






"""simple docstring"""



from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


__A		:	str      =		logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
class  _UpperCAmelCase	(			_A							):
					SCREAMING_SNAKE_CASE_		:    torch.FloatTensor





class  _UpperCAmelCase	(			_A       ,						_A							):
					SCREAMING_SNAKE_CASE_		:    List[Any]							   =       1




					@register_to_config
					def __init__(    self					:						Tuple   ,						A					:						int = 40   ,						A					:						float = 0.002   ,						A					:						float = 80.0   ,						A					:						float = 0.5   ,						A					:						float = 1.0   ,						A					:						float = 7.0   ,						A					:						bool = True   ,						)  ->  Dict:
							# standard deviation of the initial noise distribution
							lowercase_			:			Optional[int]								=       sigma_max

							lowercase_			:			str								=       np.linspace(0   ,						1   ,						A       )
							lowercase_			:			str								=       self._convert_to_karras(A       )
							lowercase_			:			Any								=       self.sigma_to_t(A       )

							# setable values
							lowercase_			:			str								=       None
							lowercase_			:			Dict								=       torch.from_numpy(A       )
							lowercase_			:			Any								=       torch.from_numpy(A       )
							lowercase_			:			Optional[int]								=       False
							lowercase_			:			str								=       False




					def   A  (    self					:						Dict   ,						A					:						Optional[Any]   ,						A					:						Optional[Any]=None       )  ->  str:
							if schedule_timesteps is None:
									lowercase_			:			int								=       self.timesteps

							lowercase_			:			Any								=       (schedule_timesteps == timestep).nonzero()
							return indices.item()




					def   A  (    self					:						int   ,						A					:						torch.FloatTensor   ,						A					:						Union[float, torch.FloatTensor]       )  ->  torch.FloatTensor:
							# Get sigma corresponding to timestep
							if isinstance(A   ,						torch.Tensor       ):
									lowercase_			:			List[str]								=       timestep.to(self.timesteps.device       )
							lowercase_			:			List[Any]								=       self.index_for_timestep(A       )
							lowercase_			:			List[str]								=       self.sigmas[step_idx]

							lowercase_			:			str								=       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

							lowercase_			:			Dict								=       True
							return sample




					def   A  (    self					:						Optional[Any]   ,						A					:						Union[float, np.ndarray]       )  ->  int:
							if not isinstance(A   ,						np.ndarray       ):
									lowercase_			:			int								=       np.array(A   ,						dtype=np.floataa       )

							lowercase_			:			Tuple								=       10_00 * 0.25 * np.log(sigmas + 1e-44       )

							return timesteps




					def   A  (    self					:						Any   ,						A					:						Optional[int] = None   ,						A					:						Union[str, torch.device] = None   ,						A					:						Optional[List[int]] = None   ,						)  ->  List[Any]:
							if num_inference_steps is None and timesteps is None:
									raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''       )

							if num_inference_steps is not None and timesteps is not None:
									raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''       )

							# Follow DDPMScheduler custom timesteps logic
							if timesteps is not None:
									for i in range(1   ,						len(A       )       ):
											if timesteps[i] >= timesteps[i - 1]:
													raise ValueError('''`timesteps` must be in descending order.'''       )

									if timesteps[0] >= self.config.num_train_timesteps:
											raise ValueError(
											    F'''`timesteps` must start before `self.config.train_timesteps`:'''
											    F''' {self.config.num_train_timesteps}.'''       )

									lowercase_			:			str								=       np.array(A   ,						dtype=np.intaa       )
									lowercase_			:			str								=       True
							else:
									if num_inference_steps > self.config.num_train_timesteps:
											raise ValueError(
											    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
											    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
											    F''' maximal {self.config.num_train_timesteps} timesteps.'''       )

									lowercase_			:			Optional[int]								=       num_inference_steps

									lowercase_			:			Optional[int]								=       self.config.num_train_timesteps // self.num_inference_steps
									lowercase_			:			int								=       (np.arange(0   ,						A       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
									lowercase_			:			List[Any]								=       False

