



"""simple docstring"""
import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__a           =							logging.get_logger(__name__)
@dataclass
class 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     nn.Module
					_A						:     List[nn.Module]		=	field(default_factory=_lowerCAmelCase							)
					_A						:     list		=	field(default_factory=_lowerCAmelCase							)






					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Union[str, Any]		,	snake_case:				Tensor		,	snake_case:				Tensor		)  ->      List[str]:
											snake_case_					:Optional[int]		=     len(list(m.modules()		)		) == 1 or isinstance(snake_case		,	nn.Convad		) or isinstance(snake_case		,	nn.BatchNormad		)
											if has_not_submodules:
																	if not isinstance(snake_case		,	snake_case		):
																							self.traced.append(snake_case		)






					def __call__(			self:				Optional[Any]		,	snake_case:				Tensor		)  ->      Optional[Any]:
											for m in self.module.modules():
																	self.handles.append(m.register_forward_hook(self._forward_hook		)		)
											self.module(snake_case		)
											[x.remove() for x in self.handles]
											return self






					@property
					def   lowerCAmelCase_							(			self:				str		)  ->      int:
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case		: len(list(x.state_dict().keys()		)		) > 0		,	self.traced		)		)

@dataclass
class 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     nn.Module
					_A						:     nn.Module
					_A						:     int		=	0
					_A						:     List		=	field(default_factory=_lowerCAmelCase							)
					_A						:     List		=	field(default_factory=_lowerCAmelCase							)






					def __call__(			self:				List[Any]		,	snake_case:				Tensor		)  ->      List[Any]:
											snake_case_					:Optional[int]		=     Tracker(self.dest		)(snake_case		).parametrized
											snake_case_					:int		=     Tracker(self.src		)(snake_case		).parametrized

											snake_case_					:Optional[Any]		=     list(filter(lambda snake_case		: type(snake_case		) not in self.src_skip		,	snake_case		)		)
											snake_case_					:Any		=     list(filter(lambda snake_case		: type(snake_case		) not in self.dest_skip		,	snake_case		)		)

											if len(snake_case		) != len(snake_case		):
																	raise Exception(
																	    f"""Numbers of operations are different. Source module has {len(snake_case		)} operations while"""
																	    f""" destination module has {len(snake_case		)}."""		)

											for dest_m, src_m in zip(snake_case		,	snake_case		):
																	dest_m.load_state_dict(src_m.state_dict()		)
																	if self.verbose == 1:
																							print(f"""Transfered from={src_m} to={dest_m}"""		)

def   A_    (     _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Any		=     from_model.state_dict()
						snake_case_					:Optional[Any]		=     our_model.state_dict()
						snake_case_					:Dict		=     our_model.config
						snake_case_					:List[str]		=     []
						for stage_idx in range(len(config.hidden_sizes      )      ):
												for block_id in range(config.depths[stage_idx]      ):
																		snake_case_					:List[Any]		=     f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
																		snake_case_					:Dict		=     f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

																		all_keys.append((from_key, to_key)      )
																		snake_case_					:Optional[int]		=     f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		snake_case_					:Tuple		=     f"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

																		all_keys.append((from_key, to_key)      )

						for from_key, to_key in all_keys:
												snake_case_					:List[str]		=     from_state_dict.pop(_lowercase      )

						our_model.load_state_dict(_lowercase      )
						return our_model
def   A_    (     _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase = True,   ):







						'''simple docstring'''


						print(f"""Downloading weights for {name}..."""      )
						snake_case_					:Union[str, Any]		=     cached_download(_lowercase      )
						print(f"""Converting {name}..."""      )
						snake_case_					:Union[str, Any]		=     torch.load(_lowercase      )["""state_dict"""]
						from_model.load_state_dict(_lowercase      )
						from_model.eval()
						with torch.no_grad():
												snake_case_					:List[str]		=     VanForImageClassification(_lowercase      ).eval()
												snake_case_					:Tuple		=     ModuleTransfer(src=_lowercase,   dest=_lowercase      )
												snake_case_					:int		=     torch.randn((1, 3, 224, 224)      )
												module_transfer(_lowercase      )
												snake_case_					:int		=     copy_parameters(_lowercase,   _lowercase      )

						if not torch.allclose(from_model(_lowercase      ),   our_model(_lowercase      ).logits      ):
												raise ValueError("""The model logits don't match the original one."""      )

						snake_case_					:List[str]		=     name
						print(_lowercase      )

