



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__       =			logging.get_logger()





@dataclass
class    __lowerCAmelCase					:
				__lowerCamelCase					=							42
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       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						)
														_lowerCAmelCase         =       m




				def __call__(				self		,							_snake_case						):



									"""simple docstring"""





									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 				snake_case  (				self						):



									"""simple docstring"""





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





class    __lowerCAmelCase					(	nn.Module			):




				def __init__(				self		,							_snake_case						):



									"""simple docstring"""





									super().__init__()

									_lowerCAmelCase         =       []
									# - 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         =       len(_snake_case						) + 1
														feature_blocks.append((F'res{block_index}', v)						)

									_lowerCAmelCase         =       nn.ModuleDict(_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





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





class    __lowerCAmelCase					(	lowerCamelCase__			):




				def 				snake_case  (				self						):



									"""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 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       RegNetConfig(depths=[2, 7, 17, 1]      ,		hidden_sizes=[8, 8, 8, 8]      ,		groups_width=8     )
					if "in1k" in model_name:
										_lowerCAmelCase         =       RegNetForImageClassification(snake_case     )
					else:
										_lowerCAmelCase         =       RegNetModel(snake_case     )
					# create from model (with small weights)
					_lowerCAmelCase         =       FakeRegNetVisslWrapper(
					    RegNet(FakeRegNetParams(depth=27      ,		group_width=10_10      ,		w_a=17_44      ,		w_a=620.83      ,		w_m=2.52     )     )     )

					with torch.no_grad():
										_lowerCAmelCase         =       from_model.eval()
										_lowerCAmelCase         =       our_model.eval()

										_lowerCAmelCase         =       torch.randn((1, 3, 32, 32)     )
										# trace both
										_lowerCAmelCase         =       Tracker(snake_case     )
										_lowerCAmelCase         =       dest_tracker(snake_case     ).parametrized

										pprint(dest_tracker.nameamodule     )
										_lowerCAmelCase         =       Tracker(snake_case     )
										_lowerCAmelCase         =       src_tracker(snake_case     ).parametrized

					# convert the keys -> module dict to keys -> params
					def to_params_dict(snake_case     ):
										_lowerCAmelCase         =       OrderedDict()
										for name, module in dict_with_modules.items():
															for param_name, param in module.state_dict().items():
																				_lowerCAmelCase         =       param
										return params_dict

					_lowerCAmelCase         =       {}

					_lowerCAmelCase         =       to_params_dict(snake_case     )
					_lowerCAmelCase         =       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         =       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         =       """classifier.1.weight"""
										_lowerCAmelCase         =       """classifier.1.bias"""

					return from_to_ours_keys


def 						_UpperCAmelCase							( snake_case      ,		snake_case = None      ,		snake_case = True     ):





					"""simple docstring"""

					_lowerCAmelCase         =       """imagenet-1k-id2label.json"""
					_lowerCAmelCase         =       10_00

					_lowerCAmelCase         =       """huggingface/label-files"""
					_lowerCAmelCase         =       num_labels
					_lowerCAmelCase         =       json.load(open(cached_download(hf_hub_url(snake_case      ,		snake_case      ,		repo_type="""dataset"""     )     )      ,		"""r"""     )     )
					_lowerCAmelCase         =       {int(snake_case     ): v for k, v in idalabel.items()}

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

					_lowerCAmelCase         =       partial(snake_case      ,		num_labels=snake_case      ,		idalabel=snake_case      ,		labelaid=snake_case     )

					_lowerCAmelCase         =       {
					    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]      ,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]      ,		groups_width=10_10     ),
					    # finetuned on imagenet
					    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]      ,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]      ,		groups_width=10_10     ),
					}

					# add seer weights logic
					def load_using_classy_vision(snake_case     ) -> Tuple[Dict, Dict]:
										_lowerCAmelCase         =       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         =       files["""classy_state_dict"""]["""base_model"""]["""model"""]
										return model_state_dict["trunk"], model_state_dict["heads"]

					_lowerCAmelCase         =       {
					    """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         =       get_from_to_our_keys(snake_case     )

