




'''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()
_lowercase  =			logging.get_logger()
@dataclass
class 						a_				:
	lowercase_       :     Optional[int]			  =      42
	lowercase_       :     Optional[int]			  =      field(default_factory=_lowerCAmelCase						)
	lowercase_       :     List[Any]			  =      field(default_factory=_lowerCAmelCase						)




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  str      ):
							__snake_case						 =   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     :  Any       ,		__lowerCAmelCase     :  int      ):
							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   lowercase__   (       self     :  List[Any]      ):
							return list(filter(lambda __lowerCAmelCase      : len(list(x.state_dict().keys()      )      ) > 0       ,		self.traced      )      )





@dataclass
class 						a_				:
	lowercase_       :     Optional[int]			  =      42
	lowercase_       :     Any			  =      42
	lowercase_       :     Union[str, Any]			  =      1
	lowercase_       :     List[Any]			  =      field(default_factory=_lowerCAmelCase						)
	lowercase_       :     List[Any]			  =      field(default_factory=_lowerCAmelCase						)
	lowercase_       :     int			  =      True




	def __call__(       self     :  Dict       ,		__lowerCAmelCase     :  Any      ):
							__snake_case						 =   Tracker(self.dest      )(__lowerCAmelCase      ).parametrized
							__snake_case						 =   Tracker(self.src      )(__lowerCAmelCase      ).parametrized

							__snake_case						 =   list(filter(lambda __lowerCAmelCase      : type(__lowerCAmelCase      ) not in self.src_skip       ,		__lowerCAmelCase      )      )
							__snake_case						 =   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 						a_				(			nn.Module						):




	def __init__(       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  Dict      ):
							super().__init__()

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

							__snake_case						 =   nn.ModuleDict(__lowerCAmelCase      )




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  List[Any]      ):
							return get_trunk_forward_outputs(
							    __lowerCAmelCase       ,		out_feat_keys=__lowerCAmelCase       ,		feature_blocks=self._feature_blocks       ,		)





class 						a_				(			_lowerCAmelCase						):




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




	def __getitem__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  List[str]      ):
							# default to timm!
							if x not in self:
													__snake_case						 =   self.convert_name_to_timm(__lowerCAmelCase      )
													__snake_case						 =   partial(lambda: (timm.create_model(__lowerCAmelCase       ,		pretrained=__lowerCAmelCase      ).eval(), None)      )

							else:
													__snake_case						 =   super().__getitem__(__lowerCAmelCase      )

							return val





class 						a_				(			_lowerCAmelCase						):




	def __getitem__(       self     :  Tuple       ,		__lowerCAmelCase     :  str      ):
							if "seer" in x and "in1k" not in x:
													__snake_case						 =   RegNetModel
							else:
													__snake_case						 =   RegNetForImageClassification
							return val





def 						lowerCamelCase__   (				a    ,	a    ,	a							):
						for from_key, to_key in keys:
												__snake_case						 =   from_state_dict[from_key].clone()
												print(f'Copied key={from_key} to={to_key}'							)
						return to_state_dict
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a    ,	a    ,	a = True    ,	):
						print(f'Converting {name}...'							)
						with torch.no_grad():
												__snake_case ,       __snake_case						 =   from_model_func()
												__snake_case						 =   our_model_func(SCREAMING_SNAKE_CASE__							).eval()
												__snake_case						 =   ModuleTransfer(src=SCREAMING_SNAKE_CASE__    ,	dest=SCREAMING_SNAKE_CASE__    ,	raise_if_mismatch=SCREAMING_SNAKE_CASE__							)
												__snake_case						 =   torch.randn((1, 3, 224, 224)							)
												module_transfer(SCREAMING_SNAKE_CASE__							)

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

						__snake_case						 =   our_model(SCREAMING_SNAKE_CASE__    ,	output_hidden_states=SCREAMING_SNAKE_CASE__							)
						__snake_case						 =   (
						    our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE__    ,	SCREAMING_SNAKE_CASE__							) else our_outputs.last_hidden_state
						)