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

												# we can use the convnext one
												snake_case_					:str		=     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=_lowercase,   )

												print(f"""Pushed {checkpoint_name}"""      )
def   A_    (     _lowercase,   _lowercase = None,   _lowercase = True      ):







						'''simple docstring'''


						snake_case_					:str		=     """imagenet-1k-id2label.json"""
						snake_case_					:int		=     1000

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

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

						snake_case_					:str		=     partial(_lowercase,   num_labels=_lowercase,   idalabel=_lowercase,   labelaid=_lowercase      )

						snake_case_					:Dict		=     {
						    """van-tiny""": ImageNetPreTrainedConfig(
						        hidden_sizes=[32, 64, 160, 256],   depths=[3, 3, 5, 2],   mlp_ratios=[8, 8, 4, 4],   ),
						    """van-small""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512],   depths=[2, 2, 4, 2],   mlp_ratios=[8, 8, 4, 4],   ),
						    """van-base""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512],   depths=[3, 3, 12, 3],   mlp_ratios=[8, 8, 4, 4],   ),
						    """van-large""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512],   depths=[3, 5, 27, 3],   mlp_ratios=[8, 8, 4, 4],   ),
						}

						snake_case_					:Optional[int]		=     {
						    """van-tiny""": van_tiny,
						    """van-small""": van_small,
						    """van-base""": van_base,
						    """van-large""": van_large,
						}

						snake_case_					:str		=     {
						    """van-tiny""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
						    ),
						    """van-small""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
						    ),
						    """van-base""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
						    ),
						    """van-large""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
						    ),
						}

						if model_name:
												convert_weight_and_push(
												    _lowercase,   names_to_config[model_name],   checkpoint=names_to_original_checkpoints[model_name],   from_model=names_to_original_models[model_name](),   save_directory=_lowercase,   push_to_hub=_lowercase,   )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    _lowercase,   _lowercase,   checkpoint=names_to_original_checkpoints[model_name],   from_model=names_to_original_models[model_name](),   save_directory=_lowercase,   push_to_hub=_lowercase,   )


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 resnet* architecture,"
						        " currently: van-tiny/small/base/large. 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(
						    "--van_dir",
						    required=True,
						    type=Path,
						    help=(
						        "A path to VAN's original implementation directory. You can download from here:"
						        " https://github.com/Visual-Attention-Network/VAN-Classification"
						    ),
						)
						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)
						__a           =							args.van_dir
						# append the path to the parents to maskformer dir
						sys.path.append(str(van_dir.parent))
						from van.models.van import van_base, van_large, van_small, van_tiny

						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







'''simple docstring'''

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      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	42
					SCREAMING_SNAKE_CASE							=	42






def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=0.999,       _UpperCAmelCase="cosine",       ):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(_UpperCAmelCase  ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

		elif alpha_transform_type == "exp":

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

		else:
				raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"  )

		__UpperCAmelCase	:							Union[str, Any]     =     []
		for i in range(_UpperCAmelCase  ):
				__UpperCAmelCase	:							int     =     i / num_diffusion_timesteps
				__UpperCAmelCase	:							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      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	1
					@register_to_config
					def __init__(   self       :      Dict		,	UpperCAmelCase_       :      int = 1_000		,	UpperCAmelCase_       :      float = 0.0001		,	UpperCAmelCase_       :      float = 0.02		,	UpperCAmelCase_       :      str = "linear"		,	UpperCAmelCase_       :      float = 0.0		,	UpperCAmelCase_       :      Optional[np.ndarray] = None		,	UpperCAmelCase_       :      bool = True		,	):







							"""simple docstring"""
							if trained_betas is not None:
									__UpperCAmelCase	:							Tuple     =     torch.from_numpy(UpperCAmelCase_		)
							elif beta_schedule == "linear":
									__UpperCAmelCase	:							Dict     =     torch.linspace(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	dtype=torch.floataa		)
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									__UpperCAmelCase	:							int     =     (
									    torch.linspace(beta_start**0.5		,	beta_end**0.5		,	UpperCAmelCase_		,	dtype=torch.floataa		) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__UpperCAmelCase	:							str     =     betas_for_alpha_bar(UpperCAmelCase_		)
							elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									__UpperCAmelCase	:							Optional[Any]     =     torch.linspace(-6		,	6		,	UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[int]     =     torch.sigmoid(UpperCAmelCase_		) * (beta_end - beta_start) + beta_start
							else:
									raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"		)

