
'''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


__lowercase							:   int					=     {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 131072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
}




def lowercase_	(   _lowercase			,       _lowercase				)     ->							List[Any]:
     '''simple docstring'''

     return torch.atana(_lowercase			,       _lowercase				) / math.pi * 2




def lowercase_	(   _lowercase				)     ->							List[str]:
     '''simple docstring'''

     lowerCamelCase_					:      List[str]     =		torch.sin(t * math.pi / 2				) ** 2
     lowerCamelCase_					:      Any     =		(1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(_lowercase			,       _lowercase				)

class 	__lowercase				(  _lowercase    ):
   pass

class 	__lowercase				(  nn.Module    ):

   def __init__(self			,   A      ):
        super().__init__()

        lowerCamelCase_					:      str     =		DiffusionAttnUnetaD(A			,   n_attn_layers=4      )
        lowerCamelCase_					:      str     =		deepcopy(self.diffusion      )
        lowerCamelCase_					:      Union[str, Any]     =		torch.quasirandom.SobolEngine(1			,   scramble=A      )




def lowercase_	(   _lowercase				)     ->							Optional[Any]:
     '''simple docstring'''

     lowerCamelCase_					:      Optional[Any]     =		MODELS_MAP[model_name]['''url''']
     os.system(F"""wget {url} ./"""				)

     return F"""./{model_name}.ckpt"""


__lowercase							:   int					=     {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
__lowercase							:   int					=     {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
__lowercase							:   List[Any]					=     {
    '''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''',
}
__lowercase							:   int					=     {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

__lowercase							:   Optional[Any]					=     {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

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




def lowercase_	(   _lowercase				)     ->							Dict:
     '''simple docstring'''

     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 lowercase_	(   _lowercase				)     ->							Optional[int]:
     '''simple docstring'''

     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 lowercase_	(   _lowercase			,       _lowercase=13				)     ->							Optional[Any]:
     '''simple docstring'''

     lowerCamelCase_					:      Union[str, Any]     =		input_string

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

     lowerCamelCase_					:      int     =		0
     if string.startswith('''net.3.'''				):
          depth += 1
          lowerCamelCase_					:      List[str]     =		string[6:]
     elif string.startswith('''net.'''				):
          lowerCamelCase_					:      Optional[Any]     =		string[4:]

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

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

     # mid block
     if string[:2].isdigit():
          lowerCamelCase_					:      int     =		string[:2]
          lowerCamelCase_					:      Tuple     =		string[2:]
     else:
          lowerCamelCase_					:      Any     =		string[0]
          lowerCamelCase_					:      Optional[int]     =		string[1:]

     if depth == max_depth:
          lowerCamelCase_					:      Union[str, Any]     =		MID_NUM_TO_LAYER[layer_num]
          lowerCamelCase_					:      Optional[Any]     =		'''mid_block'''
     elif depth > 0 and int(_lowercase				) < 7:
          lowerCamelCase_					:      Tuple     =		DOWN_NUM_TO_LAYER[layer_num]
          lowerCamelCase_					:      Dict     =		F"""down_blocks.{depth}"""
     elif depth > 0 and int(_lowercase				) > 7:
          lowerCamelCase_					:      Optional[int]     =		UP_NUM_TO_LAYER[layer_num]
          lowerCamelCase_					:      Tuple     =		F"""up_blocks.{max_depth - depth - 1}"""
     elif depth == 0:
          lowerCamelCase_					:      int     =		DEPTH_0_TO_LAYER[layer_num]
          lowerCamelCase_					:      Any     =		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_					:      Union[str, Any]     =		string_left[1:]

     if "resnets" in new_layer:
          lowerCamelCase_					:      Optional[Any]     =		convert_resconv_naming(_lowercase				)
     elif "attentions" in new_layer:
          lowerCamelCase_					:      str     =		convert_attn_naming(_lowercase				)
          lowerCamelCase_					:      Dict     =		new_string_left

     if not isinstance(_lowercase			,       _lowercase				):
          lowerCamelCase_					:      Tuple     =		prefix + '''.''' + new_layer + '''.''' + string_left
     else:
          lowerCamelCase_					:      str     =		[prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
     return new_string




def lowercase_	(   _lowercase				)     ->							List[str]:
     '''simple docstring'''

