code: 
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  @dataclass
  class 						A__      (					snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	42
  
  
  
  
  
  
  class 						A__      (					snake_case__ ,      snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	1
      @register_to_config
      def __init__( self    ,      __snake_case = 4_0    ,      __snake_case = 0.002    ,      __snake_case = 80.0    ,      __snake_case = 0.5    ,      __snake_case = 1.0    ,      __snake_case = 7.0    ,      __snake_case = True    ,      ):
           # standard deviation of the initial noise distribution
           snake_case					=       sigma_max
  
           snake_case					=       np.linspace(0    ,      1    ,      __snake_case					)
           snake_case					=       self._convert_to_karras(__snake_case					)
           snake_case					=       self.sigma_to_t(__snake_case					)
  
           # setable values
           snake_case					=       None
           snake_case					=       torch.from_numpy(__snake_case					)
           snake_case					=       torch.from_numpy(__snake_case					)
           snake_case					=       False
           snake_case					=       False
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None					):
           if schedule_timesteps is None:
                snake_case					=       self.timesteps
  
           snake_case					=       (schedule_timesteps == timestep).nonzero()
           return indices.item()
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
           # Get sigma corresponding to timestep
           if isinstance(__snake_case    ,      torch.Tensor					):
                snake_case					=       timestep.to(self.timesteps.device					)
           snake_case					=       self.index_for_timestep(__snake_case					)
           snake_case					=       self.sigmas[step_idx]
  
           snake_case					=       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
           snake_case					=       True
           return sample
      def 	a_    ( self    ,      __snake_case					):
           if not isinstance(__snake_case    ,      np.ndarray					):
                snake_case					=       np.array(__snake_case    ,      dtype=np.floataa					)
  
           snake_case					=       1_0_0_0 * 0.25 * np.log(sigmas + 1E-44					)
  
           return timesteps
      def 	a_    ( self    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = None    ,      ):
           if num_inference_steps is None and timesteps is None:
                raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''					)
  
           if num_inference_steps is not None and timesteps is not None:
                raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''					)
  
           # Follow DDPMScheduler custom timesteps logic
           if timesteps is not None:
                for i in range(1    ,      len(__snake_case					)					):
                     if timesteps[i] >= timesteps[i - 1]:
                          raise ValueError('''`timesteps` must be in descending order.'''					)
  
                if timesteps[0] >= self.config.num_train_timesteps:
                     raise ValueError(
                         F'''`timesteps` must start before `self.config.train_timesteps`:'''
                         F''' {self.config.num_train_timesteps}.'''					)
  
                snake_case					=       np.array(__snake_case    ,      dtype=np.intaa					)
                snake_case					=       True
           else:
                if num_inference_steps > self.config.num_train_timesteps:
                     raise ValueError(
                         F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                         F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                         F''' maximal {self.config.num_train_timesteps} timesteps.'''					)
  
                snake_case					=       num_inference_steps
  
                snake_case					=       self.config.num_train_timesteps // self.num_inference_steps
                snake_case					=       (np.arange(0    ,      __snake_case					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
                snake_case					=       False
  
           # Map timesteps to Karras sigmas directly for multistep sampling
           # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
           snake_case					=       self.config.num_train_timesteps
           snake_case					=       timesteps[::-1].copy()
           snake_case					=       ramp / (num_train_timesteps - 1)
           snake_case					=       self._convert_to_karras(__snake_case					)
           snake_case					=       self.sigma_to_t(__snake_case					)
  
           snake_case					=       np.concatenate([sigmas, [self.sigma_min]]					).astype(np.floataa					)
           snake_case					=       torch.from_numpy(__snake_case					).to(device=__snake_case					)
  
           if str(__snake_case					).startswith('''mps'''					):
                # mps does not support float64
                snake_case					=       torch.from_numpy(__snake_case					).to(__snake_case    ,      dtype=torch.floataa					)
           else:
                snake_case					=       torch.from_numpy(__snake_case					).to(device=__snake_case					)
      def 	a_    ( self    ,      __snake_case					):
           snake_case					=       self.config.sigma_min
           snake_case					=       self.config.sigma_max
  
