




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__			:       Optional[Any]         =							{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 48_000,
        'sample_size': 65_536,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 48_000,
        'sample_size': 65_536,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 48_000,
        'sample_size': 131_072,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 16_000,
        'sample_size': 65_536,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 16_000,
        'sample_size': 65_536,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 16_000,
        'sample_size': 65_536,
    },
}

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple      ,      __UpperCAmelCase						:     Optional[Any]							) ->			Optional[int]:
      return torch.atana(__UpperCAmelCase      ,      __UpperCAmelCase							) / math.pi * 2

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			Dict:
      SCREAMING_SNAKE_CASE_     						=      torch.sin(t * math.pi / 2							) ** 2
      SCREAMING_SNAKE_CASE_     						=      (1 - sigma**2) ** 0.5
      return alpha_sigma_to_t(__UpperCAmelCase      ,      __UpperCAmelCase							)





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 pass





class 	lowerCamelCase_							(				nn.Module ):


 '''simple docstring'''







 def __init__(							self  :		Dict    ,    _lowerCAmelCase  :		List[str]						):
       super().__init__()

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


def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			Optional[int]:
      SCREAMING_SNAKE_CASE_     						=      MODELS_MAP[model_name]['url']
      os.system(f"wget {url} ./"							)

      return f"./{model_name}.ckpt"


lowerCamelCase__			:       List[str]         =							{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
lowerCamelCase__			:       Any         =							{
    '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__			:       Optional[Any]         =							{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

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

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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int							) ->			str:
      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  UpperCAmelCase_							(				__UpperCAmelCase						:     int							) ->			Optional[int]:
      for key, value in ATTN_MAP.items():
            if name.startswith(__UpperCAmelCase							) and not isinstance(__UpperCAmelCase      ,      __UpperCAmelCase							):
                  return name.replace(__UpperCAmelCase      ,      __UpperCAmelCase							)
            elif name.startswith(__UpperCAmelCase							):
                  return [name.replace(__UpperCAmelCase      ,      __UpperCAmelCase							) for v in value]
      raise ValueError(f"Attn error with {name}"							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     Union[str, Any]=13							) ->			Dict:
      SCREAMING_SNAKE_CASE_     						=      input_string

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

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

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

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

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

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

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

      SCREAMING_SNAKE_CASE_     						=      string_left[1:]

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

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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			Optional[Any]:
      SCREAMING_SNAKE_CASE_     						=      {}
      for k, v in state_dict.items():
            if k.endswith('kernel'							):
                  # up- and downsample layers, don't have trainable weights
                  continue

            SCREAMING_SNAKE_CASE_     						=      rename(__UpperCAmelCase							)

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

      return new_state_dict

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Tuple      ,      __UpperCAmelCase						:     Union[str, Any]							) ->			int:
      if len(__UpperCAmelCase							) == 1:
            if len(v.shape							) == 3:
                  # weight
                  SCREAMING_SNAKE_CASE_     						=      v[:, :, 0]
            else:
                  # bias
                  SCREAMING_SNAKE_CASE_     						=      v
      else:
            # qkv matrices
            SCREAMING_SNAKE_CASE_     						=      v.shape[0]
            SCREAMING_SNAKE_CASE_     						=      trippled_shape // 3
            for i in range(3							):
                  if len(v.shape							) == 3:
                        SCREAMING_SNAKE_CASE_     						=      v[i * single_shape : (i + 1) * single_shape, :, 0]
                  else:
                        SCREAMING_SNAKE_CASE_     						=      v[i * single_shape : (i + 1) * single_shape]
      return new_state_dict

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]							) ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE_     						=      torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

      SCREAMING_SNAKE_CASE_     						=      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()}"
            SCREAMING_SNAKE_CASE_     						=      download(__UpperCAmelCase							)

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

      SCREAMING_SNAKE_CASE_     						=      Object()
      SCREAMING_SNAKE_CASE_     						=      sample_size
      SCREAMING_SNAKE_CASE_     						=      sample_rate
      SCREAMING_SNAKE_CASE_     						=      0

      SCREAMING_SNAKE_CASE_     						=      UNetaDModel(sample_size=__UpperCAmelCase      ,      sample_rate=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      diffusers_model.state_dict()

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

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

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

            SCREAMING_SNAKE_CASE_     						=      value

      diffusers_model.load_state_dict(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_     						=      1_00
      SCREAMING_SNAKE_CASE_     						=      33

      SCREAMING_SNAKE_CASE_     						=      IPNDMScheduler(num_train_timesteps=__UpperCAmelCase							)

