			lowercase__     ) / math.pi * 2





def       a_ (	lowercase__ :Optional[int]     ):
			__lowerCamelCase          =      torch.sin(t * math.pi / 2     ) ** 2
			__lowerCamelCase          =      (1 - sigma**2) ** 0.5
			return alpha_sigma_to_t(lowercase__,			lowercase__     )




class 			__snake_case   (lowerCamelCase  ):
							pass




class 			__snake_case   (nn.Module  ):




							def __init__(     self:    Tuple     ,							A_:    Dict   ):
										super().__init__()

										__lowerCamelCase          =      DiffusionAttnUnetaD(A_     ,							n_attn_layers=4   )
										__lowerCamelCase          =      deepcopy(self.diffusion   )
										__lowerCamelCase          =      torch.quasirandom.SobolEngine(1     ,							scramble=A_   )





def       a_ (	lowercase__ :List[str]     ):
			__lowerCamelCase          =      MODELS_MAP[model_name]["""url"""]
			os.system(f'wget {url} ./'     )

			return f'./{model_name}.ckpt'


__magic_name__						: Optional[int]    =				{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
__magic_name__						: Optional[Any]    =				{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
__magic_name__						: Dict    =				{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
__magic_name__						: Union[str, Any]    =				{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

__magic_name__						: List[str]    =				{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

__magic_name__						: Optional[Any]    =				{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}





def       a_ (	lowercase__ :int     ):
			if name.startswith("""skip"""     ):
						return name.replace("""skip""",			RES_CONV_MAP["""skip"""]     )

			# name has to be of format main.{digit}
			if not name.startswith("""main."""     ):
						raise ValueError(f'ResConvBlock error with {name}'     )

			return name.replace(name[:6],			RES_CONV_MAP[name[:6]]     )





def       a_ (	lowercase__ :str     ):
			for key, value in ATTN_MAP.items():
						if name.startswith(lowercase__     ) and not isinstance(lowercase__,			lowercase__     ):
									return name.replace(lowercase__,			lowercase__     )
						elif name.startswith(lowercase__     ):
									return [name.replace(lowercase__,			lowercase__     ) for v in value]
			raise ValueError(f'Attn error with {name}'     )





def       a_ (	lowercase__ :Optional[int],			lowercase__ :Union[str, Any]=13     ):
			__lowerCamelCase          =      input_string

			if string.split("""."""     )[0] == "timestep_embed":
						return string.replace("""timestep_embed""",			"""time_proj"""     )

			__lowerCamelCase          =      0
			if string.startswith("""net.3."""     ):
						depth += 1
						__lowerCamelCase          =      string[6:]
			elif string.startswith("""net."""     ):
						__lowerCamelCase          =      string[4:]

			while string.startswith("""main.7."""     ):
						depth += 1
						__lowerCamelCase          =      string[7:]

			if string.startswith("""main."""     ):
						__lowerCamelCase          =      string[5:]

			# mid block
			if string[:2].isdigit():
						__lowerCamelCase          =      string[:2]
						__lowerCamelCase          =      string[2:]
			else:
						__lowerCamelCase          =      string[0]
						__lowerCamelCase          =      string[1:]

			if depth == max_depth:
						__lowerCamelCase          =      MID_NUM_TO_LAYER[layer_num]
						__lowerCamelCase          =      """mid_block"""
			elif depth > 0 and int(lowercase__     ) < 7:
						__lowerCamelCase          =      DOWN_NUM_TO_LAYER[layer_num]
						__lowerCamelCase          =      f'down_blocks.{depth}'
			elif depth > 0 and int(lowercase__     ) > 7:
						__lowerCamelCase          =      UP_NUM_TO_LAYER[layer_num]
						__lowerCamelCase          =      f'up_blocks.{max_depth - depth - 1}'
			elif depth == 0:
						__lowerCamelCase          =      DEPTH_0_TO_LAYER[layer_num]
						__lowerCamelCase          =      f'up_blocks.{max_depth - 1}' if int(lowercase__     ) > 3 else """down_blocks.0"""

			if not string_left.startswith("""."""     ):
						raise ValueError(f'Naming error with {input_string} and string_left: {string_left}.'     )

