






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()
__UpperCamelCase					:		List[str]	  =						logging.get_logger()


@dataclass
class   __SCREAMING_SNAKE_CASE:
							_UpperCAmelCase     =       42
							_UpperCAmelCase     =       field(default_factory=a_	)
							_UpperCAmelCase     =       field(default_factory=a_	)
							def lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      Tensor       ,	UpperCamelCase:      Tensor						)		->		List[Any]:
											snake_case__									=     len(list(m.modules()						)						) == 1 or isinstance(UpperCamelCase       ,	nn.Convad						) or isinstance(UpperCamelCase       ,	nn.BatchNormad						)
											if has_not_submodules:
															self.traced.append(UpperCamelCase						)
							def __call__(     self:      Tuple       ,	UpperCamelCase:      Tensor						)		->		Optional[Any]:
											for m in self.module.modules():
															self.handles.append(m.register_forward_hook(self._forward_hook						)						)
											self.module(UpperCamelCase						)
											[x.remove() for x in self.handles]
											return self


							@property
							def lowerCAmelCase_ (     self:      Dict						)		->		Optional[Any]:
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda UpperCamelCase						: len(list(x.state_dict().keys()						)						) > 0       ,	self.traced						)						)


@dataclass
class   __SCREAMING_SNAKE_CASE:
							_UpperCAmelCase     =       42
							_UpperCAmelCase     =       42
							_UpperCAmelCase     =       1
							_UpperCAmelCase     =       field(default_factory=a_	)
							_UpperCAmelCase     =       field(default_factory=a_	)
							_UpperCAmelCase     =       True
							def __call__(     self:      List[Any]       ,	UpperCamelCase:      Tensor						)		->		Optional[Any]:
											snake_case__									=     Tracker(self.dest						)(UpperCamelCase						).parametrized
											snake_case__									=     Tracker(self.src						)(UpperCamelCase						).parametrized

											snake_case__									=     list(filter(lambda UpperCamelCase						: type(UpperCamelCase						) not in self.src_skip       ,	UpperCamelCase						)						)
											snake_case__									=     list(filter(lambda UpperCamelCase						: type(UpperCamelCase						) not in self.dest_skip       ,	UpperCamelCase						)						)

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

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


class   __SCREAMING_SNAKE_CASE(	nn.Module	):
							def __init__(     self:      str       ,	UpperCamelCase:      nn.Module						)		->		List[Any]:
											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(UpperCamelCase						) + 1
															feature_blocks.append((F'''res{block_index}''', v)						)

											snake_case__									=     nn.ModuleDict(UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Tensor						)		->		int:
											return get_trunk_forward_outputs(
											    UpperCamelCase       ,	out_feat_keys=UpperCamelCase       ,	feature_blocks=self._feature_blocks       ,	)


class   __SCREAMING_SNAKE_CASE(	a_	):
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      str						)		->		str:
											snake_case__									=     x.split('-'						)
											return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]						)


							def __getitem__(     self:      Optional[int]       ,	UpperCamelCase:      str						)		->		Callable[[], Tuple[nn.Module, Dict]]:
											# default to timm!
											if x not in self:
															snake_case__									=     self.convert_name_to_timm(UpperCamelCase						)
															snake_case__									=     partial(lambda: (timm.create_model(UpperCamelCase       ,	pretrained=UpperCamelCase						).eval(), None)						)

											else:
															snake_case__									=     super().__getitem__(UpperCamelCase						)

											return val


class   __SCREAMING_SNAKE_CASE(	a_	):
							def __getitem__(     self:      List[Any]       ,	UpperCamelCase:      str						)		->		Callable[[], nn.Module]:
											if "seer" in x and "in1k" not in x:
															snake_case__									=     RegNetModel
											else:
															snake_case__									=     RegNetForImageClassification
											return val


def      a_ (      _A      ,   _A      ,   _A  )			->			str:







				"""simple docstring"""
				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      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A      ,   _A = True      ,   )			->			Union[str, Any]:







				"""simple docstring"""
				print(f'''Converting {name}...'''  )
				with torch.no_grad():
								snake_case__							,						snake_case__									=     from_model_func()
								snake_case__									=     our_model_func(_A  ).eval()
								snake_case__									=     ModuleTransfer(src=_A      ,   dest=_A      ,   raise_if_mismatch=_A  )
								snake_case__									=     torch.randn((1, 3, 224, 224)  )
								module_transfer(_A  )

				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(_A      ,   our_model.state_dict()      ,   _A  )
								our_model.load_state_dict(_A  )

				snake_case__									=     our_model(_A      ,   output_hidden_states=_A  )
				snake_case__									=     (
				    our_outputs.logits if isinstance(_A      ,   _A  ) else our_outputs.last_hidden_state
				)

				snake_case__									=     from_model(_A  )
				snake_case__									=     from_output[-1] if type(_A  ) 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(_A      ,   _A  ), "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=_A      ,   )

								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=_A  )
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / name      ,   commit_message='Add image processor'      ,   use_temp_dir=_A      ,   )

								print(f'''Pushed {name}'''  )
def      a_ (      _A      ,   _A = None      ,   _A = True  )			->			Optional[Any]:







				"""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(cached_download(hf_hub_url(_A      ,   _A      ,   repo_type='dataset'  )  )      ,   'r'  )  )
				snake_case__									=     {int(_A  ): v for k, v in idalabel.items()}

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

				snake_case__									=     partial(_A      ,   num_labels=_A      ,   idalabel=_A      ,   labelaid=_A  )

				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(_A      ,   model_dir=str(_A  )      ,   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(_A  )
								return model.eval(), model_state_dict["heads"]

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

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

				snake_case__									=     partial(
				    _A      ,   '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(
				    _A      ,   '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(
				    _A      ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'      ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )      ,   )

				snake_case__									=     partial(
				    _A      ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'      ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )      ,   )

				snake_case__									=     partial(
				    _A      ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'      ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()  )      ,   )

				snake_case__									=     partial(
				    _A      ,   '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(
								    _A      ,   names_to_from_model_map[model_name]      ,   names_to_ours_model_map[model_name]      ,   names_to_config[model_name]      ,   _A      ,   _A      ,   )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    _A      ,   names_to_from_model_map[model_name]      ,   names_to_ours_model_map[model_name]      ,   _A      ,   _A      ,   _A      ,   )
				return config, expected_shape


if __name__ == "__main__":
							__UpperCamelCase					:		int	  =						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.""",
							)

							__UpperCamelCase					:		int	  =						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)












from collections.abc import Sequence
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				return sum(c * (x**i) for i, c in enumerate(_A  )  )
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     0.0
				for coeff in reversed(_A  ):
								snake_case__									=     result * x + coeff
				return result


if __name__ == "__main__":
							__UpperCamelCase					:		str	  =						(0.0, 0.0, 5.0, 9.3, 7.0)
							__UpperCamelCase					:		Dict	  =						1_0.0
							print(evaluate_poly(poly, x))
							print(horner(poly, x))