							__UpperCAmelCase	:							List[str]     =     1.0 - self.betas
							__UpperCAmelCase	:							List[str]     =     torch.cumprod(self.alphas		,	dim=0		)
							__UpperCAmelCase	:							List[Any]     =     torch.tensor(1.0		)

							__UpperCAmelCase	:							str     =     torch.tensor(1.0		)

							# standard deviation of the initial noise distribution
							__UpperCAmelCase	:							Optional[Any]     =     1.0

							# setable values
							__UpperCAmelCase	:							Optional[Any]     =     None
							__UpperCAmelCase	:							Optional[Any]     =     torch.from_numpy(np.arange(0		,	UpperCAmelCase_		)[::-1].copy()		)

							__UpperCAmelCase	:							Tuple     =     eta
					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      Optional[int] = None		):







							"""simple docstring"""
							return sample
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      int = 10		,	UpperCAmelCase_       :      int = 10		,	UpperCAmelCase_       :      Union[str, torch.device] = None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     min(self.config.num_train_timesteps		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							List[Any]     =     num_inference_steps

							__UpperCAmelCase	:							Dict     =     []

							__UpperCAmelCase	:							int     =     {}
							for j in range(0		,	num_inference_steps - jump_length		,	UpperCAmelCase_		):
									__UpperCAmelCase	:							int     =     jump_n_sample - 1

							__UpperCAmelCase	:							Optional[int]     =     num_inference_steps
							while t >= 1:
									__UpperCAmelCase	:							int     =     t - 1
									timesteps.append(UpperCAmelCase_		)

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

							__UpperCAmelCase	:							Optional[Any]     =     np.array(UpperCAmelCase_		) * (self.config.num_train_timesteps // self.num_inference_steps)
							__UpperCAmelCase	:							str     =     torch.from_numpy(UpperCAmelCase_		).to(UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     t - self.config.num_train_timesteps // self.num_inference_steps

							__UpperCAmelCase	:							str     =     self.alphas_cumprod[t]
							__UpperCAmelCase	:							int     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							__UpperCAmelCase	:							Dict     =     1 - alpha_prod_t
							__UpperCAmelCase	:							Optional[Any]     =     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	:							Dict     =     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance
					def 		lowerCamelCase_   (   self       :      Any		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      Optional[torch.Generator] = None		,	UpperCAmelCase_       :      bool = True		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     timestep
							__UpperCAmelCase	:							Optional[Any]     =     timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							__UpperCAmelCase	:							Any     =     self.alphas_cumprod[t]
							__UpperCAmelCase	:							Optional[int]     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							__UpperCAmelCase	:							Optional[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	:							List[Any]     =     (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
									__UpperCAmelCase	:							int     =     torch.clamp(UpperCAmelCase_		,	-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[int]     =     randn_tensor(model_output.shape		,	generator=UpperCAmelCase_		,	device=UpperCAmelCase_		,	dtype=model_output.dtype		)
							__UpperCAmelCase	:							List[Any]     =     self.eta * self._get_variance(UpperCAmelCase_		) ** 0.5

							__UpperCAmelCase	:							str     =     0
							if t > 0 and self.eta > 0:
									__UpperCAmelCase	:							Union[str, Any]     =     std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCAmelCase	:							int     =     (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	:							Any     =     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	:							str     =     (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	:							Union[str, 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=UpperCAmelCase_		,	pred_original_sample=UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[Any]		,	UpperCAmelCase_       :      Any		,	UpperCAmelCase_       :      List[str]=None		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     self.config.num_train_timesteps // self.num_inference_steps

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

									# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
									__UpperCAmelCase	:							Any     =     (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.IntTensor		,	):







							"""simple docstring"""
							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."		)






					def __len__(   self       :      List[str]		):







							"""simple docstring"""
							return self.config.num_train_timesteps




