

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      :					Tuple					    =      {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 6_5536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 6_5536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 13_1072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
}





def      A__					(			__A  :						int     ,				__A  :						Dict			)					->List[Any]:
      return torch.atana(__A     ,				__A			) / math.pi * 2





def      A__					(			__A  :						Tuple			)					->int:
      __A    =torch.sin(t * math.pi / 2			) ** 2
      __A    =(1 - sigma**2) ** 0.5
      return alpha_sigma_to_t(__A     ,				__A			)





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       pass





class        lowerCAmelCase__  (					nn.Module			):



       '''simple docstring'''







       def __init__(							self ,   lowercase__     ):







             '''simple docstring'''



             super().__init__()

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





def      A__					(			__A  :						int			)					->int:
      __A    =MODELS_MAP[model_name]['''url''']
      os.system(F'''wget {url} ./'''			)

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


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

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

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





def      A__					(			__A  :						List[str]			)					->List[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      A__					(			__A  :						Tuple			)					->str:
      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  :						List[Any]     ,				__A  :						int=13			)					->Optional[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  :						Optional[Any]			)					->int:
      __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  :						Optional[int]     ,				__A  :						str			)					->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  :						Tuple			)					->List[Any]:
      __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    =1_00
      __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__":
   _lowerCamelCase      :					Optional[int]					    =      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      :					List[Any]					    =      parser.parse_args()

   main(args)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


_lowerCamelCase      :					int					    =      {
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ASTForAudioClassification''',
       '''ASTModel''',
       '''ASTPreTrainedModel''',
   ]

try:
   if not is_speech_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Optional[Any]					    =      ['''ASTFeatureExtractor''']

if TYPE_CHECKING:
   from .configuration_audio_spectrogram_transformer import (
       AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ASTConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_audio_spectrogram_transformer import (
          AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ASTForAudioClassification,
          ASTModel,
          ASTPreTrainedModel,
      )

   try:
      if not is_speech_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
   import sys

   _lowerCamelCase      :					Optional[int]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


