'''simple docstring'''




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__snake_case  : List[str]		       =    logging.get_logger(__name__)

__snake_case  : Union[str, Any]		       =    {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'mask_emb': 'masked_spec_embed',
}





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Any,  _UpperCamelCase       :      Union[str, Any],  _UpperCamelCase       :      int,  _UpperCamelCase       :      Any,  _UpperCamelCase       :      Union[str, Any]  )      -> str:
    for attribute in key.split('''.'''  ):
        A_      =  getattr(_UpperCamelCase,  _UpperCamelCase  )

    if weight_type is not None:
        A_      =  getattr(_UpperCamelCase,  _UpperCamelCase  ).shape
    else:
        A_      =  hf_pointer.shape

    assert hf_shape == value.shape, (
        F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
        F''' {value.shape} for {full_name}'''
    )

    if weight_type == "weight":
        A_      =  value
    elif weight_type == "weight_g":
        A_      =  value
    elif weight_type == "weight_v":
        A_      =  value
    elif weight_type == "bias":
        A_      =  value
    else:
        A_      =  value

    logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''  )





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Tuple,  _UpperCamelCase       :      List[str]  )      -> List[Any]:
    A_      =  []
    A_      =  fairseq_model.state_dict()

    A_      =  hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        A_      =  False
        if "conv_layers" in name:
            load_conv_layer(
                _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  hf_model.config.feat_extract_norm == '''group''',  )
            A_      =  True
        else:
            for key, mapped_key in MAPPING.items():
                A_      =  mapped_key

                if key in name:
                    A_      =  True
                    if "*" in mapped_key:
                        A_      =  name.split(_UpperCamelCase  )[0].split('''.'''  )[-2]
                        A_      =  mapped_key.replace('''*''',  _UpperCamelCase  )
                    if "weight_g" in name:
                        A_      =  '''weight_g'''
                    elif "weight_v" in name:
                        A_      =  '''weight_v'''
                    elif "weight" in name:
                        A_      =  '''weight'''
                    elif "bias" in name:
                        A_      =  '''bias'''
                    else:
                        A_      =  None
                    set_recursively(_UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase  )
                continue
        if not is_used:
            unused_weights.append(_UpperCamelCase  )

    logger.warning(F'''Unused weights: {unused_weights}'''  )





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int,  _UpperCamelCase       :      Union[str, Any],  _UpperCamelCase       :      List[Any],  _UpperCamelCase       :      Optional[Any],  _UpperCamelCase       :      Any  )      -> Any:
    A_      =  full_name.split('''conv_layers.'''  )[-1]
    A_      =  name.split('''.'''  )
    A_      =  int(items[0]  )
    A_      =  int(items[1]  )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
            )
            A_      =  value
            logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
            )
            A_      =  value
            logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                " found."
            )
            A_      =  value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
            )
            A_      =  value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
    else:
        unused_weights.append(_UpperCamelCase  )





def 	_UpperCAmelCase       (				_UpperCamelCase       :      str  )      -> List[Any]:
    A_      =  HubertConfig()
    A_      =  model.config

    A_      =  fs_config.activation_dropout
    A_      =  False
    A_      =  fs_config.attention_dropout
    A_      =  False
    A_      =  eval(fs_config.extractor_conv_feature_layers  )
    A_      =  [x[0] for x in conv_layers]
    A_      =  [x[1] for x in conv_layers]
    A_      =  [x[2] for x in conv_layers]
    A_      =  '''gelu'''
    A_      =  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    A_      =  False
    A_      =  0.0
    A_      =  0.0
    A_      =  fs_config.activation_fn
    A_      =  fs_config.dropout
    A_      =  fs_config.encoder_embed_dim
    A_      =  0.0_2
    A_      =  fs_config.encoder_ffn_embed_dim
    A_      =  1E-5
    A_      =  0.0
    A_      =  fs_config.encoder_attention_heads
    A_      =  fs_config.conv_pos_groups
    A_      =  fs_config.conv_pos
    A_      =  len(_UpperCamelCase  )
    A_      =  fs_config.encoder_layers

    return config





@torch.no_grad()
def 	_UpperCAmelCase       (				_UpperCamelCase       :      int,  _UpperCamelCase       :      Dict=None  )      -> Union[str, Any]:
    A_      =  distilhubert().model.model

    if config_path is not None:
        A_      =  HubertConfig.from_pretrained(_UpperCamelCase  )
    else:
        A_      =  convert_config(_UpperCamelCase  )
    A_      =  model.eval()

