




'''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()
_SCREAMING_SNAKE_CASE			=						logging.get_logger()
@dataclass
class   __UpperCAmelCase				:



					'''simple docstring'''





					_UpperCamelCase							=		42
					_UpperCamelCase							=		field(default_factory=lowerCAmelCase				)
					_UpperCamelCase							=		field(default_factory=lowerCAmelCase				)



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Dict      ,       _lowercase     :						Tensor      ,       _lowercase     :						Tensor)    ->							Tuple:
								A_									= len(list(m.modules())) == 1 or isinstance(_lowercase      ,       nn.Convad) or isinstance(_lowercase      ,       nn.BatchNormad)
								if has_not_submodules:
											self.traced.append(_lowercase)



					def __call__(	self     :						Any      ,       _lowercase     :						Tensor)    ->							str:
								for m in self.module.modules():
											self.handles.append(m.register_forward_hook(self._forward_hook))
								self.module(_lowercase)
								[x.remove() for x in self.handles]
								return self



					@property
					def     __snake_case						(	self     :						Dict)    ->							int:
								# check the len of the state_dict keys to see if we have learnable params
								return list(filter(lambda _lowercase: len(list(x.state_dict().keys())) > 0      ,       self.traced))





@dataclass
class   __UpperCAmelCase				:



					'''simple docstring'''





					_UpperCamelCase							=		42
					_UpperCamelCase							=		42
					_UpperCamelCase							=		1
					_UpperCamelCase							=		field(default_factory=lowerCAmelCase				)
					_UpperCamelCase							=		field(default_factory=lowerCAmelCase				)
					_UpperCamelCase							=		True



					def __call__(	self     :						List[str]      ,       _lowercase     :						Tensor)    ->							str:
								A_									= Tracker(self.dest)(_lowercase).parametrized
								A_									= Tracker(self.src)(_lowercase).parametrized

								A_									= list(filter(lambda _lowercase: type(_lowercase) not in self.src_skip      ,       _lowercase))
								A_									= list(filter(lambda _lowercase: type(_lowercase) not in self.dest_skip      ,       _lowercase))

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

								for dest_m, src_m in zip(_lowercase      ,       _lowercase):
											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     :						Union[str, Any]      ,       _lowercase     :						nn.Module)    ->							Optional[int]:
								super().__init__()

								A_									= []
								# - 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}'
											A_									= len(_lowercase) + 1
											feature_blocks.append((F'res{block_index}', v))

								A_									= nn.ModuleDict(_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Tensor)    ->							List[Any]:
								return get_trunk_forward_outputs(
								    _lowercase      ,       out_feat_keys=_lowercase      ,       feature_blocks=self._feature_blocks      ,       )





class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						str)    ->							str:
								A_									= x.split('-')
								return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])



					def __getitem__(	self     :						Any      ,       _lowercase     :						str)    ->							Callable[[], Tuple[nn.Module, Dict]]:
								# default to timm!
								if x not in self:
											A_									= self.convert_name_to_timm(_lowercase)
											A_									= partial(lambda: (timm.create_model(_lowercase      ,       pretrained=_lowercase).eval(), None))

								else:
											A_									= super().__getitem__(_lowercase)

								return val





class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def __getitem__(	self     :						Tuple      ,       _lowercase     :						str)    ->							Callable[[], nn.Module]:
								if "seer" in x and "in1k" not in x:
											A_									= RegNetModel
								else:
											A_									= RegNetForImageClassification
								return val





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Tuple:
			for from_key, to_key in keys:
						A_									= from_state_dict[from_key].clone()
						print(F'Copied key={from_key} to={to_key}'     )
			return to_state_dict





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_ = True       ,)		->		List[Any]:
			print(F'Converting {name}...'     )
			with torch.no_grad():
						A_		, A_									= from_model_func()
						A_									= our_model_func(SCREAMING_SNAKE_CASE_     ).eval()
						A_									= ModuleTransfer(src=SCREAMING_SNAKE_CASE_       ,dest=SCREAMING_SNAKE_CASE_       ,raise_if_mismatch=SCREAMING_SNAKE_CASE_     )
						A_									= torch.randn((1, 3, 224, 224)     )
						module_transfer(SCREAMING_SNAKE_CASE_     )

