"""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()
SCREAMING_SNAKE_CASE									=						logging.get_logger()



@dataclass
class  _lowerCamelCase			:
				_snake_case     =  42
				_snake_case     =  field(default_factory=__lowerCamelCase     )
				_snake_case     =  field(default_factory=__lowerCamelCase     )







				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Tensor			,					lowerCamelCase_    :       Tensor						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							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    :       Dict			,					lowerCamelCase_    :       Tensor						):

					"""simple docstring"""






					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 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






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



@dataclass
class  _lowerCamelCase			:
				_snake_case     =  42
				_snake_case     =  42
				_snake_case     =  0
				_snake_case     =  field(default_factory=__lowerCamelCase     )
				_snake_case     =  field(default_factory=__lowerCamelCase     )







				def __call__( self    :       Optional[Any]			,					lowerCamelCase_    :       Tensor						):

					"""simple docstring"""






					_lowercase :    List[str]							=							Tracker(self.dest						)(lowerCamelCase_						).parametrized
					_lowercase :    str							=							Tracker(self.src						)(lowerCamelCase_						).parametrized

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

					if len(lowerCamelCase_						) != len(lowerCamelCase_						):
						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}'''						)



def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = True			):






	"""simple docstring"""





	print(F'''Converting {name}...'''			)
	with torch.no_grad():
		_lowercase :    Tuple							=							timm.create_model(__UpperCAmelCase     ,pretrained=__UpperCAmelCase			).eval()
		_lowercase :    Optional[Any]							=							ResNetForImageClassification(__UpperCAmelCase			).eval()
		_lowercase :    Any							=							ModuleTransfer(src=__UpperCAmelCase     ,dest=__UpperCAmelCase			)
		_lowercase :    Dict							=							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."

	_lowercase :    int							=							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
		_lowercase :    int							=							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"""





	_lowercase :    Optional[int]							=							'imagenet-1k-id2label.json'
	_lowercase :    Optional[int]							=							1_000
	_lowercase :    Any							=							(1, num_labels)

	_lowercase :    Optional[int]							=							'huggingface/label-files'
	_lowercase :    Optional[int]							=							num_labels
	_lowercase :    Optional[Any]							=							json.load(open(hf_hub_download(__UpperCAmelCase     ,__UpperCAmelCase     ,repo_type='dataset'			)     ,'r'			)			)
	_lowercase :    Optional[Any]							=							{int(__UpperCAmelCase			): v for k, v in idalabel.items()}

	_lowercase :    Dict							=							idalabel
	_lowercase :    int							=							{v: k for k, v in idalabel.items()}

	_lowercase :    List[str]							=							partial(__UpperCAmelCase     ,num_labels=__UpperCAmelCase     ,idalabel=__UpperCAmelCase     ,labelaid=__UpperCAmelCase			)

	_lowercase :    Union[str, Any]							=							{
	    '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, 1_024, 2_048]     ,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, 1_024, 2_048]     ,layer_type='bottleneck'			),
	    'resnet101': ImageNetPreTrainedConfig(
	        depths=[3, 4, 23, 3]     ,hidden_sizes=[256, 512, 1_024, 2_048]     ,layer_type='bottleneck'			),
	    'resnet152': ImageNetPreTrainedConfig(
	        depths=[3, 8, 36, 3]     ,hidden_sizes=[256, 512, 1_024, 2_048]     ,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__":
						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 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.',
						)

						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"""







from __future__ import annotations

import collections
import pprint
from pathlib import Path


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	return "".join(sorted(__UpperCAmelCase			)			)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	return word_by_signature[signature(__UpperCAmelCase			)]


SCREAMING_SNAKE_CASE									=						Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
SCREAMING_SNAKE_CASE									=						sorted({word.strip().lower() for word in data.splitlines()})

SCREAMING_SNAKE_CASE									=						collections.defaultdict(list)
for word in word_list:
						word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

						with open('anagrams.txt', 'w') as file:
												file.write('all_anagrams = \n ')
												file.write(pprint.pformat(all_anagrams))