			__lowerCamelCase          =      string_left[1:]

			if "resnets" in new_layer:
						__lowerCamelCase          =      convert_resconv_naming(lowercase__     )
			elif "attentions" in new_layer:
						__lowerCamelCase          =      convert_attn_naming(lowercase__     )
						__lowerCamelCase          =      new_string_left

			if not isinstance(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      prefix + """.""" + new_layer + """.""" + string_left
			else:
						__lowerCamelCase          =      [prefix + """.""" + new_layer + """.""" + s for s in string_left]
			return new_string





def       a_ (	lowercase__ :Union[str, Any]     ):
			__lowerCamelCase          =      {}
			for k, v in state_dict.items():
						if k.endswith("""kernel"""     ):
									# up- and downsample layers, don't have trainable weights
									continue

						__lowerCamelCase          =      rename(lowercase__     )

						# check if we need to transform from Conv => Linear for attention
						if isinstance(lowercase__,			lowercase__     ):
									__lowerCamelCase          =      transform_conv_attns(lowercase__,			lowercase__,			lowercase__     )
						else:
									__lowerCamelCase          =      v

			return new_state_dict





def       a_ (	lowercase__ :Union[str, Any],			lowercase__ :Optional[int],			lowercase__ :List[Any]     ):
			if len(lowercase__     ) == 1:
						if len(v.shape     ) == 3:
									# weight
									__lowerCamelCase          =      v[:, :, 0]
						else:
									# bias
									__lowerCamelCase          =      v
			else:
						# qkv matrices
						__lowerCamelCase          =      v.shape[0]
						__lowerCamelCase          =      trippled_shape // 3
						for i in range(3     ):
									if len(v.shape     ) == 3:
												__lowerCamelCase          =      v[i * single_shape : (i + 1) * single_shape, :, 0]
									else:
												__lowerCamelCase          =      v[i * single_shape : (i + 1) * single_shape]
			return new_state_dict





def       a_ (	lowercase__ :Any     ):
			__lowerCamelCase          =      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""     )

			__lowerCamelCase          =      args.model_path.split("""/"""     )[-1].split("""."""     )[0]
			if not os.path.isfile(args.model_path     ):
						assert (
						    model_name == args.model_path
						), f'Make sure to provide one of the official model names {MODELS_MAP.keys()}'
						__lowerCamelCase          =      download(lowercase__     )

			__lowerCamelCase          =      MODELS_MAP[model_name]["""sample_rate"""]
			__lowerCamelCase          =      MODELS_MAP[model_name]["""sample_size"""]

			__lowerCamelCase          =      Object()
			__lowerCamelCase          =      sample_size
			__lowerCamelCase          =      sample_rate
			__lowerCamelCase          =      0

			__lowerCamelCase          =      UNetaDModel(sample_size=lowercase__,			sample_rate=lowercase__     )
			__lowerCamelCase          =      diffusers_model.state_dict()

			__lowerCamelCase          =      DiffusionUncond(lowercase__     )
			orig_model.load_state_dict(torch.load(args.model_path,			map_location=lowercase__     )["""state_dict"""]     )
			__lowerCamelCase          =      orig_model.diffusion_ema.eval()
			__lowerCamelCase          =      orig_model.state_dict()
			__lowerCamelCase          =      rename_orig_weights(lowercase__     )

			__lowerCamelCase          =      set(renamed_state_dict.keys()     ) - set(diffusers_state_dict.keys()     )
			__lowerCamelCase          =      set(diffusers_state_dict.keys()     ) - set(renamed_state_dict.keys()     )

			assert len(lowercase__     ) == 0, f'Problem with {renamed_minus_diffusers}'
			assert all(k.endswith("""kernel"""     ) for k in list(lowercase__     )     ), f'Problem with {diffusers_minus_renamed}'

			for key, value in renamed_state_dict.items():
						assert (
						    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
						), f'Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'
						if key == "time_proj.weight":
									__lowerCamelCase          =      value.squeeze()

						__lowerCamelCase          =      value

			diffusers_model.load_state_dict(lowercase__     )

			__lowerCamelCase          =      100
			__lowerCamelCase          =      33

			__lowerCamelCase          =      IPNDMScheduler(num_train_timesteps=lowercase__     )

			__lowerCamelCase          =      torch.manual_seed(lowercase__     )
			__lowerCamelCase          =      torch.randn([1, 2, config.sample_size],			generator=lowercase__     ).to(lowercase__     )