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

			A_									= our_model(SCREAMING_SNAKE_CASE_       ,output_hidden_states=SCREAMING_SNAKE_CASE_     )
			A_									= (
			    our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     ) else our_outputs.last_hidden_state
			)

			A_									= from_model(SCREAMING_SNAKE_CASE_     )
			A_									= from_output[-1] if type(SCREAMING_SNAKE_CASE_     ) 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:
						A_									= our_outputs.hidden_states[-1]

			assert torch.allclose(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     ), "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=SCREAMING_SNAKE_CASE_       ,)

						A_									= 224 if 'seer' not in name else 384
						# we can use the convnext one
						A_									= AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       ,size=SCREAMING_SNAKE_CASE_     )
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / name       ,commit_message='Add image processor'       ,use_temp_dir=SCREAMING_SNAKE_CASE_       ,)

						print(F'Pushed {name}'     )





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_ = None       ,SCREAMING_SNAKE_CASE_ = True     )		->		Any:
			A_									= 'imagenet-1k-id2label.json'
			A_									= 1000
			A_									= (1, num_labels)

			A_									= 'huggingface/label-files'
			A_									= num_labels
			A_									= json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,repo_type='dataset'     )     )       ,'r'     )     )
			A_									= {int(SCREAMING_SNAKE_CASE_     ): v for k, v in idalabel.items()}

			A_									= idalabel
			A_									= {v: k for k, v in idalabel.items()}

			A_									= partial(SCREAMING_SNAKE_CASE_       ,num_labels=SCREAMING_SNAKE_CASE_       ,idalabel=SCREAMING_SNAKE_CASE_       ,labelaid=SCREAMING_SNAKE_CASE_     )

			A_									= {
			    '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     ),
			}

			A_									= NameToOurModelFuncMap()
			A_									= NameToFromModelFuncMap()
			# add seer weights logic

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

			# pretrained
			A_									= partial(
			    SCREAMING_SNAKE_CASE_       ,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'       ,lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )       ,)

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

			A_									= partial(
			    SCREAMING_SNAKE_CASE_       ,'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()     )       ,)

			A_									= partial(
			    SCREAMING_SNAKE_CASE_       ,'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=2.52     )     )     )       ,)

			# IN1K finetuned
			A_									= partial(
			    SCREAMING_SNAKE_CASE_       ,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'       ,lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )       ,)

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

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

			A_									= partial(
			    SCREAMING_SNAKE_CASE_       ,'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=2.52     )     )     )       ,)

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


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE			=						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.",
							)

							_SCREAMING_SNAKE_CASE			=						parser.parse_args()

							_SCREAMING_SNAKE_CASE			=						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     __snake_case						(	self     :						str)    ->							Optional[int]:
								A_									= pipeline(
								    task='zero-shot-audio-classification'      ,       model='hf-internal-testing/tiny-clap-htsat-unfused')
								A_									= load_dataset('ashraq/esc50')
								A_									= dataset['train']['audio'][-1]['array']
								A_									= audio_classifier(_lowercase      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [{'score': 0.5_01, 'label': 'Sound of a dog'}, {'score': 0.4_99, 'label': 'Sound of vaccum cleaner'}]      ,       )



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



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

								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        {'score': 0.9_99, 'label': 'Sound of a dog'},
								        {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								    ]      ,       )

								A_									= audio_classifier([audio] * 5      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        [
								            {'score': 0.9_99, 'label': 'Sound of a dog'},
								            {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								        ],
								    ]
								    * 5      ,       )
								A_									= audio_classifier(
								    [audio] * 5      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']      ,       batch_size=5)
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        [
								            {'score': 0.9_99, 'label': 'Sound of a dog'},
								            {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								        ],
								    ]
								    * 5      ,       )



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




