




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


_lowerCamelCase     					=		{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 6_55_36,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 6_55_36,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 13_10_72,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
}




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Dict			,	__UpperCamelCase     :		int )    ->				str:
    return torch.atana(a__			,	a__ ) / math.pi * 2




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		int )    ->				Dict:
    UpperCAmelCase_									=	torch.sin(t * math.pi / 2 ) ** 2
    UpperCAmelCase_									=	(1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(a__			,	a__ )



class      a	(  _A     ):

     '''simple docstring'''





     pass



class      a	(  nn.Module     ):

     '''simple docstring'''





     def __init__(      self  :     Optional[int]   ,       __snake_case  :     Optional[int]							):
         super().__init__()

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




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Union[str, Any] )    ->				Optional[int]:
    UpperCAmelCase_									=	MODELS_MAP[model_name]['''url''']
    os.system(f'wget {url} ./' )

    return f'./{model_name}.ckpt'


_lowerCamelCase     					=		{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
_lowerCamelCase     					=		{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
_lowerCamelCase     					=		{
    '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',
}
_lowerCamelCase     					=		{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

_lowerCamelCase     					=		{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

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




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Tuple )    ->				Optional[Any]:


    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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Union[str, Any] )    ->				Any:


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




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Optional[int]			,	__UpperCamelCase     :		List[str]=13 )    ->				Tuple:
    UpperCAmelCase_									=	input_string

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

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

    while string.startswith('''main.7.''' ):
        depth += 1
        UpperCAmelCase_									=	string[7:]

    if string.startswith('''main.''' ):
        UpperCAmelCase_									=	string[5:]

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

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

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

    UpperCAmelCase_									=	string_left[1:]

    if "resnets" in new_layer:
        UpperCAmelCase_									=	convert_resconv_naming(a__ )
    elif "attentions" in new_layer:
        UpperCAmelCase_									=	convert_attn_naming(a__ )
        UpperCAmelCase_									=	new_string_left

    if not isinstance(a__			,	a__ ):
        UpperCAmelCase_									=	prefix + '''.''' + new_layer + '''.''' + string_left
    else:
        UpperCAmelCase_									=	[prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
    return new_string




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Any )    ->				str:
    UpperCAmelCase_									=	{}
    for k, v in state_dict.items():
        if k.endswith('''kernel''' ):
            # up- and downsample layers, don't have trainable weights
            continue

        UpperCAmelCase_									=	rename(a__ )

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

    return new_state_dict




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Union[str, Any]			,	__UpperCamelCase     :		int			,	__UpperCamelCase     :		List[str] )    ->				Dict:


    if len(a__ ) == 1:
        if len(v.shape ) == 3:
            # weight
            UpperCAmelCase_									=	v[:, :, 0]
        else:
            # bias
            UpperCAmelCase_									=	v
    else:
        # qkv matrices
        UpperCAmelCase_									=	v.shape[0]
        UpperCAmelCase_									=	trippled_shape // 3
        for i in range(3 ):
            if len(v.shape ) == 3:
                UpperCAmelCase_									=	v[i * single_shape : (i + 1) * single_shape, :, 0]
            else:
                UpperCAmelCase_									=	v[i * single_shape : (i + 1) * single_shape]
    return new_state_dict




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Optional[int] )    ->				Union[str, Any]:
    UpperCAmelCase_									=	torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )

    UpperCAmelCase_									=	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()}'
        UpperCAmelCase_									=	download(a__ )

    UpperCAmelCase_									=	MODELS_MAP[model_name]['''sample_rate''']
    UpperCAmelCase_									=	MODELS_MAP[model_name]['''sample_size''']

    UpperCAmelCase_									=	Object()
    UpperCAmelCase_									=	sample_size
    UpperCAmelCase_									=	sample_rate
    UpperCAmelCase_									=	0

    UpperCAmelCase_									=	UNetaDModel(sample_size=a__			,	sample_rate=a__ )
    UpperCAmelCase_									=	diffusers_model.state_dict()

    UpperCAmelCase_									=	DiffusionUncond(a__ )
    orig_model.load_state_dict(torch.load(args.model_path			,	map_location=a__ )['''state_dict'''] )
    UpperCAmelCase_									=	orig_model.diffusion_ema.eval()
    UpperCAmelCase_									=	orig_model.state_dict()
    UpperCAmelCase_									=	rename_orig_weights(a__ )

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

    assert len(a__ ) == 0, f'Problem with {renamed_minus_diffusers}'
    assert all(k.endswith('''kernel''' ) for k in list(a__ ) ), 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":
            UpperCAmelCase_									=	value.squeeze()

        UpperCAmelCase_									=	value

    diffusers_model.load_state_dict(a__ )

    UpperCAmelCase_									=	100
    UpperCAmelCase_									=	33

    UpperCAmelCase_									=	IPNDMScheduler(num_train_timesteps=a__ )

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

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

    UpperCAmelCase_									=	DanceDiffusionPipeline(unet=a__			,	scheduler=a__ )

    UpperCAmelCase_									=	torch.manual_seed(33 )
    UpperCAmelCase_									=	pipe(num_inference_steps=a__			,	generator=a__ ).audios

    UpperCAmelCase_									=	sampling.iplms_sample(a__			,	a__			,	a__			,	{} )
    UpperCAmelCase_									=	generated.clamp(-1			,	1 )

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

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

    print('''Diff sum'''			,	a__ )
    print('''Diff max'''			,	a__ )

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

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


if __name__ == "__main__":
 _lowerCamelCase     					=		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.')
 _lowerCamelCase     					=		parser.parse_args()

 main(args)








'''simple docstring'''



from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class 		lowerCAmelCase__  ( a					,   a   ):



 """simple docstring"""



 @register_to_config
 def __init__(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			int = 768   ,				)    ->					int:


      """simple docstring"""




      super().__init__()

      __SCREAMING_SNAKE_CASE      = nn.Parameter(torch.zeros(1   ,				__SCREAMING_SNAKE_CASE     )     )
      __SCREAMING_SNAKE_CASE      = nn.Parameter(torch.ones(1   ,				__SCREAMING_SNAKE_CASE     )     )
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, torch.device]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[torch.dtype] = None   ,				)    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = nn.Parameter(self.mean.to(__SCREAMING_SNAKE_CASE     ).to(__SCREAMING_SNAKE_CASE     )     )
      __SCREAMING_SNAKE_CASE      = nn.Parameter(self.std.to(__SCREAMING_SNAKE_CASE     ).to(__SCREAMING_SNAKE_CASE     )     )
      return self
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = (embeds - self.mean) * 1.0 / self.std
      return embeds


 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = (embeds * self.std) + self.mean
      return embeds






