'''simple docstring'''

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
__a:			Optional[int]	       =       logging.get_logger()



@dataclass
class       UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   field(default_factory=a__ )
       SCREAMING_SNAKE_CASE 		=   field(default_factory=a__ )

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[Any]:
        lowercase__    :   List[Any]						= len(list(m.modules()			)			) == 1 or isinstance(lowerCAmelCase__     ,       nn.Convad			) or isinstance(lowerCAmelCase__     ,       nn.BatchNormad			)
        if has_not_submodules:
         self.traced.append(lowerCAmelCase__			)

       def __call__(						self     ,       __lowerCAmelCase			)  ->  List[Any]:




        for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook			)			)
        self.module(lowerCAmelCase__			)
        [x.remove() for x in self.handles]
        return self


       @property
       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        return list(filter(lambda __lowerCAmelCase			: len(list(x.state_dict().keys()			)			) > 0     ,       self.traced			)			)



@dataclass
class       UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   1
       SCREAMING_SNAKE_CASE 		=   field(default_factory=a__ )
       SCREAMING_SNAKE_CASE 		=   field(default_factory=a__ )
       SCREAMING_SNAKE_CASE 		=   True

       def __call__(						self     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   Optional[Any]						= Tracker(self.dest			)(lowerCAmelCase__			).parametrized
        lowercase__    :   str						= Tracker(self.src			)(lowerCAmelCase__			).parametrized

        lowercase__    :   Optional[int]						= list(filter(lambda __lowerCAmelCase			: type(lowerCAmelCase__			) not in self.src_skip     ,       lowerCAmelCase__			)			)
        lowercase__    :   Optional[Any]						= list(filter(lambda __lowerCAmelCase			: type(lowerCAmelCase__			) not in self.dest_skip     ,       lowerCAmelCase__			)			)

        if len(lowerCAmelCase__			) != len(lowerCAmelCase__			) and self.raise_if_mismatch:
         raise Exception(
             F"""Numbers of operations are different. Source module has {len(lowerCAmelCase__			)} operations while"""
             F""" destination module has {len(lowerCAmelCase__			)}."""			)

        for dest_m, src_m in zip(lowerCAmelCase__     ,       lowerCAmelCase__			):
         dest_m.load_state_dict(src_m.state_dict()			)
         if self.verbose == 1:
          print(F"""Transfered from={src_m} to={dest_m}"""			)



class       UpperCAmelCase  (						nn.Module ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase			)  ->  Dict:
        super().__init__()

        lowercase__    :   List[Tuple[str, nn.Module]]						= []
        # - get the stem
        feature_blocks.append(('''conv1''', model.stem)			)
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
         assert k.startswith('''block'''			), F"""Unexpected layer name {k}"""
         lowercase__    :   Tuple						= len(lowerCAmelCase__			) + 1
         feature_blocks.append((F"""res{block_index}""", v)			)

        lowercase__    :   Optional[Any]						= nn.ModuleDict(lowerCAmelCase__			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[str]:
        return get_trunk_forward_outputs(
            lowerCAmelCase__     ,       out_feat_keys=lowerCAmelCase__     ,       feature_blocks=self._feature_blocks     ,       )



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   int						= x.split('''-'''			)
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]			)


       def __getitem__(						self     ,       __lowerCAmelCase			)  ->  Callable[[], Tuple[nn.Module, Dict]]:




        if x not in self:
         lowercase__    :   Tuple						= self.convert_name_to_timm(lowerCAmelCase__			)
         lowercase__    :   List[Any]						= partial(lambda: (timm.create_model(lowerCAmelCase__     ,       pretrained=lowerCAmelCase__			).eval(), None)			)

        else:
         lowercase__    :   List[Any]						= super().__getitem__(lowerCAmelCase__			)

        return val



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __getitem__(						self     ,       __lowerCAmelCase			)  ->  Callable[[], nn.Module]:




        if "seer" in x and "in1k" not in x:
         lowercase__    :   Optional[Any]						= RegNetModel
        else:
         lowercase__    :   Dict						= RegNetForImageClassification
        return val