           snake_case					=       self.config.rho
           snake_case					=       sigma_min ** (1 / rho)
           snake_case					=       sigma_max ** (1 / rho)
           snake_case					=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
      def 	a_    ( self    ,      __snake_case					):
           snake_case					=       self.config.sigma_data
  
           snake_case					=       sigma_data**2 / (sigma**2 + sigma_data**2)
           snake_case					=       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
      def 	a_    ( self    ,      __snake_case					):
           snake_case					=       self.config.sigma_min
           snake_case					=       self.config.sigma_data
  
           snake_case					=       sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
           snake_case					=       (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case = None    ,      __snake_case = True    ,      ):
  
           if (
               isinstance(__snake_case    ,      __snake_case					)
               or isinstance(__snake_case    ,      torch.IntTensor					)
               or isinstance(__snake_case    ,      torch.LongTensor					)
           ):
                raise ValueError(
                    (
                        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                        F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                        ''' one of the `scheduler.timesteps` as a timestep.'''
                    )    ,      )
  
           if not self.is_scale_input_called:
                logger.warning(
                    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                    '''See `StableDiffusionPipeline` for a usage example.'''					)
  
           if isinstance(__snake_case    ,      torch.Tensor					):
                snake_case					=       timestep.to(self.timesteps.device					)
  
           snake_case					=       self.config.sigma_min
           snake_case					=       self.config.sigma_max
  
           snake_case					=       self.index_for_timestep(__snake_case					)
  
           # sigma_next corresponds to next_t in original implementation
           snake_case					=       self.sigmas[step_index]
           if step_index + 1 < self.config.num_train_timesteps:
                snake_case					=       self.sigmas[step_index + 1]
           else:
                # Set sigma_next to sigma_min
                snake_case					=       self.sigmas[-1]
  
           # Get scalings for boundary conditions
           snake_case				,						snake_case					=       self.get_scalings_for_boundary_condition(__snake_case					)
  
           # 1. Denoise model output using boundary conditions
           snake_case					=       c_out * model_output + c_skip * sample
           if self.config.clip_denoised:
                snake_case					=       denoised.clamp(-1    ,      1					)
  
           # 2. Sample z ~ N(0, s_noise^2 * I)
           # Noise is not used for onestep sampling.
           if len(self.timesteps					) > 1:
                snake_case					=       randn_tensor(
                    model_output.shape    ,      dtype=model_output.dtype    ,      device=model_output.device    ,      generator=__snake_case					)
           else:
                snake_case					=       torch.zeros_like(__snake_case					)
           snake_case					=       noise * self.config.s_noise
  
           snake_case					=       sigma_next.clamp(min=__snake_case    ,      max=__snake_case					)
  
           # 3. Return noisy sample
           # tau = sigma_hat, eps = sigma_min
           snake_case					=       denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
           if not return_dict:
                return (prev_sample,)
  
           return CMStochasticIterativeSchedulerOutput(prev_sample=__snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      ):
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           snake_case					=       self.sigmas.to(device=original_samples.device    ,      dtype=original_samples.dtype					)
           if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case					):
                # mps does not support float64
                snake_case					=       self.timesteps.to(original_samples.device    ,      dtype=torch.floataa					)
                snake_case					=       timesteps.to(original_samples.device    ,      dtype=torch.floataa					)
           else:
                snake_case					=       self.timesteps.to(original_samples.device					)
                snake_case					=       timesteps.to(original_samples.device					)
  
           snake_case					=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
           snake_case					=       sigmas[step_indices].flatten()
           while len(sigma.shape					) < len(original_samples.shape					):
                snake_case					=       sigma.unsqueeze(-1					)
  
           snake_case					=       original_samples + noise * sigma
           return noisy_samples
  