							# Map timesteps to Karras sigmas directly for multistep sampling
							# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
							lowercase_			:			Any								=       self.config.num_train_timesteps
							lowercase_			:			List[Any]								=       timesteps[::-1].copy()
							lowercase_			:			Tuple								=       ramp / (num_train_timesteps - 1)
							lowercase_			:			List[Any]								=       self._convert_to_karras(A       )
							lowercase_			:			Any								=       self.sigma_to_t(A       )

							lowercase_			:			str								=       np.concatenate([sigmas, [self.sigma_min]]       ).astype(np.floataa       )
							lowercase_			:			List[Any]								=       torch.from_numpy(A       ).to(device=A       )

							if str(A       ).startswith('''mps'''       ):
									# mps does not support float64
									lowercase_			:			int								=       torch.from_numpy(A       ).to(A   ,						dtype=torch.floataa       )
							else:
									lowercase_			:			List[Any]								=       torch.from_numpy(A       ).to(device=A       )




					def   A  (    self					:						List[Any]   ,						A					:						Optional[Any]       )  ->  List[Any]:
							lowercase_			:			float								=       self.config.sigma_min
							lowercase_			:			float								=       self.config.sigma_max

							lowercase_			:			Union[str, Any]								=       self.config.rho
							lowercase_			:			Union[str, Any]								=       sigma_min ** (1 / rho)
							lowercase_			:			Union[str, Any]								=       sigma_max ** (1 / rho)
							lowercase_			:			int								=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas




					def   A  (    self					:						Union[str, Any]   ,						A					:						int       )  ->  Union[str, Any]:
							lowercase_			:			Union[str, Any]								=       self.config.sigma_data

							lowercase_			:			Union[str, Any]								=       sigma_data**2 / (sigma**2 + sigma_data**2)
							lowercase_			:			Dict								=       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out




					def   A  (    self					:						int   ,						A					:						Dict       )  ->  int:
							lowercase_			:			int								=       self.config.sigma_min
							lowercase_			:			Optional[int]								=       self.config.sigma_data

							lowercase_			:			List[Any]								=       sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
							lowercase_			:			Optional[int]								=       (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out




					def   A  (    self					:						int   ,						A					:						torch.FloatTensor   ,						A					:						Union[float, torch.FloatTensor]   ,						A					:						torch.FloatTensor   ,						A					:						Optional[torch.Generator] = None   ,						A					:						bool = True   ,						)  ->  Union[CMStochasticIterativeSchedulerOutput, Tuple]:

							if (
							    isinstance(A   ,						A       )
							    or isinstance(A   ,						torch.IntTensor       )
							    or isinstance(A   ,						torch.LongTensor       )
							):
									raise ValueError(
									    (
									        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
									        F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
									        ''' one of the `scheduler.timesteps` as a timestep.'''
									    )   ,						)

							if not self.is_scale_input_called:
									logger.warning(
									    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
									    '''See `StableDiffusionPipeline` for a usage example.'''       )

							if isinstance(A   ,						torch.Tensor       ):
									lowercase_			:			int								=       timestep.to(self.timesteps.device       )

							lowercase_			:			Optional[Any]								=       self.config.sigma_min
							lowercase_			:			int								=       self.config.sigma_max

							lowercase_			:			Dict								=       self.index_for_timestep(A       )

							# sigma_next corresponds to next_t in original implementation
							lowercase_			:			Optional[int]								=       self.sigmas[step_index]
							if step_index + 1 < self.config.num_train_timesteps:
									lowercase_			:			Optional[Any]								=       self.sigmas[step_index + 1]
							else:
									# Set sigma_next to sigma_min
									lowercase_			:			str								=       self.sigmas[-1]

							# Get scalings for boundary conditions
							lowercase_	, lowercase_			:			Optional[int]								=       self.get_scalings_for_boundary_condition(A       )

