






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def       lowercase__	(					A_:						str  ,      A_:						int=0.9_9_9  ,      A_:						List[str]="cosine"  ,      )	->				Tuple:







       """simple docstring"""






       if alpha_transform_type == "cosine":

              def alpha_bar_fn(A_:						Tuple		):
                     return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(A_:						int		):
                     return math.exp(t * -1_2.0		)

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

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





class 				_A			(     UpperCamelCase					,					UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							Optional[int]				= [e.name for e in KarrasDiffusionSchedulers]
    lowerCamelCase	:							Dict				= 2



    @register_to_config
    def __init__(				self      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     int = 1000 ,     __SCREAMING_SNAKE_CASE      :     float = 0.00_085 ,     __SCREAMING_SNAKE_CASE      :     float = 0.012 ,     __SCREAMING_SNAKE_CASE      :     str = "linear" ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[np.ndarray, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     str = "epsilon" ,     __SCREAMING_SNAKE_CASE      :     str = "linspace" ,     __SCREAMING_SNAKE_CASE      :     int = 0 ,     )      ->							Optional[Any]:
           if trained_betas is not None:
                  __UpperCAmelCase     				=torch.tensor(__SCREAMING_SNAKE_CASE ,     dtype=torch.floataa      )
           elif beta_schedule == "linear":
                  __UpperCAmelCase     				=torch.linspace(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     dtype=torch.floataa      )
           elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  __UpperCAmelCase     				=(
                      torch.linspace(beta_start**0.5 ,     beta_end**0.5 ,     __SCREAMING_SNAKE_CASE ,     dtype=torch.floataa      ) ** 2
                  )
           elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  __UpperCAmelCase     				=betas_for_alpha_bar(__SCREAMING_SNAKE_CASE      )
           else:
                  raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''      )

           __UpperCAmelCase     				=1.0 - self.betas
           __UpperCAmelCase     				=torch.cumprod(self.alphas ,     dim=0      )

           #  set all values
           self.set_timesteps(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     str=None      )      ->							Optional[int]:
           if schedule_timesteps is None:
                  __UpperCAmelCase     				=self.timesteps

           __UpperCAmelCase     				=(schedule_timesteps == timestep).nonzero()

           # The sigma index that is taken for the **very** first `step`
           # is always the second index (or the last index if there is only 1)
           # This way we can ensure we don't accidentally skip a sigma in
           # case we start in the middle of the denoising schedule (e.g. for image-to-image)
           if len(self._index_counter      ) == 0:
                  __UpperCAmelCase     				=1 if len(__SCREAMING_SNAKE_CASE      ) > 1 else 0
           else:
                  __UpperCAmelCase     				=timestep.cpu().item() if torch.is_tensor(__SCREAMING_SNAKE_CASE      ) else timestep
                  __UpperCAmelCase     				=self._index_counter[timestep_int]

           return indices[pos].item()



    @property
    def _a							(				self      :     List[str]      )      ->							Any:
           # standard deviation of the initial noise distribution
           if self.config.timestep_spacing in ["linspace", "trailing"]:
                  return self.sigmas.max()

           return (self.sigmas.max() ** 2 + 1) ** 0.5



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     torch.FloatTensor ,     __SCREAMING_SNAKE_CASE      :     Union[float, torch.FloatTensor] ,     )      ->							torch.FloatTensor:
           __UpperCAmelCase     				=self.index_for_timestep(__SCREAMING_SNAKE_CASE      )

           if self.state_in_first_order:
                  __UpperCAmelCase     				=self.sigmas[step_index]
           else:
                  __UpperCAmelCase     				=self.sigmas_interpol[step_index]

           __UpperCAmelCase     				=sample / ((sigma**2 + 1) ** 0.5)
           return sample



    def _a							(				self      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Union[str, torch.device] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[int] = None ,     )      ->							Optional[Any]:
           __UpperCAmelCase     				=num_inference_steps