  
  
  
      def __len__( self					):
           return self.config.num_train_timesteps
  
  
  
  
  
style_context: 
  
  
  import argparse
  import json
  import os
  
  import fairseq
  import torch
  from fairseq.data import Dictionary
  
  # Register SEW's fairseq modules
  from sew_asapp import tasks  # noqa: F401
  
  from transformers import (
      SEWDConfig,
      SEWDForCTC,
      SEWDModel,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaProcessor,
      logging,
  )
  
  
  logging.set_verbosity_info()
  _SCREAMING_SNAKE_CASE    :				List[str]       =					logging.get_logger(__name__)
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					{
      "post_extract_proj": "feature_projection",
      "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
      "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
      "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
      "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
      "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
      "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
      "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
      "output.dense": "encoder.encoder.layer.*.output.dense",
      "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
      "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
      "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
      "encoder.upsample.0": "encoder.upsample.projection",
      "encoder.layer_norm": "encoder.layer_norm",
      "w2v_model.layer_norm": "layer_norm",
      "w2v_encoder.proj": "lm_head",
      "mask_emb": "masked_spec_embed",
  }
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for attribute in key.split('''.'''       ):
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       )
  
       if weight_type is not None:
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       ).shape
       else:
            snake_case					=       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":
            snake_case					=       value
       elif weight_type == "weight_g":
            snake_case					=       value
       elif weight_type == "weight_v":
            snake_case					=       value
       elif weight_type == "bias":
            snake_case					=       value
       else:
            snake_case					=       value
  
       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       fairseq_model.state_dict()
  
       snake_case					=       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
       for name, value in fairseq_dict.items():
            snake_case					=       False
            if "conv_layers" in name:
                 load_conv_layer(
                     UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,hf_model.config.feat_extract_norm == '''group'''							,)
                 snake_case					=       True
            else:
                 for key, mapped_key in MAPPING.items():
                      snake_case					=       '''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
  
                      if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
                           snake_case					=       True
                           if "*" in mapped_key:
                                snake_case					=       name.split(UpperCamelCase_       )[0].split('''.'''       )[-2]
                                if not layer_index.isnumeric():
                                     continue
                                snake_case					=       mapped_key.replace('''*'''							,UpperCamelCase_       )
                           if "weight_g" in name:
                                snake_case					=       '''weight_g'''
                           elif "weight_v" in name:
                                snake_case					=       '''weight_v'''
                           elif "weight" in name:
                                snake_case					=       '''weight'''
                           elif "bias" in name:
                                snake_case					=       '''bias'''
                           else:
                                snake_case					=       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_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       full_name.split('''conv_layers.'''       )[-1]
       snake_case					=       name.split('''.'''       )
       snake_case					=       int(items[0]       )
       snake_case					=       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.'''
                 )
                 snake_case					=       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.'''
                 )
                 snake_case					=       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."
                 )
                 snake_case					=       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.'''
                 )
                 snake_case					=       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_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       SEWDConfig()
       if is_finetuned:
            snake_case					=       model.wav_encoder.wav_model.cfg
       else:
            snake_case					=       model.cfg
  
       snake_case					=       fs_config.conv_bias
       snake_case					=       eval(fs_config.conv_feature_layers       )
       snake_case					=       [x[0] for x in conv_layers]
       snake_case					=       [x[1] for x in conv_layers]
       snake_case					=       [x[2] for x in conv_layers]
       snake_case					=       '''gelu'''
       snake_case					=       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       snake_case					=       0.0
       snake_case					=       fs_config.activation_fn.name
       snake_case					=       fs_config.encoder_embed_dim
       snake_case					=       0.02
       snake_case					=       fs_config.encoder_ffn_embed_dim
       snake_case					=       1e-5
       snake_case					=       fs_config.encoder_layerdrop
       snake_case					=       fs_config.encoder_attention_heads
       snake_case					=       fs_config.conv_pos_groups
       snake_case					=       fs_config.conv_pos
       snake_case					=       len(UpperCamelCase_       )
       snake_case					=       fs_config.encoder_layers
       snake_case					=       fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       snake_case					=       fs_config.max_position_embeddings
       snake_case					=       fs_config.position_buckets
       snake_case					=       fs_config.share_att_key
       snake_case					=       fs_config.relative_attention
       snake_case					=       fs_config.position_biased_input
       snake_case					=       tuple(fs_config.pos_att_type.split('''|'''       )       )
       snake_case					=       fs_config.norm_rel_ebd
  
