






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


UpperCAmelCase__							  =						{
    "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 		_a       ( a					:str      ,				a					:Tuple					)				->     Optional[int]:
 return torch.atana(a      ,				a					) / math.pi * 2
def 		_a       ( a					:Optional[int]					)				->     Any:
 a						    = torch.sin(t * math.pi / 2					) ** 2
 a						    = (1 - sigma**2) ** 0.5
 return alpha_sigma_to_t(a      ,				a					)






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      pass






class 						lowercase_   (      nn.Module  ):






      '''simple docstring'''




      def __init__( self :		str					,   __UpperCAmelCase :		Optional[Any]    )       ->Any:




       """simple docstring"""


       super().__init__()

       a						    = DiffusionAttnUnetaD(__UpperCAmelCase					,   n_attn_layers=4    )
       a						    = deepcopy(self.diffusion    )
       a						    = torch.quasirandom.SobolEngine(1					,   scramble=__UpperCAmelCase    )
def 		_a       ( a					:List[str]					)				->     List[Any]:
 a						    = MODELS_MAP[model_name]['''url''']
 os.system(F"""wget {url} ./"""					)

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


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

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

UpperCAmelCase__							  =						{
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}
def 		_a       ( a					:List[str]					)				->     Optional[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       ( a					:List[Any]					)				->     List[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 		_a       ( a					:Optional[Any]      ,				a					:Optional[int]=13					)				->     Union[str, Any]:
 a						    = input_string

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

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

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

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

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

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

 a						    = string_left[1:]

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

 if not isinstance(a      ,				a					):
  a						    = prefix + '''.''' + new_layer + '''.''' + string_left
 else:
  a						    = [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
 return new_string
def 		_a       ( a					:str					)				->     Union[str, Any]:
 a						    = {}
 for k, v in state_dict.items():
  if k.endswith('''kernel'''					):
   # up- and downsample layers, don't have trainable weights
   continue

  a						    = rename(a					)

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

 return new_state_dict
def 		_a       ( a					:int      ,				a					:List[str]      ,				a					:Optional[int]					)				->     str:
 if len(a					) == 1:
  if len(v.shape					) == 3:
   # weight
   a						    = v[:, :, 0]
  else:
   # bias
   a						    = v
 else:
  # qkv matrices
  a						    = v.shape[0]
  a						    = trippled_shape // 3
  for i in range(3					):
   if len(v.shape					) == 3:
    a						    = v[i * single_shape : (i + 1) * single_shape, :, 0]
   else:
    a						    = v[i * single_shape : (i + 1) * single_shape]
 return new_state_dict
def 		_a       ( a					:Union[str, Any]					)				->     Tuple:
 a						    = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''					)

 a						    = 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()}"""
  a						    = download(a					)

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

 a						    = Object()
 a						    = sample_size
 a						    = sample_rate
 a						    = 0

 a						    = UNetaDModel(sample_size=a      ,				sample_rate=a					)
 a						    = diffusers_model.state_dict()

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

 a						    = set(renamed_state_dict.keys()					) - set(diffusers_state_dict.keys()					)
 a						    = 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":
   a						    = value.squeeze()

  a						    = value

 diffusers_model.load_state_dict(a					)

 a						    = 100
 a						    = 33

 a						    = IPNDMScheduler(num_train_timesteps=a					)

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

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

 a						    = DanceDiffusionPipeline(unet=a      ,				scheduler=a					)

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

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

 a						    = (generated - audio).abs().sum()
 a						    = (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__":
     UpperCAmelCase__							  =						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.")
     UpperCAmelCase__							  =						parser.parse_args()

     main(args)










from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, torch.Tensor]:
 a						    = []
 a						    = []
 a						    = []

 for rt in rc.restypes:
  a						    = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
  restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
  a						    = {name: i for i, name in enumerate(a					)}
  restype_atomaa_to_atomaa_list.append(
      [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

  restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

 # Add dummy mapping for restype 'UNK'
 restype_atomaa_to_atomaa_list.append([0] * 14					)
 restype_atomaa_to_atomaa_list.append([0] * 37					)
 restype_atomaa_mask_list.append([0.0] * 14					)

 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.floataa      ,				device=protein['''aatype'''].device      ,				)
 a						    = protein['''aatype'''].to(torch.long					)

 # create the mapping for (residx, atom14) --> atom37, i.e. an array
 # with shape (num_res, 14) containing the atom37 indices for this protein
 a						    = restype_atomaa_to_atomaa[protein_aatype]
 a						    = restype_atomaa_mask[protein_aatype]

 a						    = residx_atomaa_mask
 a						    = residx_atomaa_to_atomaa.long()

 # create the gather indices for mapping back
 a						    = restype_atomaa_to_atomaa[protein_aatype]
 a						    = residx_atomaa_to_atomaa.long()

 # create the corresponding mask
 a						    = torch.zeros([21, 37]      ,				dtype=torch.floataa      ,				device=protein['''aatype'''].device					)
 for restype, restype_letter in enumerate(rc.restypes					):
  a						    = rc.restype_atoa[restype_letter]
  a						    = rc.residue_atoms[restype_name]
  for atom_name in atom_names:
   a						    = rc.atom_order[atom_name]
   a						    = 1

 a						    = restype_atomaa_mask[protein_aatype]
 a						    = residx_atomaa_mask

 return protein

def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, np.ndarray]:
 a						    = tree_map(lambda a					: torch.tensor(a      ,				device=batch['''aatype'''].device					)      ,				a      ,				np.ndarray					)
 a						    = tensor_tree_map(lambda a					: np.array(a					)      ,				make_atomaa_masks(a					)					)
 return out


