

import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
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.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__             =		logging.get_logger()







@dataclass
class 						lowercase     :


		"""simple docstring"""




		a__       							=	42
		a__       							=	field(default_factory=_lowercase				)
		a__       							=	field(default_factory=_lowercase				)
		a__       							=	field(default_factory=_lowercase				)


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Optional[Any]								=    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)
				_UpperCamelCase :		Optional[Any]								=    m


		def __call__(					self						,							__snake_case):
			for name, m in self.module.named_modules():
				self.handles.append(m.register_forward_hook(partial(self._forward_hook						,							name=__snake_case)))
			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 {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys())) > 0}







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def __init__(					self						,							__snake_case):
			super().__init__()

			_UpperCamelCase :		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}'''
				_UpperCamelCase :		Dict								=    len(__snake_case) + 1
				feature_blocks.append((f'''res{block_index}''', v))

			_UpperCamelCase :		str								=    nn.ModuleDict(__snake_case)






		def 		A__       (					self						,							__snake_case):
			return get_trunk_forward_outputs(
			    __snake_case						,							out_feat_keys=__snake_case						,							feature_blocks=self._feature_blocks						,							)







class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		def 		A__       (					self):
			return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]






def        lowerCamelCase_  (				UpperCAmelCase_				:  str						)    ->					Dict[str, str]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    RegNetConfig(depths=[2, 7, 1_7, 1]				,							hidden_sizes=[8, 8, 8, 8]				,							groups_width=8						)
	if "in1k" in model_name:
		_UpperCamelCase :		Any								=    RegNetForImageClassification(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Tuple								=    RegNetModel(UpperCAmelCase_						)
	# create from model (with small weights)
	_UpperCamelCase :		Any								=    FakeRegNetVisslWrapper(
	    RegNet(FakeRegNetParams(depth=2_7				,							group_width=1_0_1_0				,							w_a=1_7_4_4				,							w_a=6_2_0.8_3				,							w_m=2.5_2						)						)						)

	with torch.no_grad():
		_UpperCamelCase :		Optional[int]								=    from_model.eval()
		_UpperCamelCase :		List[str]								=    our_model.eval()

		_UpperCamelCase :		Any								=    torch.randn((1, 3, 3_2, 3_2)						)
		# trace both
		_UpperCamelCase :		List[Any]								=    Tracker(UpperCAmelCase_						)
		_UpperCamelCase :		List[Any]								=    dest_tracker(UpperCAmelCase_						).parametrized

		pprint(dest_tracker.nameamodule						)
		_UpperCamelCase :		Any								=    Tracker(UpperCAmelCase_						)
		_UpperCamelCase :		Union[str, Any]								=    src_tracker(UpperCAmelCase_						).parametrized

	# convert the keys -> module dict to keys -> params
	def to_params_dict(UpperCAmelCase_				:  Union[str, Any]						):
		_UpperCamelCase :		str								=    OrderedDict()
		for name, module in dict_with_modules.items():
			for param_name, param in module.state_dict().items():
				_UpperCamelCase :		Optional[Any]								=    param
		return params_dict

	_UpperCamelCase :		str								=    {}

	_UpperCamelCase :		Union[str, Any]								=    to_params_dict(UpperCAmelCase_						)
	_UpperCamelCase :		List[str]								=    to_params_dict(UpperCAmelCase_						)

	for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()				,							dst_state_dict.items()						):
		_UpperCamelCase :		List[Any]								=    dest_key
		logger.info(F'''{src_key} -> {dest_key}'''						)
	# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
	if "in1k" in model_name:
		_UpperCamelCase :		Any								=    'classifier.1.weight'
		_UpperCamelCase :		Union[str, Any]								=    'classifier.1.bias'

	return from_to_ours_keys






def        lowerCamelCase_  (				UpperCAmelCase_				:  Path				,							UpperCAmelCase_				:  str = None				,							UpperCAmelCase_				:  bool = True						)    ->					List[Any]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    'imagenet-1k-id2label.json'
	_UpperCamelCase :		List[Any]								=    1_0_0_0

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

	_UpperCamelCase :		Dict								=    idalabel
	_UpperCamelCase :		Optional[int]								=    {v: k for k, v in idalabel.items()}

	_UpperCamelCase :		Tuple								=    partial(UpperCAmelCase_				,							num_labels=UpperCAmelCase_				,							idalabel=UpperCAmelCase_				,							labelaid=UpperCAmelCase_						)

	_UpperCamelCase :		int								=    {
	    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_7, 1]				,							hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]				,							groups_width=1_0_1_0						),
	    # finetuned on imagenet
	    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_7, 1]				,							hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]				,							groups_width=1_0_1_0						),
	}

