




'''simple docstring'''



import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase_			=						logging.get_logger(__name__)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			Union[str, Any]	,					__A    :			List[str]  )			->			int:
   _SCREAMING_SNAKE_CASE	=				UniSpeechSatForSequenceClassification.from_pretrained(__A	,					config=__A  )
   _SCREAMING_SNAKE_CASE	=				downstream_dict["projector.weight"]
   _SCREAMING_SNAKE_CASE	=				downstream_dict["projector.bias"]
   _SCREAMING_SNAKE_CASE	=				downstream_dict["model.post_net.linear.weight"]
   _SCREAMING_SNAKE_CASE	=				downstream_dict["model.post_net.linear.bias"]
   return model
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]	,					__A    :			Optional[Any]	,					__A    :			Any  )			->			str:
   _SCREAMING_SNAKE_CASE	=				UniSpeechSatForAudioFrameClassification.from_pretrained(__A	,					config=__A  )
   _SCREAMING_SNAKE_CASE	=				downstream_dict["model.linear.weight"]
   _SCREAMING_SNAKE_CASE	=				downstream_dict["model.linear.bias"]
   return model
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict	,					__A    :			str	,					__A    :			str  )			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				UniSpeechSatForXVector.from_pretrained(__A	,					config=__A  )
   _SCREAMING_SNAKE_CASE	=				downstream_dict["connector.weight"]
   _SCREAMING_SNAKE_CASE	=				downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
      _SCREAMING_SNAKE_CASE	=				downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      _SCREAMING_SNAKE_CASE	=				downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   _SCREAMING_SNAKE_CASE	=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   _SCREAMING_SNAKE_CASE	=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   _SCREAMING_SNAKE_CASE	=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   _SCREAMING_SNAKE_CASE	=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   _SCREAMING_SNAKE_CASE	=				downstream_dict["objective.W"]
   return model
@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]	,					__A    :			Optional[int]	,					__A    :			Dict	,					__A    :			List[str]  )			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				torch.load(__A	,					map_location="cpu"  )

   _SCREAMING_SNAKE_CASE	=				checkpoint["Downstream"]

   _SCREAMING_SNAKE_CASE	=				UniSpeechSatConfig.from_pretrained(__A  )
   _SCREAMING_SNAKE_CASE	=				WavaVecaFeatureExtractor.from_pretrained(
       __A	,					return_attention_mask=__A	,					do_normalize=__A  )

   _SCREAMING_SNAKE_CASE	=				hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"  ):
      _SCREAMING_SNAKE_CASE	=				convert_classification(__A	,					__A	,					__A  )
   elif arch.endswith("ForAudioFrameClassification"  ):
      _SCREAMING_SNAKE_CASE	=				convert_diarization(__A	,					__A	,					__A  )
   elif arch.endswith("ForXVector"  ):
      _SCREAMING_SNAKE_CASE	=				convert_xvector(__A	,					__A	,					__A  )
   else:
      raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""  )

   if hf_config.use_weighted_layer_sum:
      _SCREAMING_SNAKE_CASE	=				checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(__A  )
   hf_model.save_pretrained(__A  )


if __name__ == "__main__":
 lowerCamelCase_			=						argparse.ArgumentParser()
 parser.add_argument(
     '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
 )
 parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
 parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
 lowerCamelCase_			=						parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







'''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


lowerCamelCase_			=						{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 6_55_36,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 6_55_36,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 13_10_72,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]	,					__A    :			List[Any]  )			->			Tuple:
   return torch.atana(__A	,					__A  ) / math.pi * 2
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				torch.sin(t * math.pi / 2  ) ** 2
   _SCREAMING_SNAKE_CASE	=				(1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(__A	,					__A  )







class 		lowercase_   (			A       ):


   """simple docstring"""

   pass







class 		lowercase_   (			nn.Module       ):


   """simple docstring"""

   def __init__(   self :     int  ,				__lowerCamelCase :     Dict   ):

      """simple docstring"""
      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 			SCREAMING_SNAKE_CASE_			(							__A    :			Any  )			->			Union[str, Any]:
   _SCREAMING_SNAKE_CASE	=				MODELS_MAP[model_name]["url"]
   os.system(f"""wget {url} ./"""  )

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


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

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

lowerCamelCase_			=						{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]  )			->			Dict:
   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 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]  )			->			Union[str, 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 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict	,					__A    :			List[Any]=13  )			->			List[Any]:
   _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(__A  ) < 7:
      _SCREAMING_SNAKE_CASE	=				DOWN_NUM_TO_LAYER[layer_num]
      _SCREAMING_SNAKE_CASE	=				f"""down_blocks.{depth}"""
   elif depth > 0 and int(__A  ) > 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(__A  ) > 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(__A  )
   elif "attentions" in new_layer:
      _SCREAMING_SNAKE_CASE	=				convert_attn_naming(__A  )
      _SCREAMING_SNAKE_CASE	=				new_string_left

   if not isinstance(__A	,					__A  ):
      _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 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]  )			->			int:
   _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(__A  )

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

   return new_state_dict
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Tuple	,					__A    :			Dict	,					__A    :			Tuple  )			->			Optional[int]:
   if len(__A  ) == 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 			SCREAMING_SNAKE_CASE_			(							__A    :			str  )			->			Optional[int]:
   _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(__A  )

   _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=__A	,					sample_rate=__A  )
   _SCREAMING_SNAKE_CASE	=				diffusers_model.state_dict()

   _SCREAMING_SNAKE_CASE	=				DiffusionUncond(__A  )
   orig_model.load_state_dict(torch.load(args.model_path	,					map_location=__A  )["state_dict"]  )
   _SCREAMING_SNAKE_CASE	=				orig_model.diffusion_ema.eval()
   _SCREAMING_SNAKE_CASE	=				orig_model.state_dict()
   _SCREAMING_SNAKE_CASE	=				rename_orig_weights(__A  )

   _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(__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":
         _SCREAMING_SNAKE_CASE	=				value.squeeze()

      _SCREAMING_SNAKE_CASE	=				value

   diffusers_model.load_state_dict(__A  )

   _SCREAMING_SNAKE_CASE	=				1_00
   _SCREAMING_SNAKE_CASE	=				33

   _SCREAMING_SNAKE_CASE	=				IPNDMScheduler(num_train_timesteps=__A  )

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

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

   _SCREAMING_SNAKE_CASE	=				DanceDiffusionPipeline(unet=__A	,					scheduler=__A  )

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

   _SCREAMING_SNAKE_CASE	=				sampling.iplms_sample(__A	,					__A	,					__A	,					{}  )
   _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"	,					__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_			=						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_			=						parser.parse_args()

 main(args)