							# 1. Denoise model output using boundary conditions
							lowercase_			:			Optional[Any]								=       c_out * model_output + c_skip * sample
							if self.config.clip_denoised:
									lowercase_			:			List[Any]								=       denoised.clamp(-1   ,						1       )

							# 2. Sample z ~ N(0, s_noise^2 * I)
							# Noise is not used for onestep sampling.
							if len(self.timesteps       ) > 1:
									lowercase_			:			Dict								=       randn_tensor(
									    model_output.shape   ,						dtype=model_output.dtype   ,						device=model_output.device   ,						generator=A       )
							else:
									lowercase_			:			Optional[int]								=       torch.zeros_like(A       )
							lowercase_			:			str								=       noise * self.config.s_noise

							lowercase_			:			Dict								=       sigma_next.clamp(min=A   ,						max=A       )

							# 3. Return noisy sample
							# tau = sigma_hat, eps = sigma_min
							lowercase_			:			Optional[int]								=       denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

							if not return_dict:
									return (prev_sample,)

							return CMStochasticIterativeSchedulerOutput(prev_sample=A       )




					def   A  (    self					:						List[str]   ,						A					:						torch.FloatTensor   ,						A					:						torch.FloatTensor   ,						A					:						torch.FloatTensor   ,						)  ->  torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							lowercase_			:			Any								=       self.sigmas.to(device=original_samples.device   ,						dtype=original_samples.dtype       )
							if original_samples.device.type == "mps" and torch.is_floating_point(A       ):
									# mps does not support float64
									lowercase_			:			List[Any]								=       self.timesteps.to(original_samples.device   ,						dtype=torch.floataa       )
									lowercase_			:			Tuple								=       timesteps.to(original_samples.device   ,						dtype=torch.floataa       )
							else:
									lowercase_			:			Optional[int]								=       self.timesteps.to(original_samples.device       )
									lowercase_			:			List[Any]								=       timesteps.to(original_samples.device       )

							lowercase_			:			Union[str, Any]								=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

							lowercase_			:			Dict								=       sigmas[step_indices].flatten()
							while len(sigma.shape       ) < len(original_samples.shape       ):
									lowercase_			:			Tuple								=       sigma.unsqueeze(-1       )

							lowercase_			:			Any								=       original_samples + noise * sigma
							return noisy_samples






					def __len__(    self					:						Union[str, Any]       )  ->  Any:
							return self.config.num_train_timesteps











"""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()
__A		:	List[str]      =		logging.get_logger()





@dataclass
class  _UpperCAmelCase	:
					SCREAMING_SNAKE_CASE_		:    nn.Module
					SCREAMING_SNAKE_CASE_		:    List[nn.Module]							   =       field(default_factory=_A							)
					SCREAMING_SNAKE_CASE_		:    list							   =       field(default_factory=_A							)
					SCREAMING_SNAKE_CASE_		:    Dict[str, nn.Module]							   =       field(default_factory=_A							)




					def   A  (    self					:						Tuple   ,						A					:						List[str]   ,						A					:						Tensor   ,						A					:						Tensor   ,						A					:						str       )  ->  Optional[int]:
							lowercase_			:			Optional[int]								=       len(list(m.modules()       )       ) == 1 or isinstance(A   ,						nn.Convad       ) or isinstance(A   ,						nn.BatchNormad       )
							if has_not_submodules:
									self.traced.append(A       )
									lowercase_			:			Tuple								=       m




					def __call__(    self					:						Optional[int]   ,						A					:						Tensor       )  ->  Tuple:
							for name, m in self.module.named_modules():
									self.handles.append(m.register_forward_hook(partial(self._forward_hook   ,						name=A       )       )       )
							self.module(A       )
							[x.remove() for x in self.handles]
							return self