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 for from_key, to_key in keys:
  lowercase__    :   Union[str, Any]						= from_state_dict[from_key].clone()
  print(F"""Copied key={from_key} to={to_key}"""					)
 return to_state_dict
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = True		,					):
 print(F"""Converting {name}..."""					)
 with torch.no_grad():
  lowercase__    :   Any						= from_model_func()
  lowercase__    :   int						= our_model_func(a_					).eval()
  lowercase__    :   List[Any]						= ModuleTransfer(src=a_		,					dest=a_		,					raise_if_mismatch=a_					)
  lowercase__    :   Any						= torch.randn((1, 3, 224, 224)					)
  module_transfer(a_					)

 if from_state_dict is not None:
  lowercase__    :   Optional[Any]						= []
  # for seer - in1k finetuned we have to manually copy the head
  if "seer" in name and "in1k" in name:
   lowercase__    :   Optional[int]						= [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
  lowercase__    :   List[Any]						= manually_copy_vissl_head(a_		,					our_model.state_dict()		,					a_					)
  our_model.load_state_dict(a_					)

 lowercase__    :   Dict						= our_model(a_		,					output_hidden_states=a_					)
 lowercase__    :   Optional[int]						= (
     our_outputs.logits if isinstance(a_		,					a_					) else our_outputs.last_hidden_state
 )

 lowercase__    :   str						= from_model(a_					)
 lowercase__    :   List[Any]						= from_output[-1] if type(a_					) is list else from_output

 # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
 if "seer" in name and "in1k" in name:
  lowercase__    :   List[str]						= our_outputs.hidden_states[-1]

 assert torch.allclose(a_		,					a_					), "The model logits don't match the original one."

 if push_to_hub:
  our_model.push_to_hub(
      repo_path_or_name=save_directory / name		,					commit_message='''Add model'''		,					use_temp_dir=a_		,					)

  lowercase__    :   Optional[Any]						= 224 if "seer" not in name else 384
  # we can use the convnext one
  lowercase__    :   Optional[int]						= AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''		,					size=a_					)
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / name		,					commit_message='''Add image processor'''		,					use_temp_dir=a_		,					)

  print(F"""Pushed {name}"""					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase = None		,					UpperCAmelCase = True					):
 lowercase__    :   Union[str, Any]						= "imagenet-1k-id2label.json"
 lowercase__    :   Any						= 1000
 lowercase__    :   int						= (1, num_labels)

 lowercase__    :   Any						= "huggingface/label-files"
 lowercase__    :   str						= num_labels
 lowercase__    :   List[Any]						= json.load(open(cached_download(hf_hub_url(a_		,					a_		,					repo_type='''dataset'''					)					)		,					'''r'''					)					)
 lowercase__    :   Union[str, Any]						= {int(a_					): v for k, v in idalabel.items()}

 lowercase__    :   Tuple						= idalabel
 lowercase__    :   List[Any]						= {v: k for k, v in idalabel.items()}

 lowercase__    :   str						= partial(a_		,					num_labels=a_		,					idalabel=a_		,					labelaid=a_					)

