code: 
  
  
  
  
  
  
  import argparse
  import collections
  
  import jax
  import jax.numpy as jnp
  import torch
  import torch.nn as nn
  from clip.model import CLIP
  from flax.training import checkpoints
  from huggingface_hub import Repository
  
  from transformers import (
      CLIPTokenizer,
      OwlViTConfig,
      OwlViTForObjectDetection,
      OwlViTImageProcessor,
      OwlViTModel,
      OwlViTProcessor,
  )
  
  
  lowerCamelCase			:							int       ={
      '''vit_b32''': {
          '''embed_dim''': 512,
          '''image_resolution''': 768,
          '''context_length''': 16,
          '''vocab_size''': 49408,
          '''vision_layers''': 12,
          '''vision_width''': 768,
          '''vision_patch_size''': 32,
          '''transformer_width''': 512,
          '''transformer_heads''': 8,
          '''transformer_layers''': 12,
      },
      '''vit_b16''': {
          '''embed_dim''': 512,
          '''image_resolution''': 768,
          '''context_length''': 16,
          '''vocab_size''': 49408,
          '''vision_layers''': 12,
          '''vision_width''': 768,
          '''vision_patch_size''': 16,
          '''transformer_width''': 512,
          '''transformer_heads''': 8,
          '''transformer_layers''': 12,
      },
      '''vit_l14''': {
          '''embed_dim''': 768,
          '''image_resolution''': 840,
          '''context_length''': 16,
          '''vocab_size''': 49408,
          '''vision_layers''': 24,
          '''vision_width''': 1024,
          '''vision_patch_size''': 14,
          '''transformer_width''': 768,
          '''transformer_heads''': 12,
          '''transformer_layers''': 12,
      },
  }
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase=""      ,					__lowerCAmelCase="/"    )				->			List[str]:
       UpperCamelCase__    :     int        =						[]
  
       for k, v in params.items():
            UpperCamelCase__    :     Optional[int]        =						parent_key + sep + k if parent_key else k
  
            if isinstance(UpperCamelCase__      ,					collections.MutableMapping    ):
                 items.extend(flatten_nested_dict(UpperCamelCase__      ,					UpperCamelCase__      ,					sep=UpperCamelCase__    ).items()    )
            else:
                 items.append((new_key, v)    )
       return dict(UpperCamelCase__    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			Optional[int]:
       return jax.tree_util.tree_map(lambda __lowerCAmelCase    : x.astype(jnp.floataa    ) if x.dtype == jnp.bfloataa else x      ,					UpperCamelCase__    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			Optional[Any]:
       UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__    :     str        =						pt_attn_layer.in_proj_weight.chunk(3      ,					dim=0    )
       UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__    :     int        =						pt_attn_layer.in_proj_bias.chunk(3      ,					dim=0    )
  
       UpperCamelCase__    :     Optional[int]        =						pt_attn_layer.out_proj.weight
       UpperCamelCase__    :     str        =						pt_attn_layer.out_proj.bias
  
       UpperCamelCase__    :     Tuple        =						q_proj
       UpperCamelCase__    :     int        =						q_proj_bias
  
       UpperCamelCase__    :     Optional[Any]        =						k_proj
       UpperCamelCase__    :     List[str]        =						k_proj_bias
  
       UpperCamelCase__    :     Optional[int]        =						v_proj
       UpperCamelCase__    :     List[str]        =						v_proj_bias
  
       UpperCamelCase__    :     str        =						out_proj_weights
       UpperCamelCase__    :     Union[str, Any]        =						out_proj_bias
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[Any]:
       copy_linear(hf_mlp.fca      ,					pt_mlp.c_fc    )
       copy_linear(hf_mlp.fca      ,					pt_mlp.c_proj    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[Any]:
       UpperCamelCase__    :     Any        =						pt_linear.weight
       UpperCamelCase__    :     List[Any]        =						pt_linear.bias
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[str]:
       copy_linear(hf_layer.layer_norma      ,					pt_layer.ln_a    )
       copy_linear(hf_layer.layer_norma      ,					pt_layer.ln_a    )
  
       # copy MLP
       copy_mlp(hf_layer.mlp      ,					pt_layer.mlp    )
  
       # copy attn
       copy_attn_layer(hf_layer.self_attn      ,					pt_layer.attn    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[str]:
  
  
  