			__lowerCamelCase          =      torch.linspace(1,			0,			steps + 1,			device=lowercase__     )[:-1]
			__lowerCamelCase          =      get_crash_schedule(lowercase__     )

			__lowerCamelCase          =      DanceDiffusionPipeline(unet=lowercase__,			scheduler=lowercase__     )

			__lowerCamelCase          =      torch.manual_seed(33     )
			__lowerCamelCase          =      pipe(num_inference_steps=lowercase__,			generator=lowercase__     ).audios

			__lowerCamelCase          =      sampling.iplms_sample(lowercase__,			lowercase__,			lowercase__,			{}     )
			__lowerCamelCase          =      generated.clamp(-1,			1     )

			__lowerCamelCase          =      (generated - audio).abs().sum()
			__lowerCamelCase          =      (generated - audio).abs().max()

			if args.save:
						pipe.save_pretrained(args.checkpoint_path     )

			print("""Diff sum""",			lowercase__     )
			print("""Diff max""",			lowercase__     )

			assert diff_max < 1e-3, f'Diff max: {diff_max} is too much :-/'

			print(f'Conversion for {model_name} successful!'     )


if __name__ == "__main__":
					__magic_name__						: str    =				argparse.ArgumentParser()

					parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
					parser.add_argument(
					    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
					)
					parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
					__magic_name__						: Optional[int]    =				parser.parse_args()

					main(args)








"""simple docstring"""




import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__						: List[str]    =				logging.get_logger(__name__)

__magic_name__						: Tuple    =				{
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''encodec'''




							def __init__(     self:    Union[str, Any]     ,							A_:    Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,							A_:    Any=2_40_00     ,							A_:    List[Any]=1     ,							A_:    Optional[Any]=False     ,							A_:    Optional[int]=None     ,							A_:    int=None     ,							A_:    List[str]=1_28     ,							A_:    Union[str, Any]=32     ,							A_:    List[str]=1     ,							A_:    Dict=[8, 5, 4, 2]     ,							A_:    List[Any]="weight_norm"     ,							A_:    Any=7     ,							A_:    List[Any]=7     ,							A_:    Tuple=3     ,							A_:    List[str]=2     ,							A_:    Optional[Any]=True     ,							A_:    Optional[int]="reflect"     ,							A_:    Dict=2     ,							A_:    Union[str, Any]=2     ,							A_:    Union[str, Any]=1.0     ,							A_:    List[str]=10_24     ,							A_:    str=None     ,							A_:    List[str]=True     ,							**A_:    int     ,							):
										__lowerCamelCase          =      target_bandwidths
										__lowerCamelCase          =      sampling_rate
										__lowerCamelCase          =      audio_channels
										__lowerCamelCase          =      normalize
										__lowerCamelCase          =      chunk_length_s
										__lowerCamelCase          =      overlap
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      num_filters
										__lowerCamelCase          =      num_residual_layers
										__lowerCamelCase          =      upsampling_ratios
										__lowerCamelCase          =      norm_type
										__lowerCamelCase          =      kernel_size
										__lowerCamelCase          =      last_kernel_size
										__lowerCamelCase          =      residual_kernel_size
										__lowerCamelCase          =      dilation_growth_rate
										__lowerCamelCase          =      use_causal_conv
										__lowerCamelCase          =      pad_mode
										__lowerCamelCase          =      compress
										__lowerCamelCase          =      num_lstm_layers
										__lowerCamelCase          =      trim_right_ratio
										__lowerCamelCase          =      codebook_size
										__lowerCamelCase          =      codebook_dim if codebook_dim is not None else hidden_size
										__lowerCamelCase          =      use_conv_shortcut

										if self.norm_type not in ["weight_norm", "time_group_norm"]:
													raise ValueError(
													    f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'   )

										super().__init__(**A_   )




							@property
							def   __a   (     self:    Union[str, Any]   ):
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate   )




							@property
							def   __a   (     self:    List[Any]   ):
										if self.chunk_length_s is None or self.overlap is None:
													return None
										else:
													return max(1     ,							int((1.0 - self.overlap) * self.chunk_length   )   )




							@property
							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )




							@property
							def   __a   (     self:    List[Any]   ):
										return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )




