
"""simple docstring"""


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 					A_			(  unittest.TestCase     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     (ScoreSdeVeScheduler,)
							SCREAMING_SNAKE_CASE_			   =     ()





							@property
							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  int  =4
										lowerCamelCase__			:  int  =3
										lowerCamelCase__			:  List[Any]  =8
										lowerCamelCase__			:  Tuple  =8

										lowerCamelCase__			:  List[str]  =torch.rand((batch_size, num_channels, height, width) )

										return sample





							@property
							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										lowerCamelCase__			:  str  =4
										lowerCamelCase__			:  str  =3
										lowerCamelCase__			:  List[str]  =8
										lowerCamelCase__			:  Any  =8

										lowerCamelCase__			:  List[Any]  =batch_size * num_channels * height * width
										lowerCamelCase__			:  str  =torch.arange(lowerCamelCase_ )
										lowerCamelCase__			:  Optional[Any]  =sample.reshape(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )
										lowerCamelCase__			:  Optional[Any]  =sample / num_elems
										lowerCamelCase__			:  List[str]  =sample.permute(3  ,							0  ,							1  ,							2 )

										return sample





							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										def model(lowerCamelCase_						:Any  ,							lowerCamelCase_						:Optional[int]  ,							*lowerCamelCase_						:Dict ):
													return sample * t / (t + 1)

										return model





							def    UpperCAmelCase__							(						self						:List[Any]  ,							**lowerCamelCase_						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  ={
										    'num_train_timesteps': 2_000,
										    'snr': 0.15,
										    'sigma_min': 0.01,
										    'sigma_max': 1_348,
										    'sampling_eps': 1e-5,
										}

										config.update(**lowerCamelCase_ )
										return config





							def    UpperCAmelCase__							(						self						:List[str]  ,							lowerCamelCase_						:int=0  ,							**lowerCamelCase_						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  str  =dict(self.forward_default_kwargs )

										for scheduler_class in self.scheduler_classes:
													lowerCamelCase__			:  Any  =self.dummy_sample
													lowerCamelCase__			:  List[str]  =0.1 * sample

													lowerCamelCase__			:  str  =self.get_scheduler_config(**lowerCamelCase_ )
													lowerCamelCase__			:  Union[str, Any]  =scheduler_class(**lowerCamelCase_ )

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase_ )
																lowerCamelCase__			:  Optional[Any]  =scheduler_class.from_pretrained(lowerCamelCase_ )

													lowerCamelCase__			:  List[str]  =scheduler.step_pred(
													    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							generator=torch.manual_seed(0 )  ,							**lowerCamelCase_ ).prev_sample
													lowerCamelCase__			:  List[Any]  =new_scheduler.step_pred(
													    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							generator=torch.manual_seed(0 )  ,							**lowerCamelCase_ ).prev_sample

													assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

													lowerCamelCase__			:  List[str]  =scheduler.step_correct(lowerCamelCase_  ,							lowerCamelCase_  ,							generator=torch.manual_seed(0 )  ,							**lowerCamelCase_ ).prev_sample
													lowerCamelCase__			:  Optional[int]  =new_scheduler.step_correct(
													    lowerCamelCase_  ,							lowerCamelCase_  ,							generator=torch.manual_seed(0 )  ,							**lowerCamelCase_ ).prev_sample

													assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler correction are not identical"





							def    UpperCAmelCase__							(						self						:int  ,							lowerCamelCase_						:str=0  ,							**lowerCamelCase_						:Optional[int] ):





										"""simple docstring"""

										lowerCamelCase__			:  Any  =dict(self.forward_default_kwargs )
										kwargs.update(lowerCamelCase_ )

										for scheduler_class in self.scheduler_classes:
													lowerCamelCase__			:  Union[str, Any]  =self.dummy_sample
													lowerCamelCase__			:  str  =0.1 * sample

													lowerCamelCase__			:  Dict  =self.get_scheduler_config()
													lowerCamelCase__			:  int  =scheduler_class(**lowerCamelCase_ )