  
  
  
       for hf_layer, pt_layer in zip(UpperCamelCase__      ,					UpperCamelCase__    ):
            copy_layer(UpperCamelCase__      ,					UpperCamelCase__    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			str:
       UpperCamelCase__    :     Any        =						pt_model.token_embedding.weight
       UpperCamelCase__    :     Tuple        =						pt_model.positional_embedding
  
       # copy layer norm
       copy_linear(hf_encoder.final_layer_norm      ,					pt_model.ln_final    )
  
       # copy hidden layers
       copy_layers(hf_encoder.encoder.layers      ,					pt_model.transformer.resblocks    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			Dict:
       UpperCamelCase__    :     Optional[int]        =						pt_model.text_projection.data.T
  
       # copy text encoder
       copy_encoder(hf_model.text_model      ,					UpperCamelCase__    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[str]:
       UpperCamelCase__    :     Union[str, Any]        =						pt_model.visual.proj.data.T
  
       # copy layer norms
       copy_linear(hf_model.vision_model.pre_layernorm      ,					pt_model.visual.ln_pre    )
       copy_linear(hf_model.vision_model.post_layernorm      ,					pt_model.visual.ln_post    )
  
       # copy embeds
       UpperCamelCase__    :     Any        =						pt_model.visual.conva.weight.data
       UpperCamelCase__    :     Optional[int]        =						pt_model.visual.class_embedding
       UpperCamelCase__    :     List[Any]        =						pt_model.visual.positional_embedding.data
  
       # copy encoder
       copy_layers(hf_model.vision_model.encoder.layers      ,					pt_model.visual.transformer.resblocks    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			Tuple:
       UpperCamelCase__    :     List[str]        =						flatten_nested_dict(flax_params["backbone"]["merged_class_token"]    )
  
       UpperCamelCase__    :     int        =						torch.from_numpy(flax_class_token_params["scale"]    )
       UpperCamelCase__    :     List[str]        =						torch.from_numpy(flax_class_token_params["bias"]    )
       UpperCamelCase__    :     Union[str, Any]        =						nn.Parameter(UpperCamelCase__    )
       UpperCamelCase__    :     Tuple        =						nn.Parameter(UpperCamelCase__    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			Dict:
       UpperCamelCase__    :     Tuple        =						hf_model.state_dict()
       UpperCamelCase__    :     Optional[Any]        =						{}
  
       # Rename class prediction head flax params to pytorch HF
       UpperCamelCase__    :     Tuple        =						flatten_nested_dict(flax_params["class_head"]    )
  
       for flax_key, v in flax_class_params.items():
            UpperCamelCase__    :     Optional[Any]        =						flax_key.replace("/"      ,					"."    )
            UpperCamelCase__    :     Any        =						torch_key.replace(".kernel"      ,					".weight"    )
            UpperCamelCase__    :     Dict        =						torch_key.replace("Dense_0"      ,					"dense0"    )
            UpperCamelCase__    :     Optional[int]        =						"class_head." + torch_key
  
            if "weight" in torch_key and v.ndim == 2:
                 UpperCamelCase__    :     str        =						v.T
  
            UpperCamelCase__    :     List[Any]        =						nn.Parameter(torch.from_numpy(UpperCamelCase__    )    )
  
       # Rename box prediction box flax params to pytorch HF
       UpperCamelCase__    :     Dict        =						flatten_nested_dict(flax_params["obj_box_head"]    )
  
       for flax_key, v in flax_box_params.items():
            UpperCamelCase__    :     int        =						flax_key.replace("/"      ,					"."    )
            UpperCamelCase__    :     Any        =						torch_key.replace(".kernel"      ,					".weight"    )
            UpperCamelCase__    :     Tuple        =						torch_key.replace("_"      ,					""    ).lower()
            UpperCamelCase__    :     Optional[int]        =						"box_head." + torch_key
  
            if "weight" in torch_key and v.ndim == 2:
                 UpperCamelCase__    :     str        =						v.T
  
            UpperCamelCase__    :     str        =						nn.Parameter(torch.from_numpy(UpperCamelCase__    )    )
  
       # Copy flax params to PyTorch params
       for name, param in new_params.items():
            if name in pt_params.keys():
                 pt_params[name].copy_(UpperCamelCase__    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			Optional[int]:
  
  
  
