"""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__ 	=     {
    '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       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->int:
  return torch.atana(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ) / math.pi * 2







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->List[str]:
  a__:					Tuple      							=     torch.sin(t * math.pi / 2       ) ** 2
  a__:					List[Any]      							=     (1 - sigma**2) ** 0.5
  return alpha_sigma_to_t(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )




class        __snake_case	(					__lowerCAmelCase    ):
      pass







class        __snake_case	(					nn.Module    ):



      def __init__(						self   ,      lowercase)     ->      List[str]:





        '''simple docstring'''





        super().__init__()

        a__:					Dict      							=     DiffusionAttnUnetaD(lowercase   ,      n_attn_layers=4)
        a__:					Any      							=     deepcopy(self.diffusion)
        a__:					Dict      							=     torch.quasirandom.SobolEngine(1   ,      scramble=lowercase)







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->int:
  a__:					Optional[int]      							=     MODELS_MAP[model_name]['url']
  os.system(F'wget {url} ./'       )

  return F'./{model_name}.ckpt'


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

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

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







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->List[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 						__a       (     _SCREAMING_SNAKE_CASE       )     ->int:
  for key, value in ATTN_MAP.items():
    if name.startswith(_SCREAMING_SNAKE_CASE       ) and not isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
      return name.replace(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
    elif name.startswith(_SCREAMING_SNAKE_CASE       ):
      return [name.replace(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ) for v in value]
  raise ValueError(F'Attn error with {name}'       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=13       )     ->Tuple:
  a__:					int      							=     input_string

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

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

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

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

  # mid block
  if string[:2].isdigit():
    a__:					List[Any]      							=     string[:2]
    a__:					List[Any]      							=     string[2:]
  else:
    a__:					Tuple      							=     string[0]
    a__:					str      							=     string[1:]

  if depth == max_depth:
    a__:					Union[str, Any]      							=     MID_NUM_TO_LAYER[layer_num]
    a__:					List[str]      							=     'mid_block'
  elif depth > 0 and int(_SCREAMING_SNAKE_CASE       ) < 7:
    a__:					Optional[Any]      							=     DOWN_NUM_TO_LAYER[layer_num]
    a__:					Dict      							=     F'down_blocks.{depth}'
  elif depth > 0 and int(_SCREAMING_SNAKE_CASE       ) > 7:
    a__:					Optional[int]      							=     UP_NUM_TO_LAYER[layer_num]
    a__:					List[str]      							=     F'up_blocks.{max_depth - depth - 1}'
  elif depth == 0:
    a__:					int      							=     DEPTH_0_TO_LAYER[layer_num]
    a__:					Tuple      							=     F'up_blocks.{max_depth - 1}' if int(_SCREAMING_SNAKE_CASE       ) > 3 else 'down_blocks.0'

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

  a__:					Tuple      							=     string_left[1:]

  if "resnets" in new_layer:
    a__:					Dict      							=     convert_resconv_naming(_SCREAMING_SNAKE_CASE       )
  elif "attentions" in new_layer:
    a__:					int      							=     convert_attn_naming(_SCREAMING_SNAKE_CASE       )
    a__:					Any      							=     new_string_left

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







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

    a__:					List[Any]      							=     rename(_SCREAMING_SNAKE_CASE       )

    # check if we need to transform from Conv => Linear for attention
    if isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
      a__:					Optional[Any]      							=     transform_conv_attns(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
    else:
      a__:					int      							=     v

  return new_state_dict







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







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Dict:
  a__:					Tuple      							=     torch.device('cuda' if torch.cuda.is_available() else 'cpu'       )

  a__:					List[Any]      							=     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__:					Optional[Any]      							=     download(_SCREAMING_SNAKE_CASE       )

  a__:					int      							=     MODELS_MAP[model_name]['sample_rate']
  a__:					Union[str, Any]      							=     MODELS_MAP[model_name]['sample_size']

  a__:					Any      							=     Object()
  a__:					Optional[int]      							=     sample_size
  a__:					Dict      							=     sample_rate
  a__:					List[Any]      							=     0

  a__:					Tuple      							=     UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE		,					sample_rate=_SCREAMING_SNAKE_CASE       )
  a__:					Dict      							=     diffusers_model.state_dict()

  a__:					Dict      							=     DiffusionUncond(_SCREAMING_SNAKE_CASE       )
  orig_model.load_state_dict(torch.load(args.model_path		,					map_location=_SCREAMING_SNAKE_CASE       )['state_dict']       )
  a__:					Optional[Any]      							=     orig_model.diffusion_ema.eval()
  a__:					Tuple      							=     orig_model.state_dict()
  a__:					Tuple      							=     rename_orig_weights(_SCREAMING_SNAKE_CASE       )

  a__:					int      							=     set(renamed_state_dict.keys()       ) - set(diffusers_state_dict.keys()       )
  a__:					str      							=     set(diffusers_state_dict.keys()       ) - set(renamed_state_dict.keys()       )

  assert len(_SCREAMING_SNAKE_CASE       ) == 0, F'Problem with {renamed_minus_diffusers}'
  assert all(k.endswith('kernel'       ) for k in list(_SCREAMING_SNAKE_CASE       )       ), 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__:					str      							=     value.squeeze()

    a__:					Optional[Any]      							=     value

  diffusers_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

  a__:					Any      							=     100
  a__:					List[Any]      							=     33

  a__:					Optional[int]      							=     IPNDMScheduler(num_train_timesteps=_SCREAMING_SNAKE_CASE       )

  a__:					List[str]      							=     torch.manual_seed(_SCREAMING_SNAKE_CASE       )
  a__:					Optional[Any]      							=     torch.randn([1, 2, config.sample_size]		,					generator=_SCREAMING_SNAKE_CASE       ).to(_SCREAMING_SNAKE_CASE       )

  a__:					Optional[int]      							=     torch.linspace(1		,					0		,					steps + 1		,					device=_SCREAMING_SNAKE_CASE       )[:-1]
  a__:					Any      							=     get_crash_schedule(_SCREAMING_SNAKE_CASE       )

  a__:					List[Any]      							=     DanceDiffusionPipeline(unet=_SCREAMING_SNAKE_CASE		,					scheduler=_SCREAMING_SNAKE_CASE       )

  a__:					Union[str, Any]      							=     torch.manual_seed(33       )
  a__:					Optional[Any]      							=     pipe(num_inference_steps=_SCREAMING_SNAKE_CASE		,					generator=_SCREAMING_SNAKE_CASE       ).audios

  a__:					Tuple      							=     sampling.iplms_sample(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					{}       )
  a__:					List[str]      							=     generated.clamp(-1		,					1       )

  a__:					List[Any]      							=     (generated - audio).abs().sum()
  a__:					List[Any]      							=     (generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
 lowercase__ 	=     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__ 	=     parser.parse_args()

 main(args)



"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

lowercase__ 	=     '2.13.1'

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('3.7'):
 raise ImportWarning(
     'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
 )

if version.parse(pyarrow.__version__).major < 8:
 raise ImportWarning(
     'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'
     'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'
 )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

lowercase__ 	=     concatenate_datasets
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadManager
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