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

										_lowerCAmelCase         =       {}

										_lowerCAmelCase         =       list(from_state_dict.keys()     )
										_lowerCAmelCase         =       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         =       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         =       from_to_ours_keys[src_key]
															# store the parameter with our key
															_lowerCAmelCase         =       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         =       names_to_config[model_name]
										_lowerCAmelCase         =       RegNetModel
										if "in1k" in model_name:
															_lowerCAmelCase         =       RegNetForImageClassification
										_lowerCAmelCase         =       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         =       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         =       3_84
										# we can use the convnext one
										_lowerCAmelCase         =       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__       =			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__       =			parser.parse_args()

	A__       =			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)






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

import numpy as np
import torch

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





@dataclass
class    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	42
   UpperCamelCase  	=	42






def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Optional[int]=0.999							,					UpperCAmelCase				:     Tuple="cosine"							,					)    ->		List[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(UpperCAmelCase				:     List[str]     ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(UpperCAmelCase				:     int     ):
      return math.exp(t * -12.0     )

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''     )

  UpperCAmelCase			:				Dict										=					[]
  for i in range(UpperCAmelCase     ):
    UpperCAmelCase			:				Any										=					i / num_diffusion_timesteps
    UpperCAmelCase			:				List[Any]										=					(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(UpperCAmelCase     ) / alpha_bar_fn(UpperCAmelCase     )							,					UpperCAmelCase     )     )
  return torch.tensor(UpperCAmelCase							,					dtype=torch.floataa     )






class    __UpperCAmelCase	(			lowerCamelCase__     ,    lowerCamelCase__		):
   UpperCamelCase  	=	1



   @register_to_config
   def __init__(				self				:	str,						__A				:	int = 1_0_0_0,						__A				:	float = 0.0_0_0_1,						__A				:	float = 0.0_2,						__A				:	str = "linear",						__A				:	float = 0.0,						__A				:	Optional[np.ndarray] = None,						__A				:	bool = True,						):
     if trained_betas is not None:
       UpperCAmelCase			:				int										=					torch.from_numpy(__A		)
     elif beta_schedule == "linear":
       UpperCAmelCase			:				str										=					torch.linspace(__A,						__A,						__A,						dtype=torch.floataa		)
     elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       UpperCAmelCase			:				List[str]										=					(
           torch.linspace(beta_start**0.5,						beta_end**0.5,						__A,						dtype=torch.floataa		) ** 2
       )
     elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       UpperCAmelCase			:				Tuple										=					betas_for_alpha_bar(__A		)
     elif beta_schedule == "sigmoid":
       # GeoDiff sigmoid schedule
       UpperCAmelCase			:				Any										=					torch.linspace(-6,						6,						__A		)
       UpperCAmelCase			:				Tuple										=					torch.sigmoid(__A		) * (beta_end - beta_start) + beta_start
     else:
       raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

     UpperCAmelCase			:				Optional[int]										=					1.0 - self.betas
     UpperCAmelCase			:				int										=					torch.cumprod(self.alphas,						dim=0		)
     UpperCAmelCase			:				int										=					torch.tensor(1.0		)

     UpperCAmelCase			:				Union[str, Any]										=					torch.tensor(1.0		)

     # standard deviation of the initial noise distribution
     UpperCAmelCase			:				str										=					1.0

     # setable values
     UpperCAmelCase			:				int										=					None
     UpperCAmelCase			:				Dict										=					torch.from_numpy(np.arange(0,						__A		)[::-1].copy()		)

     UpperCAmelCase			:				List[str]										=					eta



   def 			__magic_name__       (				self				:	List[str],						__A				:	torch.FloatTensor,						__A				:	Optional[int] = None		):
     return sample



   def 			__magic_name__       (				self				:	List[str],						__A				:	int,						__A				:	int = 1_0,						__A				:	int = 1_0,						__A				:	Union[str, torch.device] = None,						):
     UpperCAmelCase			:				Union[str, Any]										=					min(self.config.num_train_timesteps,						__A		)
     UpperCAmelCase			:				Optional[int]										=					num_inference_steps

     UpperCAmelCase			:				Union[str, Any]										=					[]

     UpperCAmelCase			:				Any										=					{}
     for j in range(0,						num_inference_steps - jump_length,						__A		):
       UpperCAmelCase			:				Tuple										=					jump_n_sample - 1

     UpperCAmelCase			:				str										=					num_inference_steps
     while t >= 1:
       UpperCAmelCase			:				Dict										=					t - 1
       timesteps.append(__A		)

       if jumps.get(__A,						0		) > 0:
         UpperCAmelCase			:				Dict										=					jumps[t] - 1
         for _ in range(__A		):
           UpperCAmelCase			:				Any										=					t + 1
           timesteps.append(__A		)