  
  
  
       for k, v in flax_attn_params.items():
            if k.startswith("transformer"    ):
                 UpperCamelCase__    :     List[Any]        =						k.replace("transformer.resblocks"      ,					"text_model.encoder.layers"    )
            else:
                 UpperCamelCase__    :     Any        =						k.replace("visual.transformer.resblocks"      ,					"vision_model.encoder.layers"    )
  
            UpperCamelCase__    :     Optional[Any]        =						torch_key.replace("attn"      ,					"self_attn"    )
            UpperCamelCase__    :     Any        =						torch_key.replace("key"      ,					"k_proj"    )
            UpperCamelCase__    :     str        =						torch_key.replace("value"      ,					"v_proj"    )
            UpperCamelCase__    :     List[Any]        =						torch_key.replace("query"      ,					"q_proj"    )
            UpperCamelCase__    :     str        =						torch_key.replace("out"      ,					"out_proj"    )
  
            if "bias" in torch_key and v.ndim == 2:
                 UpperCamelCase__    :     Tuple        =						v.shape[0] * v.shape[1]
                 UpperCamelCase__    :     Dict        =						v.reshape(UpperCamelCase__    )
  
            if "weight" in torch_key and "out" in torch_key:
                 UpperCamelCase__    :     Dict        =						(v.shape[0] * v.shape[1], v.shape[2])
                 UpperCamelCase__    :     str        =						v.reshape(UpperCamelCase__    ).T
  
            if "weight" in torch_key and "out" not in torch_key:
                 UpperCamelCase__    :     int        =						(v.shape[0], v.shape[1] * v.shape[2])
                 UpperCamelCase__    :     Union[str, Any]        =						v.reshape(UpperCamelCase__    ).T
  
            # Copy flax CLIP attn params to HF PyTorch params
            UpperCamelCase__    :     Dict        =						torch.from_numpy(UpperCamelCase__    )
            hf_backbone.state_dict()[torch_key].copy_(UpperCamelCase__    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			List[str]:
       UpperCamelCase__    :     Optional[Any]        =						{}
       UpperCamelCase__    :     int        =						[]
  
       for k, v in params.items():
            if "attn." in k:
                 UpperCamelCase__    :     Union[str, Any]        =						k[: k.rindex("attn."    ) + 5]
                 if base in processed_attn_layers:
                      continue
  
                 processed_attn_layers.append(UpperCamelCase__    )
                 UpperCamelCase__    :     Optional[int]        =						params[base + "out.weight"].shape[-1]
                 UpperCamelCase__    :     Dict        =						params[base + "out.weight"].reshape(UpperCamelCase__      ,					UpperCamelCase__    ).T
                 UpperCamelCase__    :     Any        =						params[base + "out.bias"]
            else:
                 UpperCamelCase__    :     str        =						v
       return new_params
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			Union[str, Any]:
       UpperCamelCase__    :     Dict        =						CLIP(**UpperCamelCase__    )
       torch_model.eval()
       UpperCamelCase__    :     Optional[Any]        =						torch_model.state_dict()
  
       UpperCamelCase__    :     List[Any]        =						flatten_nested_dict(flax_params["backbone"]["clip"]    )
       UpperCamelCase__    :     Any        =						{}
  
       for flax_key, v in flax_clip_params.items():
            UpperCamelCase__    :     int        =						flax_key.replace("/"      ,					"."    )
            UpperCamelCase__    :     Optional[int]        =						torch_key.replace("text.token_embedding.embedding"      ,					"token_embedding.kernel"    )
  
            if (
                torch_key.startswith("text.transformer"    )
                or torch_key.startswith("text.text_projection"    )
                or torch_key.startswith("text.ln_final"    )
                or torch_key.startswith("text.positional_embedding"    )
            ):
                 UpperCamelCase__    :     Any        =						torch_key[5:]
  
            UpperCamelCase__    :     List[Any]        =						torch_key.replace("text_projection.kernel"      ,					"text_projection"    )
            UpperCamelCase__    :     int        =						torch_key.replace("visual.proj.kernel"      ,					"visual.proj"    )
            UpperCamelCase__    :     Optional[int]        =						torch_key.replace(".scale"      ,					".weight"    )
            UpperCamelCase__    :     Dict        =						torch_key.replace(".kernel"      ,					".weight"    )
  
            if "conv" in torch_key or "downsample.0.weight" in torch_key:
                 UpperCamelCase__    :     List[str]        =						v.transpose(3      ,					2      ,					0      ,					1    )
  
            elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
                 # Fully connected layers are transposed, embeddings are not
                 UpperCamelCase__    :     Tuple        =						v.T
  