 lowercase__    :   Union[str, Any]						= {
     "regnet-x-002": ImageNetPreTrainedConfig(
         depths=[1, 1, 4, 7]		,					hidden_sizes=[24, 56, 152, 368]		,					groups_width=8		,					layer_type='''x'''					),
     "regnet-x-004": ImageNetPreTrainedConfig(
         depths=[1, 2, 7, 12]		,					hidden_sizes=[32, 64, 160, 384]		,					groups_width=16		,					layer_type='''x'''					),
     "regnet-x-006": ImageNetPreTrainedConfig(
         depths=[1, 3, 5, 7]		,					hidden_sizes=[48, 96, 240, 528]		,					groups_width=24		,					layer_type='''x'''					),
     "regnet-x-008": ImageNetPreTrainedConfig(
         depths=[1, 3, 7, 5]		,					hidden_sizes=[64, 128, 288, 672]		,					groups_width=16		,					layer_type='''x'''					),
     "regnet-x-016": ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 2]		,					hidden_sizes=[72, 168, 408, 912]		,					groups_width=24		,					layer_type='''x'''					),
     "regnet-x-032": ImageNetPreTrainedConfig(
         depths=[2, 6, 15, 2]		,					hidden_sizes=[96, 192, 432, 1008]		,					groups_width=48		,					layer_type='''x'''					),
     "regnet-x-040": ImageNetPreTrainedConfig(
         depths=[2, 5, 14, 2]		,					hidden_sizes=[80, 240, 560, 1360]		,					groups_width=40		,					layer_type='''x'''					),
     "regnet-x-064": ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 1]		,					hidden_sizes=[168, 392, 784, 1624]		,					groups_width=56		,					layer_type='''x'''					),
     "regnet-x-080": ImageNetPreTrainedConfig(
         depths=[2, 5, 15, 1]		,					hidden_sizes=[80, 240, 720, 1920]		,					groups_width=120		,					layer_type='''x'''					),
     "regnet-x-120": ImageNetPreTrainedConfig(
         depths=[2, 5, 11, 1]		,					hidden_sizes=[224, 448, 896, 2240]		,					groups_width=112		,					layer_type='''x'''					),
     "regnet-x-160": ImageNetPreTrainedConfig(
         depths=[2, 6, 13, 1]		,					hidden_sizes=[256, 512, 896, 2048]		,					groups_width=128		,					layer_type='''x'''					),
     "regnet-x-320": ImageNetPreTrainedConfig(
         depths=[2, 7, 13, 1]		,					hidden_sizes=[336, 672, 1344, 2520]		,					groups_width=168		,					layer_type='''x'''					),
     # y variant
     "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]		,					hidden_sizes=[24, 56, 152, 368]		,					groups_width=8					),
     "regnet-y-004": ImageNetPreTrainedConfig(
         depths=[1, 3, 6, 6]		,					hidden_sizes=[48, 104, 208, 440]		,					groups_width=8					),
     "regnet-y-006": ImageNetPreTrainedConfig(
         depths=[1, 3, 7, 4]		,					hidden_sizes=[48, 112, 256, 608]		,					groups_width=16					),
     "regnet-y-008": ImageNetPreTrainedConfig(
         depths=[1, 3, 8, 2]		,					hidden_sizes=[64, 128, 320, 768]		,					groups_width=16					),
     "regnet-y-016": ImageNetPreTrainedConfig(
         depths=[2, 6, 17, 2]		,					hidden_sizes=[48, 120, 336, 888]		,					groups_width=24					),
     "regnet-y-032": ImageNetPreTrainedConfig(
         depths=[2, 5, 13, 1]		,					hidden_sizes=[72, 216, 576, 1512]		,					groups_width=24					),
     "regnet-y-040": ImageNetPreTrainedConfig(
         depths=[2, 6, 12, 2]		,					hidden_sizes=[128, 192, 512, 1088]		,					groups_width=64					),
     "regnet-y-064": ImageNetPreTrainedConfig(
         depths=[2, 7, 14, 2]		,					hidden_sizes=[144, 288, 576, 1296]		,					groups_width=72					),
     "regnet-y-080": ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 1]		,					hidden_sizes=[168, 448, 896, 2016]		,					groups_width=56					),
     "regnet-y-120": ImageNetPreTrainedConfig(
         depths=[2, 5, 11, 1]		,					hidden_sizes=[224, 448, 896, 2240]		,					groups_width=112					),
     "regnet-y-160": ImageNetPreTrainedConfig(
         depths=[2, 4, 11, 1]		,					hidden_sizes=[224, 448, 1232, 3024]		,					groups_width=112					),
     "regnet-y-320": ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]		,					hidden_sizes=[232, 696, 1392, 3712]		,					groups_width=232					),
     # models created by SEER -> https://arxiv.org/abs/2202.08360
     "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]		,					hidden_sizes=[232, 696, 1392, 3712]		,					groups_width=232					),
     "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]		,					hidden_sizes=[328, 984, 1968, 4920]		,					groups_width=328					),
     "regnet-y-1280-seer": RegNetConfig(
         depths=[2, 7, 17, 1]		,					hidden_sizes=[528, 1056, 2904, 7392]		,					groups_width=264					),
     "regnet-y-2560-seer": RegNetConfig(
         depths=[3, 7, 16, 1]		,					hidden_sizes=[640, 1696, 2544, 5088]		,					groups_width=640					),
     "regnet-y-10b-seer": ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]		,					hidden_sizes=[2020, 4040, 1_1110, 2_8280]		,					groups_width=1010					),
     # finetuned on imagenet
     "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]		,					hidden_sizes=[232, 696, 1392, 3712]		,					groups_width=232					),
     "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]		,					hidden_sizes=[328, 984, 1968, 4920]		,					groups_width=328					),
     "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]		,					hidden_sizes=[528, 1056, 2904, 7392]		,					groups_width=264					),
     "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
         depths=[3, 7, 16, 1]		,					hidden_sizes=[640, 1696, 2544, 5088]		,					groups_width=640					),
     "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]		,					hidden_sizes=[2020, 4040, 1_1110, 2_8280]		,					groups_width=1010					),
 }

 lowercase__    :   Optional[int]						= NameToOurModelFuncMap()
 lowercase__    :   List[Any]						= NameToFromModelFuncMap()
 # add seer weights logic

