
def a__    (       A_					):






  '''simple docstring'''





  if len(A_					) <= 1:
    return [tuple(A_					)]

  __magic_name__				     =       []

  def generate(A_,     A_					):
    __magic_name__				     =       [0] * n
    res.append(tuple(A_					)					)

    __magic_name__				     =       0
    while i < n:
      if c[i] < i:
        if i % 2 == 0:
          __magic_name__     ,    __magic_name__				     =       arr[i], arr[0]
        else:
          __magic_name__     ,    __magic_name__				     =       arr[i], arr[c[i]]
        res.append(tuple(A_					)					)
        c[i] += 1
        __magic_name__				     =       0
      else:
        __magic_name__				     =       0
        i += 1

  generate(len(A_					),     A_					)
  return res


if __name__ == "__main__":
    __lowerCAmelCase			:     Tuple           =   input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase			:     int           =   [int(item) for item in user_input.split(',')]
    print(heaps(arr))




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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase		:   List[Any]										=       logging.get_logger()







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: List[nn.Module]       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: list       			=			field(default_factory=lowercase_			)




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    len(list(m.modules()       )       ) == 1 or isinstance(snake_case      ,   nn.Convad       ) or isinstance(snake_case      ,   nn.BatchNormad       )
											if has_not_submodules:
																		self.traced.append(snake_case       )




				def __call__(					self      ,   snake_case       ):
											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook       )       )
											self.module(snake_case       )
											[x.remove() for x in self.handles]
											return self




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







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: int       			=			0
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)




				def __call__(					self      ,   snake_case       ):
											snake_case_							=    Tracker(self.dest       )(snake_case       ).parametrized
											snake_case_							=    Tracker(self.src       )(snake_case       ).parametrized

											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.src_skip      ,   snake_case       )       )
											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.dest_skip      ,   snake_case       )       )

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

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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							print(F'''Converting {name}...'''      )
							with torch.no_grad():
														snake_case_							=    timm.create_model(UpperCamelCase__   ,							pretrained=UpperCamelCase__      ).eval()
														snake_case_							=    ResNetForImageClassification(UpperCamelCase__      ).eval()
														snake_case_							=    ModuleTransfer(src=UpperCamelCase__   ,							dest=UpperCamelCase__      )
														snake_case_							=    torch.randn((1, 3, 224, 224)      )
														module_transfer(UpperCamelCase__      )

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

							snake_case_							=    F'''resnet{"-".join(name.split("resnet"      )      )}'''
							print(UpperCamelCase__      )

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

														# we can use the convnext one
														snake_case_							=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'      )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name   ,							commit_message='Add image processor'   ,							use_temp_dir=UpperCamelCase__   ,							)

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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = None   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							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(hf_hub_download(UpperCamelCase__   ,							UpperCamelCase__   ,							repo_type='dataset'      )   ,							'r'      )      )
							snake_case_							=    {int(UpperCamelCase__      ): v for k, v in idalabel.items()}

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

							snake_case_							=    partial(UpperCamelCase__   ,							num_labels=UpperCamelCase__   ,							idalabel=UpperCamelCase__   ,							labelaid=UpperCamelCase__      )

							snake_case_							=    {
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]   ,							hidden_sizes=[64, 128, 256, 512]   ,							layer_type='basic'      ),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]   ,							hidden_sizes=[64, 128, 256, 512]   ,							layer_type='basic'      ),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 23, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 36, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							}

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


if __name__ == "__main__":
					_UpperCAmelCase		:   List[str]										=       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 resnet* architecture,"""
					        """ currently: resnet18,26,34,50,101,152. 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		:   Optional[Any]										=       parser.parse_args()
					_UpperCAmelCase		:   Path										=       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)