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

          lowerCamelCase_					:      List[Any]     =		rename(_lowercase				)

          # check if we need to transform from Conv => Linear for attention
          if isinstance(_lowercase			,       _lowercase				):
               lowerCamelCase_					:      Any     =		transform_conv_attns(_lowercase			,       _lowercase			,       _lowercase				)
          else:
               lowerCamelCase_					:      List[Any]     =		v

     return new_state_dict




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							Dict:
     '''simple docstring'''

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




def lowercase_	(   _lowercase				)     ->							Optional[Any]:
     '''simple docstring'''

     lowerCamelCase_					:      Optional[Any]     =		torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)

     lowerCamelCase_					:      Tuple     =		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_					:      Tuple     =		download(_lowercase				)

     lowerCamelCase_					:      Optional[int]     =		MODELS_MAP[model_name]['''sample_rate''']
     lowerCamelCase_					:      Any     =		MODELS_MAP[model_name]['''sample_size''']

     lowerCamelCase_					:      Optional[Any]     =		Object()
     lowerCamelCase_					:      List[str]     =		sample_size
     lowerCamelCase_					:      List[Any]     =		sample_rate
     lowerCamelCase_					:      Tuple     =		0

     lowerCamelCase_					:      Union[str, Any]     =		UNetaDModel(sample_size=_lowercase			,       sample_rate=_lowercase				)
     lowerCamelCase_					:      int     =		diffusers_model.state_dict()

     lowerCamelCase_					:      int     =		DiffusionUncond(_lowercase				)
     orig_model.load_state_dict(torch.load(args.model_path			,       map_location=_lowercase				)['''state_dict''']				)
     lowerCamelCase_					:      List[str]     =		orig_model.diffusion_ema.eval()
     lowerCamelCase_					:      str     =		orig_model.state_dict()
     lowerCamelCase_					:      Tuple     =		rename_orig_weights(_lowercase				)

     lowerCamelCase_					:      List[Any]     =		set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
     lowerCamelCase_					:      Dict     =		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_					:      Dict     =		value.squeeze()

          lowerCamelCase_					:      Any     =		value

     diffusers_model.load_state_dict(_lowercase				)

     lowerCamelCase_					:      int     =		100
     lowerCamelCase_					:      List[str]     =		33

     lowerCamelCase_					:      Optional[Any]     =		IPNDMScheduler(num_train_timesteps=_lowercase				)

     lowerCamelCase_					:      Optional[int]     =		torch.manual_seed(_lowercase				)
     lowerCamelCase_					:      Optional[Any]     =		torch.randn([1, 2, config.sample_size]			,       generator=_lowercase				).to(_lowercase				)

     lowerCamelCase_					:      Union[str, Any]     =		torch.linspace(1			,       0			,       steps + 1			,       device=_lowercase				)[:-1]
     lowerCamelCase_					:      Optional[Any]     =		get_crash_schedule(_lowercase				)

     lowerCamelCase_					:      List[Any]     =		DanceDiffusionPipeline(unet=_lowercase			,       scheduler=_lowercase				)

     lowerCamelCase_					:      List[str]     =		torch.manual_seed(33				)
     lowerCamelCase_					:      str     =		pipe(num_inference_steps=_lowercase			,       generator=_lowercase				).audios

     lowerCamelCase_					:      str     =		sampling.iplms_sample(_lowercase			,       _lowercase			,       _lowercase			,       {}				)
     lowerCamelCase_					:      Any     =		generated.clamp(-1			,       1				)

     lowerCamelCase_					:      List[str]     =		(generated - audio).abs().sum()
     lowerCamelCase_					:      List[str]     =		(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__":
     __lowercase							:   List[Any]					=     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.''')
     __lowercase							:   List[Any]					=     parser.parse_args()

     main(args)




'''simple docstring'''


from __future__ import annotations

from math import pi, sqrt




def lowercase_	(   _lowercase			,       _lowercase				)     ->							tuple:
					'''simple docstring'''

					if inductance <= 0:
										raise ValueError('''Inductance cannot be 0 or negative'''				)

					elif capacitance <= 0:
										raise ValueError('''Capacitance cannot be 0 or negative'''				)

					else:
										return (
										    "Resonant frequency",
										    float(1 / (2 * pi * (sqrt(inductance * capacitance				)))				),
										)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


