


"""simple docstring"""




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()
lowercase_			     =  logging.get_logger()
@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			nn.Module
		__UpperCAmelCase		:			List[nn.Module]										=				field(default_factory=__SCREAMING_SNAKE_CASE       )
		__UpperCAmelCase		:			list										=				field(default_factory=__SCREAMING_SNAKE_CASE       )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a      ):
				__a           =			len(list(m.modules()      )      ) == 1 or isinstance(_a							, nn.Convad      ) or isinstance(_a							, nn.BatchNormad      )
				if has_not_submodules:
						self.traced.append(_a      )

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





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







@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			nn.Module
		__UpperCAmelCase		:			nn.Module
		__UpperCAmelCase		:			int										=				0
		__UpperCAmelCase		:			List										=				field(default_factory=__SCREAMING_SNAKE_CASE       )
		__UpperCAmelCase		:			List										=				field(default_factory=__SCREAMING_SNAKE_CASE       )

		def __call__(  self							, _a      ):
				__a           =			Tracker(self.dest      )(_a      ).parametrized
				__a           =			Tracker(self.src      )(_a      ).parametrized

				__a           =			list(filter(lambda _a      : type(_a      ) not in self.src_skip							, _a      )      )
				__a           =			list(filter(lambda _a      : type(_a      ) not in self.dest_skip							, _a      )      )

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

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







def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     ResNetConfig    ,					lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     bool = True			) ->      Tuple:
		print(f'''Converting {name}...'''			)
		with torch.no_grad():
				__a           =			timm.create_model(lowerCAmelCase__    ,					pretrained=lowerCAmelCase__			).eval()
				__a           =			ResNetForImageClassification(lowerCAmelCase__			).eval()
				__a           =			ModuleTransfer(src=lowerCAmelCase__    ,					dest=lowerCAmelCase__			)
				__a           =			torch.randn((1, 3, 224, 224)			)
				module_transfer(lowerCAmelCase__			)

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

		__a           =			f'''resnet{'-'.join(name.split('resnet'			)			)}'''
		print(lowerCAmelCase__			)

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

				# we can use the convnext one
				__a           =			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=lowerCAmelCase__    ,					)

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

def        lowercase							(		lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     str = None    ,					lowerCAmelCase__ :     bool = True			) ->      Optional[Any]:
		__a           =			'''imagenet-1k-id2label.json'''
		__a           =			1000
		__a           =			(1, num_labels)

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

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

		__a           =			partial(lowerCAmelCase__    ,					num_labels=lowerCAmelCase__    ,					idalabel=lowerCAmelCase__    ,					labelaid=lowerCAmelCase__			)

		__a           =			{
		    '''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(lowerCAmelCase__    ,					names_to_config[model_name]    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		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 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.",
				)

				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)







import math
from datetime import datetime, timedelta


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     year % 1_9
      SCREAMING_SNAKE_CASE_      =     year % 4
      SCREAMING_SNAKE_CASE_      =     year % 7
      SCREAMING_SNAKE_CASE_      =     math.floor(year / 1_0_0 )
      SCREAMING_SNAKE_CASE_      =     math.floor((1_3 + 8 * leap_day_inhibits) / 2_5 )
      SCREAMING_SNAKE_CASE_      =     leap_day_inhibits / 4
      SCREAMING_SNAKE_CASE_      =     (
          1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
      ) % 3_0
      SCREAMING_SNAKE_CASE_      =     (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

      # days to be added to March 21
      SCREAMING_SNAKE_CASE_      =     (1_9 * metonic_cycle + secular_moon_shift) % 3_0

      # PHM -> Paschal Full Moon
      SCREAMING_SNAKE_CASE_      =     (
          2 * julian_leap_year
          + 4 * non_leap_year
          + 6 * days_to_add
          + century_starting_point
      ) % 7

      if days_to_add == 2_9 and days_from_phm_to_sunday == 6:
            return datetime(__UpperCamelCase   ,					4   ,					1_9 )
      elif days_to_add == 2_8 and days_from_phm_to_sunday == 6:
            return datetime(__UpperCamelCase   ,					4   ,					1_8 )
      else:
            return datetime(__UpperCamelCase   ,					3   ,					2_2 ) + timedelta(
                days=int(days_to_add + days_from_phm_to_sunday ) )


if __name__ == "__main__":
      for year in (19_94, 20_00, 20_10, 20_21, 20_23):
            A   :     Dict       =				"will be" if year > datetime.now().year else "was"
            print(f"Easter in {year} {tense} {gauss_easter(year)}")


