




"""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()
UpperCamelCase__						=			logging.get_logger()




@dataclass
class   a__			:
						snake_case__             = 42
						snake_case__             = field(default_factory=UpperCamelCase_  )
						snake_case__             = field(default_factory=UpperCamelCase_  )





						def       __UpperCamelCase     (							self      :	int     ,a__      :	int     ,a__      :	Tensor     ,a__      :	Tensor)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  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      :	List[Any]     ,a__      :	Tensor)						->       List[str]:




											"""simple docstring"""







											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      :	Optional[int])						->       Dict:




											"""simple docstring"""







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




@dataclass
class   a__			:
						snake_case__             = 42
						snake_case__             = 42
						snake_case__             = 0
						snake_case__             = field(default_factory=UpperCamelCase_  )
						snake_case__             = field(default_factory=UpperCamelCase_  )





						def __call__(							self      :	Dict     ,a__      :	Tensor)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  Tracker(self.dest)(a__).parametrized
											_lowerCAmelCase:Optional[Any]									=  Tracker(self.src)(a__).parametrized

											_lowerCAmelCase:Tuple									=  list(filter(lambda a__: type(a__) not in self.src_skip     ,a__))
											_lowerCAmelCase:Dict									=  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 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					ResNetConfig      ,       snake_case			:					Path      ,       snake_case			:					bool = True    ):
					print(F'Converting {name}...'    )
					with torch.no_grad():
										_lowerCAmelCase:str									=  timm.create_model(snake_case      ,       pretrained=snake_case    ).eval()
										_lowerCAmelCase:Optional[Any]									=  ResNetForImageClassification(snake_case    ).eval()
										_lowerCAmelCase:Tuple									=  ModuleTransfer(src=snake_case      ,       dest=snake_case    )
										_lowerCAmelCase:Tuple									=  torch.randn((1, 3, 224, 224)    )
										module_transfer(snake_case    )

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

					_lowerCAmelCase:Optional[Any]									=  F'resnet{"-".join(name.split("resnet"    )    )}'
					print(snake_case    )

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

										# we can use the convnext one
										_lowerCAmelCase:Optional[Any]									=  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=snake_case      ,       )

										print(F'Pushed {checkpoint_name}'    )





def 							UpperCAmelCase		(		snake_case			:					Path      ,       snake_case			:					str = None      ,       snake_case			:					bool = True    ):
					_lowerCAmelCase:Dict									=  '''imagenet-1k-id2label.json'''
					_lowerCAmelCase:Optional[int]									=  1000
					_lowerCAmelCase:Optional[int]									=  (1, num_labels)

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

					_lowerCAmelCase:Tuple									=  idalabel
					_lowerCAmelCase:List[str]									=  {v: k for k, v in idalabel.items()}

					_lowerCAmelCase:Optional[int]									=  partial(snake_case      ,       num_labels=snake_case      ,       idalabel=snake_case      ,       labelaid=snake_case    )

					_lowerCAmelCase:str									=  {
					    '''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(snake_case      ,       names_to_config[model_name]      ,       snake_case      ,       snake_case    )
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(snake_case      ,       snake_case      ,       snake_case      ,       snake_case    )
					return config, expected_shape


if __name__ == "__main__":
				UpperCamelCase__						=			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__						=			parser.parse_args()
				UpperCamelCase__						=			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"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Union[str, Any]     ,a__      :	List[str]=2000     ,a__      :	Tuple=0.1     ,a__      :	Union[str, Any]=20     ,a__      :	int=1E-3)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  None
											_lowerCAmelCase:List[Any]									=  None
											_lowerCAmelCase:Dict									=  None





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Any     ,a__      :	Union[str, torch.device] = None)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.linspace(1     ,self.config.sampling_eps     ,a__     ,device=a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	str     ,a__      :	List[Any]     ,a__      :	Union[str, Any]     ,a__      :	Any=None)						->       Tuple:




											"""simple docstring"""







											if self.timesteps is None:
																raise ValueError(
																    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''')

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											_lowerCAmelCase:Union[str, Any]									=  (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											_lowerCAmelCase:Dict									=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
											_lowerCAmelCase:Optional[Any]									=  std.flatten()
											while len(std.shape) < len(score.shape):
																_lowerCAmelCase:str									=  std.unsqueeze(-1)
											_lowerCAmelCase:Optional[int]									=  -score / std

											# compute
											_lowerCAmelCase:Optional[Any]									=  -1.0 / len(self.timesteps)

											_lowerCAmelCase:Union[str, Any]									=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											_lowerCAmelCase:Any									=  beta_t.flatten()
											while len(beta_t.shape) < len(x.shape):
																_lowerCAmelCase:List[str]									=  beta_t.unsqueeze(-1)
											_lowerCAmelCase:Tuple									=  -0.5 * beta_t * x

											_lowerCAmelCase:str									=  torch.sqrt(a__)
											_lowerCAmelCase:Union[str, Any]									=  drift - diffusion**2 * score
											_lowerCAmelCase:List[str]									=  x + drift * dt

											# add noise
											_lowerCAmelCase:Any									=  randn_tensor(x.shape     ,layout=x.layout     ,generator=a__     ,device=x.device     ,dtype=x.dtype)
											_lowerCAmelCase:Union[str, Any]									=  x_mean + diffusion * math.sqrt(-dt) * noise

											return x, x_mean







						def __len__(							self      :	int)						->       Tuple:




											"""simple docstring"""







											return self.config.num_train_timesteps






