





'''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()
__UpperCamelCase					    =	logging.get_logger()




@dataclass
class  _A :
		lowercase__:   nn.Module
		lowercase__:   List[nn.Module]				     =   field(default_factory=__lowercase		)
		lowercase__:   list				     =   field(default_factory=__lowercase		)

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Tensor  ,      __magic_name__					:   Tensor			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     len(list(m.modules()			)			) == 1 or isinstance(__magic_name__  ,      nn.Convad			) or isinstance(__magic_name__  ,      nn.BatchNormad			)
					if has_not_submodules:
								self.traced.append(__magic_name__			)

		def __call__(   self					:   Optional[int]  ,      __magic_name__					:   Tensor			)					->    Any:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    str:


					"""simple docstring"""

					return list(filter(lambda __magic_name__			: len(list(x.state_dict().keys()			)			) > 0  ,      self.traced			)			)





@dataclass
class  _A :
		lowercase__:   nn.Module
		lowercase__:   nn.Module
		lowercase__:   int				     =   1
		lowercase__:   List				     =   field(default_factory=__lowercase		)
		lowercase__:   List				     =   field(default_factory=__lowercase		)
		lowercase__:   bool				     =   True

		def __call__(   self					:   Dict  ,      __magic_name__					:   Tensor			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Any							      =     Tracker(self.dest			)(__magic_name__			).parametrized
					__snake_case					:      Dict							      =     Tracker(self.src			)(__magic_name__			).parametrized

					__snake_case					:      List[str]							      =     list(filter(lambda __magic_name__			: type(__magic_name__			) not in self.src_skip  ,      __magic_name__			)			)
					__snake_case					:      List[str]							      =     list(filter(lambda __magic_name__			: type(__magic_name__			) not in self.dest_skip  ,      __magic_name__			)			)

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

					for dest_m, src_m in zip(__magic_name__  ,      __magic_name__			):
								dest_m.load_state_dict(src_m.state_dict()			)
								if self.verbose == 1:
											print(f'''Transfered from={src_m} to={dest_m}'''			)





class  _A (					nn.Module		):

		def __init__(   self					:   Dict  ,      __magic_name__					:   nn.Module			)					->    Any:


					"""simple docstring"""

					super().__init__()

					__snake_case					:      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}'''
								__snake_case					:      Optional[int]							      =     len(__magic_name__			) + 1
								feature_blocks.append((f'''res{block_index}''', v)			)

					__snake_case					:      str							      =     nn.ModuleDict(__magic_name__			)

		def 	lowercase__     (   self					:   int  ,      __magic_name__					:   Tensor			)					->    Tuple:


					"""simple docstring"""

					return get_trunk_forward_outputs(
					    __magic_name__  ,      out_feat_keys=__magic_name__  ,      feature_blocks=self._feature_blocks  ,      )





class  _A (					__lowercase		):

		def 	lowercase__     (   self					:   Any  ,      __magic_name__					:   str			)					->    str:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     x.split("""-"""			)
					return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]			)

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


					"""simple docstring"""

					if x not in self:
								__snake_case					:      Tuple							      =     self.convert_name_to_timm(__magic_name__			)
								__snake_case					:      List[str]							      =     partial(lambda: (timm.create_model(__magic_name__  ,      pretrained=__magic_name__			).eval(), None)			)

					else:
								__snake_case					:      Tuple							      =     super().__getitem__(__magic_name__			)

					return val





class  _A (					__lowercase		):

		def __getitem__(   self					:   Optional[Any]  ,      __magic_name__					:   str			)					->    Callable[[], nn.Module]:


					"""simple docstring"""

					if "seer" in x and "in1k" not in x:
								__snake_case					:      Any							      =     RegNetModel
					else:
								__snake_case					:      Tuple							      =     RegNetForImageClassification
					return val







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Optional[int]:



			"""simple docstring"""


			for from_key, to_key in keys:
						__snake_case					:      int							      =     from_state_dict[from_key].clone()
						print(F'''Copied key={from_key} to={to_key}''' )
			return to_state_dict







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = True    ,	) ->				Union[str, Any]:



			"""simple docstring"""


			print(F'''Converting {name}...''' )
			with torch.no_grad():
						__snake_case      ,     __snake_case					:      str							      =     from_model_func()
						__snake_case					:      Optional[Any]							      =     our_model_func(_lowerCamelCase ).eval()
						__snake_case					:      Dict							      =     ModuleTransfer(src=_lowerCamelCase    ,	dest=_lowerCamelCase    ,	raise_if_mismatch=_lowerCamelCase )
						__snake_case					:      Union[str, Any]							      =     torch.randn((1, 3, 224, 224) )
						module_transfer(_lowerCamelCase )