            UpperCamelCase__    :     Any        =						v
  
       UpperCamelCase__    :     Union[str, Any]        =						_convert_attn_layers(UpperCamelCase__    )
       new_torch_params.update(UpperCamelCase__    )
       UpperCamelCase__    :     Dict        =						{}
  
       # Copy flax CLIP backbone params to PyTorch params
       for name, param in new_torch_params.items():
            if name in torch_clip_params.keys():
                 UpperCamelCase__    :     Tuple        =						torch.from_numpy(new_torch_params[name]    )
                 torch_clip_params[name].copy_(UpperCamelCase__    )
            else:
                 UpperCamelCase__    :     str        =						param
  
       return torch_clip_params, torch_model, attn_params
  
  
  
  
  
  
  
  @torch.no_grad()
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase=None    )				->			Dict:
       UpperCamelCase__    :     Tuple        =						Repository(UpperCamelCase__      ,					clone_from=f'google/{pytorch_dump_folder_path}'    )
       repo.git_pull()
  
       if config_path is not None:
            UpperCamelCase__    :     Optional[int]        =						OwlViTConfig.from_pretrained(UpperCamelCase__    )
       else:
            UpperCamelCase__    :     List[str]        =						OwlViTConfig()
  
       UpperCamelCase__    :     List[Any]        =						OwlViTModel(UpperCamelCase__    ).eval()
       UpperCamelCase__    :     Union[str, Any]        =						OwlViTForObjectDetection(UpperCamelCase__    ).eval()
  
       copy_text_model_and_projection(UpperCamelCase__      ,					UpperCamelCase__    )
       copy_vision_model_and_projection(UpperCamelCase__      ,					UpperCamelCase__    )
       UpperCamelCase__    :     Dict        =						pt_backbone.logit_scale
       copy_flax_attn_params(UpperCamelCase__      ,					UpperCamelCase__    )
  
       UpperCamelCase__    :     Tuple        =						hf_backbone
       copy_class_merge_token(UpperCamelCase__      ,					UpperCamelCase__    )
       copy_class_box_heads(UpperCamelCase__      ,					UpperCamelCase__    )
  
       # Save HF model
       hf_model.save_pretrained(repo.local_dir    )
  
       # Initialize image processor
       UpperCamelCase__    :     Optional[Any]        =						OwlViTImageProcessor(
           size=config.vision_config.image_size      ,					crop_size=config.vision_config.image_size    )
       # Initialize tokenizer
       UpperCamelCase__    :     Union[str, Any]        =						CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"      ,					pad_token="!"      ,					model_max_length=16    )
  
       # Initialize processor
       UpperCamelCase__    :     Union[str, Any]        =						OwlViTProcessor(image_processor=UpperCamelCase__      ,					tokenizer=UpperCamelCase__    )
       image_processor.save_pretrained(repo.local_dir    )
       processor.save_pretrained(repo.local_dir    )
  
       repo.git_add()
       repo.git_commit("Upload model and processor"    )
       repo.git_push()
  
  
  if __name__ == "__main__":
   lowerCamelCase			:							Dict       =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--owlvit_version''',
       default=None,
       type=str,
       required=True,
       help='''OWL-ViT model name [clip_b16, clip_b32, clip_l14].''',
   )
   parser.add_argument(
       '''--owlvit_checkpoint''', default=None, type=str, required=True, help='''Path to flax model checkpoint.'''
   )
   parser.add_argument('''--hf_config''', default=None, type=str, required=True, help='''Path to HF model config.''')
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default='''hf_model''', type=str, help='''Path to the output PyTorch model.'''
   )
   lowerCamelCase			:							List[str]       =parser.parse_args()
  
   # Initialize PyToch clip model
   lowerCamelCase			:							Any       =args.owlvit_version
   if model_name == "clip_b16":
    lowerCamelCase			:							Any       =CONFIGS['''vit_b16''']
   elif model_name == "clip_b32":
    lowerCamelCase			:							str       =CONFIGS['''vit_b32''']
   elif model_name == "clip_l14":
    lowerCamelCase			:							List[str]       =CONFIGS['''vit_l14''']
  