 def load_using_classy_vision(UpperCAmelCase		,					UpperCAmelCase					) -> Tuple[nn.Module, Dict]:
  lowercase__    :   Dict						= torch.hub.load_state_dict_from_url(a_		,					model_dir=str(a_					)		,					map_location='''cpu'''					)
  lowercase__    :   str						= model_func()
  # check if we have a head, if yes add it
  lowercase__    :   Tuple						= files["classy_state_dict"]["base_model"]["model"]
  lowercase__    :   List[Any]						= model_state_dict["trunk"]
  model.load_state_dict(a_					)
  return model.eval(), model_state_dict["heads"]

 # pretrained
 lowercase__    :   Optional[Any]						= partial(
     a_		,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''		,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)		,					)

 lowercase__    :   str						= partial(
     a_		,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''		,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)		,					)

 lowercase__    :   int						= partial(
     a_		,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''		,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)		,					)

 lowercase__    :   Optional[Any]						= partial(
     a_		,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''		,					lambda: FakeRegNetVisslWrapper(
         RegNet(RegNetParams(depth=27		,					group_width=1010		,					w_a=1744		,					w_a=620.83		,					w_m=2.5_2					)					)					)		,					)

 # IN1K finetuned
 lowercase__    :   Optional[Any]						= partial(
     a_		,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''		,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)		,					)

 lowercase__    :   Tuple						= partial(
     a_		,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''		,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)		,					)

 lowercase__    :   Dict						= partial(
     a_		,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''		,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)		,					)

 lowercase__    :   Dict						= partial(
     a_		,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''		,					lambda: FakeRegNetVisslWrapper(
         RegNet(RegNetParams(depth=27		,					group_width=1010		,					w_a=1744		,					w_a=620.83		,					w_m=2.5_2					)					)					)		,					)

 if model_name:
  convert_weight_and_push(
      a_		,					names_to_from_model_map[model_name]		,					names_to_ours_model_map[model_name]		,					names_to_config[model_name]		,					a_		,					a_		,					)
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(
       a_		,					names_to_from_model_map[model_name]		,					names_to_ours_model_map[model_name]		,					a_		,					a_		,					a_		,					)
 return config, expected_shape


if __name__ == "__main__":
  __a:			List[Any]	       =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default=None,
      type=str,
      help=(
          """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
          """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=Path,
      required=True,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument(
      """--push_to_hub""",
      default=True,
      type=bool,
      required=False,
      help="""If True, push model and image processor to the hub.""",
  )

  __a:			Optional[int]	       =       parser.parse_args()

  __a:			Optional[int]	       =       args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


'''simple docstring'''

import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow



@is_pipeline_test
@require_torch
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       @require_torch
       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   str						= pipeline(
            task='''zero-shot-audio-classification'''     ,       model='''hf-internal-testing/tiny-clap-htsat-unfused'''			)
        lowercase__    :   Optional[Any]						= load_dataset('''ashraq/esc50'''			)
        lowercase__    :   Tuple						= dataset['''train''']['''audio'''][-1]['''array''']
        lowercase__    :   Optional[Any]						= audio_classifier(__lowerCAmelCase     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']			)
        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [{'''score''': 0.5_0_1, '''label''': '''Sound of a dog'''}, {'''score''': 0.4_9_9, '''label''': '''Sound of vaccum cleaner'''}]     ,       )

       @unittest.skip('''No models are available in TF'''			)
       def _lowerCAmelCase(						self			)  ->  str:
        pass

       @slow
       @require_torch
       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   List[str]						= pipeline(
            task='''zero-shot-audio-classification'''     ,       model='''laion/clap-htsat-unfused'''     ,       )
        # This is an audio of a dog
        lowercase__    :   int						= load_dataset('''ashraq/esc50'''			)
        lowercase__    :   str						= dataset['''train''']['''audio'''][-1]['''array''']
        lowercase__    :   Any						= audio_classifier(__lowerCAmelCase     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']			)

        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [
                {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
            ]     ,       )

        lowercase__    :   Dict						= audio_classifier([audio] * 5     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']			)
        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [
                [
                    {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                    {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
                ],
            ]
            * 5     ,       )
        lowercase__    :   Any						= audio_classifier(
            [audio] * 5     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']     ,       batch_size=5			)
        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [
                [
                    {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                    {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
                ],
            ]
            * 5     ,       )


       @unittest.skip('''No models are available in TF'''			)
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        pass