    A_      =  WavaVecaFeatureExtractor(
        feature_size=1,  sampling_rate=1_60_00,  padding_value=0,  do_normalize=_UpperCamelCase,  return_attention_mask=_UpperCamelCase,  )
    A_      =  HubertModel(_UpperCamelCase  )

    recursively_load_weights(_UpperCamelCase,  _UpperCamelCase  )

    feature_extractor.save_pretrained(_UpperCamelCase  )
    hf_model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
  __snake_case  : Union[str, Any]		       =    argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  __snake_case  : int		       =    parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   str  					=							(ScoreSdeVeScheduler,)
       __lowercase   :   List[str]  					=							()

       @property
       def    __A			( self					)						->	List[str]:
           A_      =  4
           A_      =  3
           A_      =  8
           A_      =  8

           A_      =  torch.rand((batch_size, num_channels, height, width)					)

           return sample

       @property
       def    __A			( self					)						->	Optional[Any]:
           A_      =  4
           A_      =  3
           A_      =  8
           A_      =  8

           A_      =  batch_size * num_channels * height * width
           A_      =  torch.arange(_SCREAMING_SNAKE_CASE					)
           A_      =  sample.reshape(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  sample / num_elems
           A_      =  sample.permute(3       ,	0       ,	1       ,	2					)

           return sample

       def    __A			( self					)						->	Union[str, Any]:
           def model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					):
               return sample * t / (t + 1)

           return model

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	Tuple:
           A_      =  {
               '''num_train_timesteps''': 2000,
               '''snr''': 0.15,
               '''sigma_min''': 0.01,
               '''sigma_max''': 1348,
               '''sampling_eps''': 1E-5,
           }

           config.update(**_SCREAMING_SNAKE_CASE					)
           return config

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  dict(self.forward_default_kwargs					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               A_      =  self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

               A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_correct(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  dict(self.forward_default_kwargs					)
           kwargs.update(_SCREAMING_SNAKE_CASE					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

               A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_correct(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"

       def    __A			( self					)						->	Any:
           for timesteps in [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Dict:
           for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,	[1, 100, 1000]					):
               self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE       ,	sigma_max=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Optional[int]:
           for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	int:
           A_      =  dict(self.forward_default_kwargs					)

           A_      =  self.scheduler_classes[0]
           A_      =  self.get_scheduler_config()
           A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

           A_      =  3

           A_      =  self.dummy_model()
           A_      =  self.dummy_sample_deter

           scheduler.set_sigmas(_SCREAMING_SNAKE_CASE					)
           scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
           A_      =  torch.manual_seed(0					)

           for i, t in enumerate(scheduler.timesteps					):
               A_      =  scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                       A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               with torch.no_grad():
                   A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
               A_  ,A_      =  output.prev_sample, output.prev_sample_mean

           A_      =  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
           A_      =  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

           assert np.isclose(result_sum.item()       ,	14_372_758_528.0					)
           assert np.isclose(result_mean.item()       ,	18_714_530.0					)

       def    __A			( self					)						->	List[Any]:
           A_      =  dict(self.forward_default_kwargs					)

           A_      =  kwargs.pop('''num_inference_steps'''       ,	_SCREAMING_SNAKE_CASE					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE       ,	'''set_timesteps'''					):
                   scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
               elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE       ,	'''set_timesteps'''					):
                   A_      =  num_inference_steps

               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	0       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	1       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               self.assertEqual(output_a.shape       ,	sample.shape					)
               self.assertEqual(output_a.shape       ,	output_a.shape					)