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

			__snake_case					:      str							      =     our_model(_lowerCamelCase    ,	output_hidden_states=_lowerCamelCase )
			__snake_case					:      Any							      =     (
			    our_outputs.logits if isinstance(_lowerCamelCase    ,	_lowerCamelCase ) else our_outputs.last_hidden_state
			)

			__snake_case					:      List[str]							      =     from_model(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     from_output[-1] if type(_lowerCamelCase ) 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:
						__snake_case					:      Union[str, Any]							      =     our_outputs.hidden_states[-1]

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

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

						print(F'''Pushed {name}''' )







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = True ) ->				List[str]:



			"""simple docstring"""


			__snake_case					:      int							      =     """imagenet-1k-id2label.json"""
			__snake_case					:      int							      =     1000
			__snake_case					:      Any							      =     (1, num_labels)

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

			__snake_case					:      Optional[Any]							      =     idalabel
			__snake_case					:      Any							      =     {v: k for k, v in idalabel.items()}

			__snake_case					:      int							      =     partial(_lowerCamelCase    ,	num_labels=_lowerCamelCase    ,	idalabel=_lowerCamelCase    ,	labelaid=_lowerCamelCase )

			__snake_case					:      Optional[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 ),
			}

			__snake_case					:      List[str]							      =     NameToOurModelFuncMap()
			__snake_case					:      List[str]							      =     NameToFromModelFuncMap()
			# add seer weights logic

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

			# pretrained
			__snake_case					:      List[str]							      =     partial(
			    _lowerCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf() )    ,	)

			__snake_case					:      List[str]							      =     partial(
			    _lowerCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf() )    ,	)

			__snake_case					:      Tuple							      =     partial(
			    _lowerCamelCase    ,	"""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() )    ,	)

			__snake_case					:      str							      =     partial(
			    _lowerCamelCase    ,	"""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=6_20.83    ,	w_m=2.52 ) ) )    ,	)

			# IN1K finetuned
			__snake_case					:      Dict							      =     partial(
			    _lowerCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf() )    ,	)

			__snake_case					:      List[Any]							      =     partial(
			    _lowerCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf() )    ,	)

			__snake_case					:      Union[str, Any]							      =     partial(
			    _lowerCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )    ,	)

			__snake_case					:      str							      =     partial(
			    _lowerCamelCase    ,	"""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=6_20.83    ,	w_m=2.52 ) ) )    ,	)

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


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

	__UpperCamelCase					    =	parser.parse_args()

	__UpperCamelCase					    =	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)





from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap

def 			lowerCamelCase__			(       _a):
	return getitem, k

def 			lowerCamelCase__			(       _a				,      _a):
	return setitem, k, v

def 			lowerCamelCase__			(       _a):
	return delitem, k

def 			lowerCamelCase__			(       _a				,      _a				,      *_a):
	try:
		return fun(_a				,      *_a), None
	except Exception as e:
		return None, e


a_      =     (
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
)

a_      =     [
    _set('key_a', 'val_a'),
    _set('key_a', 'val_b'),
]

a_      =     [
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
    _del('key_a'),
    _del('key_b'),
    _set('key_a', 'val_a'),
    _del('key_a'),
]

a_      =     [
    _get('key_a'),
    _del('key_a'),
    _set('key_a', 'val_a'),
    _del('key_a'),
    _del('key_a'),
    _get('key_a'),
]

a_      =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

a_      =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('key_a', 'val_b'),
]

@pytest.mark.parametrize(
    "operations"				,      (
        pytest.param(_add_items				,      id="add items"),
        pytest.param(_overwrite_items				,      id="overwrite items"),
        pytest.param(_delete_items				,      id="delete items"),
        pytest.param(_access_absent_items				,      id="access absent items"),
        pytest.param(_add_with_resize_up				,      id="add with resize up"),
        pytest.param(_add_with_resize_down				,      id="add with resize down"),
    )				,      )
def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							HashMap(initial_block_size=4)
	SCREAMING_SNAKE_CASE :   List[str]					  =							{}
	for _, (fun, *args) in enumerate(_a):
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							_run_operation(_a				,      _a				,      *_a)
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							_run_operation(_a				,      _a				,      *_a)
		assert my_res == py_res
		assert str(_a) == str(_a)
		assert set(_a) == set(_a)
		assert len(_a) == len(_a)
		assert set(my.items()) == set(py.items())


def 			lowerCamelCase__			(       ):
	def is_public(_a) -> bool:
		return not name.startswith("_")

	SCREAMING_SNAKE_CASE :   List[str]					  =							{name for name in dir({}) if is_public(_a)}
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							{name for name in dir(HashMap()) if is_public(_a)}

	assert dict_public_names > hash_public_names