       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
            snake_case					=       model.cfg
            snake_case					=       fs_config.final_dropout
            snake_case					=       fs_config.layerdrop
       snake_case					=       fs_config.activation_dropout
       snake_case					=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       snake_case					=       fs_config.attention_dropout
       snake_case					=       fs_config.dropout_input
       snake_case					=       fs_config.dropout
       snake_case					=       fs_config.mask_channel_length
       snake_case					=       fs_config.mask_channel_prob
       snake_case					=       fs_config.mask_length
       snake_case					=       fs_config.mask_prob
  
       snake_case					=       '''Wav2Vec2FeatureExtractor'''
       snake_case					=       '''Wav2Vec2CTCTokenizer'''
  
       return config
  @torch.no_grad()
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=True       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if is_finetuned:
            snake_case				,						snake_case				,						snake_case					=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]							,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
       else:
            snake_case				,						snake_case				,						snake_case					=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )
  
       if config_path is not None:
            snake_case					=       SEWDConfig.from_pretrained(UpperCamelCase_       )
       else:
            snake_case					=       convert_config(model[0]							,UpperCamelCase_       )
       snake_case					=       model[0].eval()
  
       snake_case					=       True if config.feat_extract_norm == '''layer''' else False
       snake_case					=       WavaVecaFeatureExtractor(
           feature_size=1							,sampling_rate=1_60_00							,padding_value=0							,do_normalize=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)
  
       if is_finetuned:
            if dict_path:
                 snake_case					=       Dictionary.load(UpperCamelCase_       )
  
                 # important change bos & pad token id since CTC symbol is <pad> and
                 # not <s> as in fairseq
                 snake_case					=       target_dict.pad_index
                 snake_case					=       target_dict.bos_index
                 snake_case					=       target_dict.pad_index
                 snake_case					=       target_dict.bos_index
                 snake_case					=       target_dict.eos_index
                 snake_case					=       len(target_dict.symbols       )
                 snake_case					=       os.path.join(UpperCamelCase_							,'''vocab.json'''       )
                 if not os.path.isdir(UpperCamelCase_       ):
                      logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase_       )       )
                      return
                 os.makedirs(UpperCamelCase_							,exist_ok=UpperCamelCase_       )
                 with open(UpperCamelCase_							,'''w'''							,encoding='''utf-8'''       ) as vocab_handle:
                      json.dump(target_dict.indices							,UpperCamelCase_       )
                 snake_case					=       WavaVecaCTCTokenizer(
                     UpperCamelCase_							,unk_token=target_dict.unk_word							,pad_token=target_dict.pad_word							,bos_token=target_dict.bos_word							,eos_token=target_dict.eos_word							,word_delimiter_token='''|'''							,do_lower_case=UpperCamelCase_							,)
                 snake_case					=       WavaVecaProcessor(feature_extractor=UpperCamelCase_							,tokenizer=UpperCamelCase_       )
                 processor.save_pretrained(UpperCamelCase_       )
  
            snake_case					=       SEWDForCTC(UpperCamelCase_       )
       else:
            snake_case					=       SEWDModel(UpperCamelCase_       )
            feature_extractor.save_pretrained(UpperCamelCase_       )
  
       recursively_load_weights(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       hf_model.save_pretrained(UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				List[Any]       =					argparse.ArgumentParser()
   parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
   parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
   parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
   parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
   parser.add_argument(
       "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
   )
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )
  
  
  
  
  