					@property
					def   A  (    self					:						int       )  ->  str:
							# 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  _UpperCAmelCase	(			nn.Module							):




					def __init__(    self					:						Optional[int]   ,						A					:						nn.Module       )  ->  Dict:
							super().__init__()

							lowercase_			:			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}'''
									lowercase_			:			Dict								=       len(A       ) + 1
									feature_blocks.append((F'''res{block_index}''', v)       )

							lowercase_			:			int								=       nn.ModuleDict(A       )






					def   A  (    self					:						Optional[int]   ,						A					:						Tensor       )  ->  List[Any]:
							return get_trunk_forward_outputs(
							    A   ,						out_feat_keys=A   ,						feature_blocks=self._feature_blocks   ,						)





class  _UpperCAmelCase	(			_A							):




					def   A  (    self					:						Tuple       )  ->  Tuple:
							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 						lowercase   (    __snake_case    :			str     ):
		lowercase_			:			Dict								=       RegNetConfig(depths=[2, 7, 1_7, 1]		, hidden_sizes=[8, 8, 8, 8]		, groups_width=8     )
		if "in1k" in model_name:
				lowercase_			:			List[str]								=       RegNetForImageClassification(__snake_case     )
		else:
				lowercase_			:			Union[str, Any]								=       RegNetModel(__snake_case     )
		# create from model (with small weights)
		lowercase_			:			Dict								=       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():
				lowercase_			:			Tuple								=       from_model.eval()
				lowercase_			:			int								=       our_model.eval()

				lowercase_			:			List[Any]								=       torch.randn((1, 3, 3_2, 3_2)     )
				# trace both
				lowercase_			:			Optional[int]								=       Tracker(__snake_case     )
				lowercase_			:			List[Any]								=       dest_tracker(__snake_case     ).parametrized

				pprint(dest_tracker.nameamodule     )
				lowercase_			:			str								=       Tracker(__snake_case     )
				lowercase_			:			str								=       src_tracker(__snake_case     ).parametrized

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

		lowercase_			:			Tuple								=       {}

		lowercase_			:			Optional[Any]								=       to_params_dict(__snake_case     )
		lowercase_			:			Dict								=       to_params_dict(__snake_case     )

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

		return from_to_ours_keys



def 						lowercase   (    __snake_case    :			Path		, __snake_case    :			str = None		, __snake_case    :			bool = True     ):
		lowercase_			:			Optional[Any]								=       '''imagenet-1k-id2label.json'''
		lowercase_			:			str								=       1_0_0_0

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

		lowercase_			:			Union[str, Any]								=       idalabel
		lowercase_			:			Tuple								=       {v: k for k, v in idalabel.items()}

		lowercase_			:			Optional[Any]								=       partial(__snake_case		, num_labels=__snake_case		, idalabel=__snake_case		, labelaid=__snake_case     )

		lowercase_			:			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]:
				lowercase_			:			str								=       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
				lowercase_			:			Any								=       files['''classy_state_dict''']['''base_model''']['''model''']
				return model_state_dict["trunk"], model_state_dict["heads"]

		lowercase_			:			int								=       {
		    '''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'''		, ),
		}

		lowercase_			:			List[Any]								=       get_from_to_our_keys(__snake_case     )

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

				lowercase_			:			List[Any]								=       {}

				lowercase_			:			int								=       list(from_state_dict.keys()     )
				lowercase_			:			int								=       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
						lowercase_			:			List[str]								=       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
						lowercase_			:			Dict								=       from_to_ours_keys[src_key]
						# store the parameter with our key
						lowercase_			:			int								=       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
				lowercase_			:			List[Any]								=       names_to_config[model_name]
				lowercase_			:			Union[str, Any]								=       RegNetModel
				if "in1k" in model_name:
						lowercase_			:			Optional[int]								=       RegNetForImageClassification
				lowercase_			:			Union[str, 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
				lowercase_			:			Tuple								=       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		, )
				lowercase_			:			Union[str, Any]								=       3_8_4
				# we can use the convnext one
				lowercase_			:			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__":
						__A		:	Tuple      =		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.''',
						)

						__A		:	Tuple      =		parser.parse_args()

						__A		:	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)