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase_ )
																lowerCamelCase__			:  str  =scheduler_class.from_pretrained(lowerCamelCase_ )

													lowerCamelCase__			:  Union[str, Any]  =scheduler.step_pred(
													    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							generator=torch.manual_seed(0 )  ,							**lowerCamelCase_ ).prev_sample
													lowerCamelCase__			:  Union[str, Any]  =new_scheduler.step_pred(
													    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							generator=torch.manual_seed(0 )  ,							**lowerCamelCase_ ).prev_sample

													assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

													lowerCamelCase__			:  Tuple  =scheduler.step_correct(lowerCamelCase_  ,							lowerCamelCase_  ,							generator=torch.manual_seed(0 )  ,							**lowerCamelCase_ ).prev_sample
													lowerCamelCase__			:  Union[str, Any]  =new_scheduler.step_correct(
													    lowerCamelCase_  ,							lowerCamelCase_  ,							generator=torch.manual_seed(0 )  ,							**lowerCamelCase_ ).prev_sample

													assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler correction are not identical"





							def    UpperCAmelCase__							(						self						:str ):





										"""simple docstring"""

										for timesteps in [10, 100, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:str ):





										"""simple docstring"""

										for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]  ,							[1, 100, 1_000] ):
													self.check_over_configs(sigma_min=lowerCamelCase_  ,							sigma_max=lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

										for t in [0.1, 0.5, 0.75]:
													self.check_over_forward(time_step=lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:int ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[Any]  =dict(self.forward_default_kwargs )

										lowerCamelCase__			:  Union[str, Any]  =self.scheduler_classes[0]
										lowerCamelCase__			:  List[Any]  =self.get_scheduler_config()
										lowerCamelCase__			:  Optional[Any]  =scheduler_class(**lowerCamelCase_ )

										lowerCamelCase__			:  str  =3

										lowerCamelCase__			:  List[Any]  =self.dummy_model()
										lowerCamelCase__			:  Tuple  =self.dummy_sample_deter

										scheduler.set_sigmas(lowerCamelCase_ )
										scheduler.set_timesteps(lowerCamelCase_ )
										lowerCamelCase__			:  Union[str, Any]  =torch.manual_seed(0 )

										for i, t in enumerate(scheduler.timesteps ):
													lowerCamelCase__			:  Any  =scheduler.sigmas[i]

													for _ in range(scheduler.config.correct_steps ):
																with torch.no_grad():
																			lowerCamelCase__			:  List[str]  =model(lowerCamelCase_  ,							lowerCamelCase_ )
																lowerCamelCase__			:  Optional[int]  =scheduler.step_correct(lowerCamelCase_  ,							lowerCamelCase_  ,							generator=lowerCamelCase_  ,							**lowerCamelCase_ ).prev_sample

													with torch.no_grad():
																lowerCamelCase__			:  int  =model(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Union[str, Any]  =scheduler.step_pred(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							generator=lowerCamelCase_  ,							**lowerCamelCase_ )
													lowerCamelCase__ ,       lowerCamelCase__			:  Any  =output.prev_sample, output.prev_sample_mean

										lowerCamelCase__			:  Optional[Any]  =torch.sum(torch.abs(lowerCamelCase_ ) )
										lowerCamelCase__			:  Any  =torch.mean(torch.abs(lowerCamelCase_ ) )

										assert np.isclose(result_sum.item()  ,							1_43_72_75_85_28.0 )
										assert np.isclose(result_mean.item()  ,							18_71_45_30.0 )





							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  List[Any]  =dict(self.forward_default_kwargs )

										lowerCamelCase__			:  Any  =kwargs.pop('num_inference_steps'  ,							lowerCamelCase_ )

										for scheduler_class in self.scheduler_classes:
													lowerCamelCase__			:  Tuple  =self.get_scheduler_config()
													lowerCamelCase__			:  int  =scheduler_class(**lowerCamelCase_ )

													lowerCamelCase__			:  List[str]  =self.dummy_sample
													lowerCamelCase__			:  List[str]  =0.1 * sample

													if num_inference_steps is not None and hasattr(lowerCamelCase_  ,							'set_timesteps' ):
																scheduler.set_timesteps(lowerCamelCase_ )
													elif num_inference_steps is not None and not hasattr(lowerCamelCase_  ,							'set_timesteps' ):
																lowerCamelCase__			:  Any  =num_inference_steps

													lowerCamelCase__			:  Any  =scheduler.step_pred(lowerCamelCase_  ,							0  ,							lowerCamelCase_  ,							generator=torch.manual_seed(0 )  ,							**lowerCamelCase_ ).prev_sample
													lowerCamelCase__			:  Any  =scheduler.step_pred(lowerCamelCase_  ,							1  ,							lowerCamelCase_  ,							generator=torch.manual_seed(0 )  ,							**lowerCamelCase_ ).prev_sample

													self.assertEqual(output_a.shape  ,							sample.shape )
													self.assertEqual(output_a.shape  ,							output_a.shape )