						__snake_case						 =   from_model(SCREAMING_SNAKE_CASE__							)
						__snake_case						 =   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:
												__snake_case						 =   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__    ,	)

												__snake_case						 =   224 if 'seer' not in name else 384
												# we can use the convnext one
												__snake_case						 =   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__   (				a    ,	a = None    ,	a = True							):
						__snake_case						 =   'imagenet-1k-id2label.json'
						__snake_case						 =   1000
						__snake_case						 =   (1, num_labels)

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

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

						__snake_case						 =   partial(SCREAMING_SNAKE_CASE__    ,	num_labels=SCREAMING_SNAKE_CASE__    ,	idalabel=SCREAMING_SNAKE_CASE__    ,	labelaid=SCREAMING_SNAKE_CASE__							)

						__snake_case						 =   {
						    '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, 11110, 28280]    ,	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, 11110, 28280]    ,	groups_width=1010							),
						}

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

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

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

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

						__snake_case						 =   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()							)    ,	)

						__snake_case						 =   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
						__snake_case						 =   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()							)    ,	)

						__snake_case						 =   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()							)    ,	)

						__snake_case						 =   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()							)    ,	)

						__snake_case						 =   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__":
	_lowercase  =			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.""",
	)

	_lowercase  =			parser.parse_args()

	_lowercase  =			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'''



from timeit import timeit

__UpperCamelCase   :							int         =   {
    """MALAYALAM""": True,
    """String""": False,
    """rotor""": True,
    """level""": True,
    """A""": True,
    """BB""": True,
    """ABC""": False,
    """amanaplanacanalpanama""": True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   __a					  =     0
   __a					  =     len(SCREAMING_SNAKE_CASE__     ) - 1
   while start_i < end_i:
      if s[start_i] == s[end_i]:
         start_i += 1
         end_i -= 1
      else:
         return False
   return True



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   __a					  =     len(SCREAMING_SNAKE_CASE__     ) // 2
   __a					  =     len(SCREAMING_SNAKE_CASE__     )

   # We need to traverse till half of the length of string
   # as we can get access of the i'th last element from
   # i'th index.
   # eg: [0,1,2,3,4,5] => 4th index can be accessed
   # with the help of 1st index (i==n-i-1)
   # where n is length of string
   return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   if len(SCREAMING_SNAKE_CASE__     ) <= 2:
      return True
   if s[0] == s[len(SCREAMING_SNAKE_CASE__     ) - 1]:
      return is_palindrome_recursive(s[1:-1]     )
   else:
      return False



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   return s == s[::-1]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      None:

   """simple docstring"""
   __a					  =     f"""all({name}(key) is value for key, value in test_data.items())"""
   __a					  =     f"""from __main__ import test_data, {name}"""
   __a					  =     500000
   __a					  =     timeit(stmt=SCREAMING_SNAKE_CASE__,       setup=SCREAMING_SNAKE_CASE__,       number=SCREAMING_SNAKE_CASE__     )
   print(f"""{name:<35} finished {number:,} runs in {result:.5f} seconds"""     )


if __name__ == "__main__":
 for key, value in test_data.items():
  assert is_palindrome(key) is is_palindrome_recursive(key)
  assert is_palindrome(key) is is_palindrome_slice(key)
  print(f"""{key:21} {value}""")
 print("""a man a plan a canal panama""")

 # finished 500,000 runs in 0.46793 seconds
 benchmark_function("""is_palindrome_slice""")
 # finished 500,000 runs in 0.85234 seconds
 benchmark_function("""is_palindrome""")
 # finished 500,000 runs in 1.32028 seconds
 benchmark_function("""is_palindrome_recursive""")
 # finished 500,000 runs in 2.08679 seconds
 benchmark_function("""is_palindrome_traversal""")