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

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

      SCREAMING_SNAKE_CASE_     						=      DanceDiffusionPipeline(unet=__UpperCAmelCase      ,      scheduler=__UpperCAmelCase							)

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

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

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

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

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

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

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


if __name__ == "__main__":
   lowerCamelCase__			:       Dict         =							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__			:       str         =							parser.parse_args()

   main(args)





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


lowerCamelCase__			:       Dict         =							logging.get_logger(__name__)

lowerCamelCase__			:       Any         =							{
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   "speech_to_text"
 lowercase_       =   ["past_key_values"]
 lowercase_       =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



 def __init__(							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Optional[int]=10_000    ,    _lowerCAmelCase  :		str=12    ,    _lowerCAmelCase  :		Tuple=2_048    ,    _lowerCAmelCase  :		str=4    ,    _lowerCAmelCase  :		Tuple=6    ,    _lowerCAmelCase  :		Optional[int]=2_048    ,    _lowerCAmelCase  :		Dict=4    ,    _lowerCAmelCase  :		List[Any]=0.0    ,    _lowerCAmelCase  :		int=0.0    ,    _lowerCAmelCase  :		Optional[int]=True    ,    _lowerCAmelCase  :		Union[str, Any]=True    ,    _lowerCAmelCase  :		Any="relu"    ,    _lowerCAmelCase  :		Any=256    ,    _lowerCAmelCase  :		List[str]=0.1    ,    _lowerCAmelCase  :		Optional[Any]=0.0    ,    _lowerCAmelCase  :		Optional[int]=0.0    ,    _lowerCAmelCase  :		Tuple=0.02    ,    _lowerCAmelCase  :		List[str]=2    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		List[str]=1    ,    _lowerCAmelCase  :		Dict=0    ,    _lowerCAmelCase  :		Tuple=2    ,    _lowerCAmelCase  :		Optional[int]=6_000    ,    _lowerCAmelCase  :		Tuple=1_024    ,    _lowerCAmelCase  :		Any=2    ,    _lowerCAmelCase  :		str=(5, 5)    ,    _lowerCAmelCase  :		Optional[int]=1_024    ,    _lowerCAmelCase  :		List[Any]=80    ,    _lowerCAmelCase  :		List[Any]=1    ,    **_lowerCAmelCase  :		List[Any]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      vocab_size
       SCREAMING_SNAKE_CASE_     						=      d_model
       SCREAMING_SNAKE_CASE_     						=      encoder_ffn_dim
       SCREAMING_SNAKE_CASE_     						=      encoder_layers
       SCREAMING_SNAKE_CASE_     						=      encoder_attention_heads
       SCREAMING_SNAKE_CASE_     						=      decoder_ffn_dim
       SCREAMING_SNAKE_CASE_     						=      decoder_layers
       SCREAMING_SNAKE_CASE_     						=      decoder_attention_heads
       SCREAMING_SNAKE_CASE_     						=      dropout
       SCREAMING_SNAKE_CASE_     						=      attention_dropout
       SCREAMING_SNAKE_CASE_     						=      activation_dropout
       SCREAMING_SNAKE_CASE_     						=      activation_function
       SCREAMING_SNAKE_CASE_     						=      init_std
       SCREAMING_SNAKE_CASE_     						=      encoder_layerdrop
       SCREAMING_SNAKE_CASE_     						=      decoder_layerdrop
       SCREAMING_SNAKE_CASE_     						=      use_cache
       SCREAMING_SNAKE_CASE_     						=      encoder_layers
       SCREAMING_SNAKE_CASE_     						=      scale_embedding  # scale factor will be sqrt(d_model) if True
       SCREAMING_SNAKE_CASE_     						=      max_source_positions
       SCREAMING_SNAKE_CASE_     						=      max_target_positions
       SCREAMING_SNAKE_CASE_     						=      num_conv_layers
       SCREAMING_SNAKE_CASE_     						=      list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      conv_channels
       SCREAMING_SNAKE_CASE_     						=      input_feat_per_channel
       SCREAMING_SNAKE_CASE_     						=      input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, "
                 F"`config.num_conv_layers = {self.num_conv_layers}`."						)

       super().__init__(
           pad_token_id=_lowerCAmelCase    ,    bos_token_id=_lowerCAmelCase    ,    eos_token_id=_lowerCAmelCase    ,    is_encoder_decoder=_lowerCAmelCase    ,    decoder_start_token_id=_lowerCAmelCase    ,    **_lowerCAmelCase    ,    )