"""simple docstring"""


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 					A_			:



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     42
							SCREAMING_SNAKE_CASE_			   =     field(default_factory=A__     )
							SCREAMING_SNAKE_CASE_			   =     field(default_factory=A__     )
							SCREAMING_SNAKE_CASE_			   =     field(default_factory=A__     )





							def    UpperCAmelCase__							(						self						:List[Any]  ,							lowerCamelCase_						:List[str]  ,							lowerCamelCase_						:Tensor  ,							lowerCamelCase_						:Tensor  ,							lowerCamelCase_						:str ):





										"""simple docstring"""

										lowerCamelCase__			:  List[Any]  =len(list(m.modules() ) ) == 1 or isinstance(lowerCamelCase_  ,							nn.Convad ) or isinstance(lowerCamelCase_  ,							nn.BatchNormad )
										if has_not_submodules:
													self.traced.append(lowerCamelCase_ )
													lowerCamelCase__			:  Union[str, Any]  =m





							def __call__(						self						:List[str]  ,							lowerCamelCase_						:Tensor ):





										"""simple docstring"""

										for name, m in self.module.named_modules():
													self.handles.append(m.register_forward_hook(partial(self._forward_hook  ,							name=lowerCamelCase_ ) ) )
										self.module(lowerCamelCase_ )
										[x.remove() for x in self.handles]
										return self





							@property
							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys() ) ) > 0}






class 					A_			(  nn.Module     ):



							"""simple docstring"""






							def __init__(						self						:Dict  ,							lowerCamelCase_						:nn.Module ):





										"""simple docstring"""

										super().__init__()

										lowerCamelCase__			:  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}"""
													lowerCamelCase__			:  Tuple  =len(lowerCamelCase_ ) + 1
													feature_blocks.append((f"""res{block_index}""", v) )

										lowerCamelCase__			:  List[str]  =nn.ModuleDict(lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:List[Any]  ,							lowerCamelCase_						:Tensor ):





										"""simple docstring"""

										return get_trunk_forward_outputs(
										    lowerCamelCase_  ,							out_feat_keys=lowerCamelCase_  ,							feature_blocks=self._feature_blocks  ,							)






class 					A_			(  A__     ):



							"""simple docstring"""






							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										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_     (	snake_case_	:    str    )     ->Dict[str, str]:
			lowerCamelCase__			:  Union[str, Any]  =RegNetConfig(depths=[2, 7, 1_7, 1]							,					hidden_sizes=[8, 8, 8, 8]							,					groups_width=8    )
			if "in1k" in model_name:
						lowerCamelCase__			:  Optional[int]  =RegNetForImageClassification(snake_case_    )
			else:
						lowerCamelCase__			:  Dict  =RegNetModel(snake_case_    )
			# create from model (with small weights)
			lowerCamelCase__			:  Any  =FakeRegNetVisslWrapper(
			    RegNet(FakeRegNetParams(depth=2_7							,					group_width=1_0_1_0							,					w_a=1_7_4_4							,					w_a=620.83							,					w_m=2.52    )    )    )

			with torch.no_grad():
						lowerCamelCase__			:  str  =from_model.eval()
						lowerCamelCase__			:  int  =our_model.eval()

						lowerCamelCase__			:  str  =torch.randn((1, 3, 3_2, 3_2)    )
						# trace both
						lowerCamelCase__			:  Optional[int]  =Tracker(snake_case_    )
						lowerCamelCase__			:  Any  =dest_tracker(snake_case_    ).parametrized

						pprint(dest_tracker.nameamodule    )
						lowerCamelCase__			:  Optional[int]  =Tracker(snake_case_    )
						lowerCamelCase__			:  Optional[Any]  =src_tracker(snake_case_    ).parametrized