   # Load from checkpoint and convert params to float-32
   lowerCamelCase			:							Optional[int]       =checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
   lowerCamelCase			:							Tuple       =jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
   del variables
  
   # Convert CLIP backbone
   lowerCamelCase       ,    lowerCamelCase       ,    lowerCamelCase			:							Union[str, Any]       =convert_clip_backbone(flax_params, torch_config)
  
   convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import numpy as np  # noqa: E402
  
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...schedulers.scheduling_utils import SchedulerMixin
  
  
  try:
   import librosa  # noqa: E402
  
   lowerCamelCase			:							List[str]       =True
   lowerCamelCase			:							Dict       =''''''
  except Exception as e:
   lowerCamelCase			:							str       =False
   lowerCamelCase			:							List[Any]       =(
       F"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
   )
  
  
  from PIL import Image  # noqa: E402
  
  
  
  
  
  class     __a						(					A__ ,     A__ ):
   _lowerCAmelCase : Optional[Any] 				=				'''mel_config.json'''
  
  
  
  
  
  
  
   @register_to_config
   def __init__(	self      :     Any							,     SCREAMING_SNAKE_CASE      :     int = 2_56							,     SCREAMING_SNAKE_CASE      :     int = 2_56							,     SCREAMING_SNAKE_CASE      :     int = 2_20_50							,     SCREAMING_SNAKE_CASE      :     int = 20_48							,     SCREAMING_SNAKE_CASE      :     int = 5_12							,     SCREAMING_SNAKE_CASE      :     int = 80							,     SCREAMING_SNAKE_CASE      :     int = 32							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						hop_length
        UpperCamelCase__    :     Optional[Any]        =						sample_rate
        UpperCamelCase__    :     Dict        =						n_fft
        UpperCamelCase__    :     str        =						top_db
        UpperCamelCase__    :     Optional[Any]        =						n_iter
        self.set_resolution(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     str        =						None
  
        if not _librosa_can_be_imported:
             raise ValueError(_import_error		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     int							,     SCREAMING_SNAKE_CASE      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Dict        =						x_res
        UpperCamelCase__    :     Dict        =						y_res
        UpperCamelCase__    :     Dict        =						self.y_res
        UpperCamelCase__    :     Any        =						self.x_res * self.hop_length - 1
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     str							,     SCREAMING_SNAKE_CASE      :     str = None							,     SCREAMING_SNAKE_CASE      :     np.ndarray = None		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if audio_file is not None:
             UpperCamelCase__       ,  UpperCamelCase__    :     int        =						librosa.load(SCREAMING_SNAKE_CASE							,     mono=SCREAMING_SNAKE_CASE							,     sr=self.sr		)
        else:
             UpperCamelCase__    :     List[Any]        =						raw_audio
  
        # Pad with silence if necessary.
        if len(self.audio		) < self.x_res * self.hop_length:
             UpperCamelCase__    :     List[Any]        =						np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio		),)		)]		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return len(self.audio		) // self.slice_size
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     int = 0		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return self.sr
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[int]        =						librosa.feature.melspectrogram(
            y=self.get_audio_slice(SCREAMING_SNAKE_CASE		)							,     sr=self.sr							,     n_fft=self.n_fft							,     hop_length=self.hop_length							,     n_mels=self.n_mels		)
        UpperCamelCase__    :     Tuple        =						librosa.power_to_db(SCREAMING_SNAKE_CASE							,     ref=np.max							,     top_db=self.top_db		)
        UpperCamelCase__    :     Optional[Any]        =						(((log_S + self.top_db) * 2_55 / self.top_db).clip(0							,     2_55		) + 0.5).astype(np.uinta		)
        UpperCamelCase__    :     Optional[Any]        =						Image.fromarray(SCREAMING_SNAKE_CASE		)
        return image
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     Image.Image		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						np.frombuffer(image.tobytes()							,     dtype="uint8"		).reshape((image.height, image.width)		)
        UpperCamelCase__    :     str        =						bytedata.astype("float"		) * self.top_db / 2_55 - self.top_db
        UpperCamelCase__    :     Any        =						librosa.db_to_power(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Tuple        =						librosa.feature.inverse.mel_to_audio(
            SCREAMING_SNAKE_CASE							,     sr=self.sr							,     n_fft=self.n_fft							,     hop_length=self.hop_length							,     n_iter=self.n_iter		)
        return audio