           __UpperCAmelCase     				=num_train_timesteps or self.config.num_train_timesteps

           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
                  __UpperCAmelCase     				=np.linspace(0 ,     num_train_timesteps - 1 ,     __SCREAMING_SNAKE_CASE ,     dtype=__SCREAMING_SNAKE_CASE      )[::-1].copy()
           elif self.config.timestep_spacing == "leading":
                  __UpperCAmelCase     				=num_train_timesteps // self.num_inference_steps
                  # creates integer timesteps by multiplying by ratio
                  # casting to int to avoid issues when num_inference_step is power of 3
                  __UpperCAmelCase     				=(np.arange(0 ,     __SCREAMING_SNAKE_CASE      ) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE      )
                  timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                  __UpperCAmelCase     				=num_train_timesteps / self.num_inference_steps
                  # creates integer timesteps by multiplying by ratio
                  # casting to int to avoid issues when num_inference_step is power of 3
                  __UpperCAmelCase     				=(np.arange(__SCREAMING_SNAKE_CASE ,     0 ,     -step_ratio      )).round().copy().astype(__SCREAMING_SNAKE_CASE      )
                  timesteps -= 1
           else:
                  raise ValueError(
                      f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''      )

           __UpperCAmelCase     				=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
           __UpperCAmelCase     				=torch.from_numpy(np.log(__SCREAMING_SNAKE_CASE      )      ).to(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=np.interp(__SCREAMING_SNAKE_CASE ,     np.arange(0 ,     len(__SCREAMING_SNAKE_CASE      )      ) ,     __SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
           __UpperCAmelCase     				=torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE      )

           # interpolate sigmas
           __UpperCAmelCase     				=sigmas.log().lerp(sigmas.roll(1      ).log() ,     0.5      ).exp()

           __UpperCAmelCase     				=torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
           __UpperCAmelCase     				=torch.cat(
               [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )

           if str(__SCREAMING_SNAKE_CASE      ).startswith("""mps"""      ):
                  # mps does not support float64
                  __UpperCAmelCase     				=torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE ,     dtype=torch.floataa      )
           else:
                  __UpperCAmelCase     				=torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE      )

           # interpolate timesteps
           __UpperCAmelCase     				=self.sigma_to_t(__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE ,     dtype=timesteps.dtype      )
           __UpperCAmelCase     				=torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,     dim=-1      ).flatten()

           __UpperCAmelCase     				=torch.cat([timesteps[:1], interleaved_timesteps]      )

           __UpperCAmelCase     				=None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           __UpperCAmelCase     				=defaultdict(__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     Optional[int]      )      ->							List[str]:
           # get log sigma
           __UpperCAmelCase     				=sigma.log()

           # get distribution
           __UpperCAmelCase     				=log_sigma - self.log_sigmas[:, None]

           # get sigmas range
           __UpperCAmelCase     				=dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
           __UpperCAmelCase     				=low_idx + 1

           __UpperCAmelCase     				=self.log_sigmas[low_idx]
           __UpperCAmelCase     				=self.log_sigmas[high_idx]

           # interpolate sigmas
           __UpperCAmelCase     				=(low - log_sigma) / (low - high)
           __UpperCAmelCase     				=w.clamp(0 ,     1      )

           # transform interpolation to time range
           __UpperCAmelCase     				=(1 - w) * low_idx + w * high_idx
           __UpperCAmelCase     				=t.view(sigma.shape      )
           return t



    @property
    def _a							(				self      :     str      )      ->							Optional[Any]:
           return self.sample is None



    def _a							(				self      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Union[torch.FloatTensor, np.ndarray] ,     __SCREAMING_SNAKE_CASE      :     Union[float, torch.FloatTensor] ,     __SCREAMING_SNAKE_CASE      :     Union[torch.FloatTensor, np.ndarray] ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     )      ->							Union[SchedulerOutput, Tuple]:
           __UpperCAmelCase     				=self.index_for_timestep(__SCREAMING_SNAKE_CASE      )

           # advance index counter by 1
           __UpperCAmelCase     				=timestep.cpu().item() if torch.is_tensor(__SCREAMING_SNAKE_CASE      ) else timestep
           self._index_counter[timestep_int] += 1

           if self.state_in_first_order:
                  __UpperCAmelCase     				=self.sigmas[step_index]
                  __UpperCAmelCase     				=self.sigmas_interpol[step_index + 1]
                  __UpperCAmelCase     				=self.sigmas[step_index + 1]
           else:
                  # 2nd order / KDPM2's method
                  __UpperCAmelCase     				=self.sigmas[step_index - 1]
                  __UpperCAmelCase     				=self.sigmas_interpol[step_index]
                  __UpperCAmelCase     				=self.sigmas[step_index]