			# convert the keys -> module dict to keys -> params
			def to_params_dict(snake_case_	:    List[str]    ):
						lowerCamelCase__			:  List[str]  =OrderedDict()
						for name, module in dict_with_modules.items():
									for param_name, param in module.state_dict().items():
												lowerCamelCase__			:  Dict  =param
						return params_dict

			lowerCamelCase__			:  int  ={}

			lowerCamelCase__			:  Any  =to_params_dict(snake_case_    )
			lowerCamelCase__			:  List[Any]  =to_params_dict(snake_case_    )

			for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()							,					dst_state_dict.items()    ):
						lowerCamelCase__			:  Optional[int]  =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:
						lowerCamelCase__			:  List[str]  ='classifier.1.weight'
						lowerCamelCase__			:  Dict  ='classifier.1.bias'

			return from_to_ours_keys






def      lowerCAmelCase_     (	snake_case_	:    Path							,					snake_case_	:    str = None							,					snake_case_	:    bool = True    )     ->Any:
			lowerCamelCase__			:  str  ='imagenet-1k-id2label.json'
			lowerCamelCase__			:  Optional[int]  =1_0_0_0

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

			lowerCamelCase__			:  Any  =idalabel
			lowerCamelCase__			:  Tuple  ={v: k for k, v in idalabel.items()}

			lowerCamelCase__			:  List[str]  =partial(snake_case_							,					num_labels=snake_case_							,					idalabel=snake_case_							,					labelaid=snake_case_    )

			lowerCamelCase__			:  Optional[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(snake_case_	:    str    ) -> Tuple[Dict, Dict]:
						lowerCamelCase__			:  Tuple  =torch.hub.load_state_dict_from_url(snake_case_							,					model_dir=str(snake_case_    )							,					map_location='cpu'    )
						# check if we have a head, if yes add it
						lowerCamelCase__			:  Optional[Any]  =files['classy_state_dict']['base_model']['model']
						return model_state_dict["trunk"], model_state_dict["heads"]

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

			lowerCamelCase__			:  Dict  =get_from_to_our_keys(snake_case_    )

			if not (save_directory / f"""{model_name}.pth""").exists():
						logger.info('Loading original state_dict.'    )
						lowerCamelCase__ ,       lowerCamelCase__			:  Optional[int]  =names_to_from_model[model_name]()
						lowerCamelCase__			:  Optional[int]  =from_state_dict_trunk
						if "in1k" in model_name:
									# add the head
									lowerCamelCase__			:  Tuple  ={**from_state_dict_trunk, **from_state_dict_head}
						logger.info('Done!'    )

						lowerCamelCase__			:  Any  ={}

						lowerCamelCase__			:  List[str]  =list(from_state_dict.keys()    )
						lowerCamelCase__			:  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
									lowerCamelCase__			:  Any  =re.sub(snake_case_							,					''							,					snake_case_    )
									# 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
									lowerCamelCase__			:  Union[str, Any]  =from_to_ours_keys[src_key]
									# store the parameter with our key
									lowerCamelCase__			:  Tuple  =from_state_dict[key]
									not_used_keys.remove(snake_case_    )
						# check that all keys have been updated
						assert len(snake_case_    ) == 0, f"""Some keys where not used {','.join(snake_case_    )}"""

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

						# save our state dict to disk
						torch.save(snake_case_							,					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
						lowerCamelCase__			:  Optional[int]  =names_to_config[model_name]
						lowerCamelCase__			:  int  =RegNetModel
						if "in1k" in model_name:
									lowerCamelCase__			:  List[Any]  =RegNetForImageClassification
						lowerCamelCase__			:  Any  =our_model_func(snake_case_    )
						# 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
						lowerCamelCase__			:  Optional[int]  =our_model.state_dict().keys()
						PreTrainedModel._load_pretrained_model_low_mem(
						    snake_case_							,					snake_case_							,					[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							,					)
						lowerCamelCase__			:  Optional[Any]  =3_8_4
						# we can use the convnext one
						lowerCamelCase__			:  Optional[int]  =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'							,					size=snake_case_    )
						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)