	# add seer weights logic
	def load_using_classy_vision(UpperCAmelCase_				:  str						) -> Tuple[Dict, Dict]:
		_UpperCamelCase :		Union[str, Any]								=    torch.hub.load_state_dict_from_url(UpperCAmelCase_				,							model_dir=str(UpperCAmelCase_						)				,							map_location='cpu'						)
		# check if we have a head, if yes add it
		_UpperCamelCase :		int								=    files['classy_state_dict']['base_model']['model']
		return model_state_dict["trunk"], model_state_dict["heads"]

	_UpperCamelCase :		Union[str, Any]								=    {
	    'regnet-y-10b-seer': partial(
	        UpperCAmelCase_				,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'				,							),
	    'regnet-y-10b-seer-in1k': partial(
	        UpperCAmelCase_				,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'				,							),
	}

	_UpperCamelCase :		Tuple								=    get_from_to_our_keys(UpperCAmelCase_						)

	if not (save_directory / F'''{model_name}.pth''').exists():
		logger.info('Loading original state_dict.'						)
		_UpperCamelCase						,		_UpperCamelCase :		List[Any]								=    names_to_from_model[model_name]()
		_UpperCamelCase :		List[str]								=    from_state_dict_trunk
		if "in1k" in model_name:
			# add the head
			_UpperCamelCase :		Any								=    {**from_state_dict_trunk, **from_state_dict_head}
		logger.info('Done!'						)

		_UpperCamelCase :		Tuple								=    {}

		_UpperCamelCase :		int								=    list(from_state_dict.keys()						)
		_UpperCamelCase :		Union[str, Any]								=    R'\.block.-part.'
		# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
		for key in from_state_dict.keys():
			# remove the weird "block[0,1]-part" from the key
			_UpperCamelCase :		List[str]								=    re.sub(UpperCAmelCase_				,							''				,							UpperCAmelCase_						)
			# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
			_UpperCamelCase :		Optional[Any]								=    from_to_ours_keys[src_key]
			# store the parameter with our key
			_UpperCamelCase :		List[str]								=    from_state_dict[key]
			not_used_keys.remove(UpperCAmelCase_						)
		# check that all keys have been updated
		assert len(UpperCAmelCase_						) == 0, F'''Some keys where not used {",".join(UpperCAmelCase_						)}'''

		logger.info(F'''The following keys were not used: {",".join(UpperCAmelCase_						)}'''						)

		# save our state dict to disk
		torch.save(UpperCAmelCase_				,							save_directory / F'''{model_name}.pth'''						)

		del converted_state_dict
	else:
		logger.info('The state_dict was already stored on disk.'						)
	if push_to_hub:
		logger.info(F'''Token is {os.environ["HF_TOKEN"]}'''						)
		logger.info('Loading our model.'						)
		# create our model
		_UpperCamelCase :		Any								=    names_to_config[model_name]
		_UpperCamelCase :		str								=    RegNetModel
		if "in1k" in model_name:
			_UpperCamelCase :		Dict								=    RegNetForImageClassification
		_UpperCamelCase :		List[Any]								=    our_model_func(UpperCAmelCase_						)
		# place our model to the meta device (so remove all the weights)
		our_model.to(torch.device('meta'						)						)
		logger.info('Loading state_dict in our model.'						)
		# load state dict
		_UpperCamelCase :		Union[str, Any]								=    our_model.state_dict().keys()
		PreTrainedModel._load_pretrained_model_low_mem(
		    UpperCAmelCase_				,							UpperCAmelCase_				,							[save_directory / F'''{model_name}.pth''']						)
		logger.info('Finally, pushing!'						)
		# push it to hub
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / model_name				,							commit_message='Add model'				,							output_dir=save_directory / model_name				,							)
		_UpperCamelCase :		Any								=    3_8_4
		# we can use the convnext one
		_UpperCamelCase :		int								=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				,							size=UpperCAmelCase_						)
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / model_name				,							commit_message='Add image processor'				,							output_dir=save_directory / model_name				,							)


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

							lowerCAmelCase__             =		parser.parse_args()

							lowerCAmelCase__             =		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 itertools import combinations, product


def    _lowerCamelCase       ( lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if xa == ya == 0 or xa == ya == 0:
          return False
     UpperCAmelCase_				:  Union[str, Any]  = xa * xa + ya * ya
     UpperCAmelCase_				:  str  = xa * xa + ya * ya
     UpperCAmelCase_				:  str  = (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
     return (
         a_square + b_square == c_square
         or a_square + c_square == b_square
         or b_square + c_square == a_square
     )


def    _lowerCamelCase       ( lowerCamelCase_   :				int = 50  ):



     """simple docstring"""





     return sum(
         1
         for pta, pta in combinations(product(range(limit + 1  )      ,	repeat=2  )      ,	2  )
         if is_right(*lowerCamelCase_      ,	*lowerCamelCase_  )  )


if __name__ == "__main__":
 print(f'''{solution() = }''')