           # currently only gamma=0 is supported. This usually works best anyways.
           # We can support gamma in the future but then need to scale the timestep before
           # passing it to the model which requires a change in API
           __UpperCAmelCase     				=0
           __UpperCAmelCase     				=sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                  __UpperCAmelCase     				=sigma_hat if self.state_in_first_order else sigma_interpol
                  __UpperCAmelCase     				=sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
                  __UpperCAmelCase     				=sigma_hat if self.state_in_first_order else sigma_interpol
                  __UpperCAmelCase     				=model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                      sample / (sigma_input**2 + 1)
                  )
           elif self.config.prediction_type == "sample":
                  raise NotImplementedError("""prediction_type not implemented yet: sample"""      )
           else:
                  raise ValueError(
                      f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''      )

           if self.state_in_first_order:
                  # 2. Convert to an ODE derivative for 1st order
                  __UpperCAmelCase     				=(sample - pred_original_sample) / sigma_hat
                  # 3. delta timestep
                  __UpperCAmelCase     				=sigma_interpol - sigma_hat

                  # store for 2nd order step
                  __UpperCAmelCase     				=sample
           else:
                  # DPM-Solver-2
                  # 2. Convert to an ODE derivative for 2nd order
                  __UpperCAmelCase     				=(sample - pred_original_sample) / sigma_interpol

                  # 3. delta timestep
                  __UpperCAmelCase     				=sigma_next - sigma_hat

                  __UpperCAmelCase     				=self.sample
                  __UpperCAmelCase     				=None

           __UpperCAmelCase     				=sample + derivative * dt

           if not return_dict:
                  return (prev_sample,)

           return SchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     torch.FloatTensor ,     __SCREAMING_SNAKE_CASE      :     torch.FloatTensor ,     __SCREAMING_SNAKE_CASE      :     torch.FloatTensor ,     )      ->							torch.FloatTensor:
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           __UpperCAmelCase     				=self.sigmas.to(device=original_samples.device ,     dtype=original_samples.dtype      )
           if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE      ):
                  # mps does not support float64
                  __UpperCAmelCase     				=self.timesteps.to(original_samples.device ,     dtype=torch.floataa      )
                  __UpperCAmelCase     				=timesteps.to(original_samples.device ,     dtype=torch.floataa      )
           else:
                  __UpperCAmelCase     				=self.timesteps.to(original_samples.device      )
                  __UpperCAmelCase     				=timesteps.to(original_samples.device      )

           __UpperCAmelCase     				=[self.index_for_timestep(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) for t in timesteps]

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

           __UpperCAmelCase     				=original_samples + noise * sigma
           return noisy_samples



    def __len__(				self      :     int      )      ->							List[str]:
           return self.config.num_train_timesteps









"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  List[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       SwinConfig(image_size=192							)

     if "base" in model_name:
          _UpperCAmelCase     :      Tuple														=       6
          _UpperCAmelCase     :      Optional[Any]														=       128
          _UpperCAmelCase     :      Dict														=       (2, 2, 18, 2)
          _UpperCAmelCase     :      List[Any]														=       (4, 8, 16, 32)
     elif "large" in model_name:
          _UpperCAmelCase     :      int														=       12
          _UpperCAmelCase     :      Optional[int]														=       192
          _UpperCAmelCase     :      Optional[Any]														=       (2, 2, 18, 2)
          _UpperCAmelCase     :      str														=       (6, 12, 24, 48)
     else:
          raise ValueError("Model not supported, only supports base and large variants"							)

     _UpperCAmelCase     :      Optional[int]														=       window_size
     _UpperCAmelCase     :      Optional[int]														=       embed_dim
     _UpperCAmelCase     :      List[Any]														=       depths
     _UpperCAmelCase     :      Any														=       num_heads

     return config

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict							)    ->  str:





     '''simple docstring'''

     if "encoder.mask_token" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("encoder.mask_token"		,    "embeddings.mask_token"							)
     if "encoder.patch_embed.proj" in name:
          _UpperCAmelCase     :      Optional[int]														=       name.replace("encoder.patch_embed.proj"		,    "embeddings.patch_embeddings.projection"							)
     if "encoder.patch_embed.norm" in name:
          _UpperCAmelCase     :      Any														=       name.replace("encoder.patch_embed.norm"		,    "embeddings.norm"							)
     if "attn.proj" in name:
          _UpperCAmelCase     :      int														=       name.replace("attn.proj"		,    "attention.output.dense"							)
     if "attn" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("attn"		,    "attention.self"							)
     if "norm1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("norm1"		,    "layernorm_before"							)
     if "norm2" in name:
          _UpperCAmelCase     :      int														=       name.replace("norm2"		,    "layernorm_after"							)
     if "mlp.fc1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("mlp.fc1"		,    "intermediate.dense"							)
     if "mlp.fc2" in name:
          _UpperCAmelCase     :      Any														=       name.replace("mlp.fc2"		,    "output.dense"							)

     if name == "encoder.norm.weight":
          _UpperCAmelCase     :      Any														=       "layernorm.weight"
     if name == "encoder.norm.bias":
          _UpperCAmelCase     :      List[Any]														=       "layernorm.bias"

     if "decoder" in name:
          pass
     else:
          _UpperCAmelCase     :      Dict														=       "swin." + name

     return name

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     for key in orig_state_dict.copy().keys():
          _UpperCAmelCase     :      Dict														=       orig_state_dict.pop(SCREAMING_SNAKE_CASE__							)

          if "attn_mask" in key:
               pass
          elif "qkv" in key:
               _UpperCAmelCase     :      int														=       key.split("."							)
               _UpperCAmelCase     :      List[str]														=       int(key_split[2]							)
               _UpperCAmelCase     :      Dict														=       int(key_split[4]							)
               _UpperCAmelCase     :      str														=       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    _UpperCAmelCase     :      Optional[Any]														=       val[:dim, :]
                    _UpperCAmelCase     :      List[Any]														=       val[
                        dim : dim * 2, :
                    ]
                    _UpperCAmelCase     :      Optional[Any]														=       val[-dim:, :]
               else:
                    _UpperCAmelCase     :      Optional[int]														=       val[
                        :dim
                    ]
                    _UpperCAmelCase     :      Dict														=       val[
                        dim : dim * 2
                    ]
                    _UpperCAmelCase     :      Union[str, Any]														=       val[
                        -dim:
                    ]
          else:
               _UpperCAmelCase     :      List[Any]														=       val

     return orig_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       torch.load(SCREAMING_SNAKE_CASE__		,    map_location="cpu"							)["model"]

     _UpperCAmelCase     :      Union[str, Any]														=       get_swin_config(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE__							)
     model.eval()

     _UpperCAmelCase     :      Optional[Any]														=       convert_state_dict(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
     model.load_state_dict(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       "http://images.cocodataset.org/val2017/000000039769.jpg"

     _UpperCAmelCase     :      List[Any]														=       ViTImageProcessor(size={"height": 192, "width": 192}							)
     _UpperCAmelCase     :      int														=       Image.open(requests.get(SCREAMING_SNAKE_CASE__		,    stream=SCREAMING_SNAKE_CASE__							).raw							)
     _UpperCAmelCase     :      str														=       image_processor(images=SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							)

     with torch.no_grad():
          _UpperCAmelCase     :      List[str]														=       model(**SCREAMING_SNAKE_CASE__							).logits

     print(outputs.keys()							)
     print("Looks ok!"							)

     if pytorch_dump_folder_path is not None:
          print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
          model.save_pretrained(SCREAMING_SNAKE_CASE__							)

          print(f'Saving image processor to {pytorch_dump_folder_path}'							)
          image_processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

     if push_to_hub:
          print(f'Pushing model and image processor for {model_name} to hub'							)
          model.push_to_hub(f'microsoft/{model_name}'							)
          image_processor.push_to_hub(f'microsoft/{model_name}'							)


if __name__ == "__main__":
 _lowerCAmelCase							:   str				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="swin-base-simmim-window6-192",
     type=str,
     choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
     help="Name of the Swin SimMIM model you'd like to convert.",
 )
 parser.add_argument(
     "--checkpoint_path",
     default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
     type=str,
     help="Path to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 _lowerCAmelCase							:   str				=				parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