     UpperCAmelCase			:				Optional[Any]										=					np.array(__A		) * (self.config.num_train_timesteps // self.num_inference_steps)
     UpperCAmelCase			:				Dict										=					torch.from_numpy(__A		).to(__A		)



   def 			__magic_name__       (				self				:	List[Any],						__A				:	Any		):
     UpperCAmelCase			:				int										=					t - self.config.num_train_timesteps // self.num_inference_steps

     UpperCAmelCase			:				Optional[Any]										=					self.alphas_cumprod[t]
     UpperCAmelCase			:				Union[str, Any]										=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     UpperCAmelCase			:				List[Any]										=					1 - alpha_prod_t
     UpperCAmelCase			:				str										=					1 - alpha_prod_t_prev

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from
     # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
     # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
     # variance to pred_sample
     # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
     # without eta.
     # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
     UpperCAmelCase			:				Union[str, Any]										=					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

     return variance



   def 			__magic_name__       (				self				:	Tuple,						__A				:	torch.FloatTensor,						__A				:	int,						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	Optional[torch.Generator] = None,						__A				:	bool = True,						):
     UpperCAmelCase			:				Union[str, Any]										=					timestep
     UpperCAmelCase			:				str										=					timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 1. compute alphas, betas
     UpperCAmelCase			:				List[Any]										=					self.alphas_cumprod[t]
     UpperCAmelCase			:				str										=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     UpperCAmelCase			:				Any										=					1 - alpha_prod_t

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     UpperCAmelCase			:				int										=					(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
       UpperCAmelCase			:				Tuple										=					torch.clamp(__A,						-1,						1		)

     # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
     # substitute formula (7) in the algorithm coming from DDPM paper
     # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
     # DDIM schedule gives the same results as DDPM with eta = 1.0
     # Noise is being reused in 7. and 8., but no impact on quality has
     # been observed.

     # 5. Add noise
     UpperCAmelCase			:				Optional[Any]										=					model_output.device
     UpperCAmelCase			:				Optional[Any]										=					randn_tensor(model_output.shape,						generator=__A,						device=__A,						dtype=model_output.dtype		)
     UpperCAmelCase			:				List[str]										=					self.eta * self._get_variance(__A		) ** 0.5

     UpperCAmelCase			:				int										=					0
     if t > 0 and self.eta > 0:
       UpperCAmelCase			:				List[str]										=					std_dev_t * noise

     # 6. compute "direction pointing to x_t" of formula (12)
     # from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase			:				Any										=					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

     # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase			:				str										=					alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

     # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
     UpperCAmelCase			:				Union[str, Any]										=					(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

     # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
     UpperCAmelCase			:				Optional[Any]										=					mask * prev_known_part + (1.0 - mask) * prev_unknown_part

     if not return_dict:
       return (
           pred_prev_sample,
           pred_original_sample,
       )

     return RePaintSchedulerOutput(prev_sample=__A,						pred_original_sample=__A		)



   def 			__magic_name__       (				self				:	Dict,						__A				:	Dict,						__A				:	Tuple,						__A				:	Tuple=None		):
     UpperCAmelCase			:				Optional[int]										=					self.config.num_train_timesteps // self.num_inference_steps

     for i in range(__A		):
       UpperCAmelCase			:				Optional[Any]										=					self.betas[timestep + i]
       if sample.device.type == "mps":
         # randn does not work reproducibly on mps
         UpperCAmelCase			:				List[str]										=					randn_tensor(sample.shape,						dtype=sample.dtype,						generator=__A		)
         UpperCAmelCase			:				Optional[int]										=					noise.to(sample.device		)
       else:
         UpperCAmelCase			:				Union[str, Any]										=					randn_tensor(sample.shape,						generator=__A,						device=sample.device,						dtype=sample.dtype		)

       # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
       UpperCAmelCase			:				Optional[int]										=					(1 - beta) ** 0.5 * sample + beta**0.5 * noise

     return sample



   def 			__magic_name__       (				self				:	Optional[Any],						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	torch.IntTensor,						):
     raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''		)





   def __len__(				self				:	str		):
     return self.config.num_train_timesteps





