import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__											=					logging.get_logger(__name__)




def    lowerCAmelCase_ (     __A,  __A=False							)     ->				List[str]:







      '''simple docstring'''


      UpperCAmelCase__								=  OrderedDict()
      for key, value in state_dict.items():
            if encoder_only and not key.startswith("head"							):
                  UpperCAmelCase__								=  "segformer.encoder." + key
            if key.startswith("backbone"							):
                  UpperCAmelCase__								=  key.replace("backbone",  "segformer.encoder"							)
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  UpperCAmelCase__								=  key[key.find("patch_embed"							) + len("patch_embed"							)]
                  UpperCAmelCase__								=  key.replace(f"""patch_embed{idx}""",  f"""patch_embeddings.{int(__A							)-1}"""							)
            if "norm" in key:
                  UpperCAmelCase__								=  key.replace("norm",  "layer_norm"							)
            if "segformer.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  UpperCAmelCase__								=  key[key.find("segformer.encoder.layer_norm"							) + len("segformer.encoder.layer_norm"							)]
                  UpperCAmelCase__								=  key.replace(f"""layer_norm{idx}""",  f"""layer_norm.{int(__A							)-1}"""							)
            if "layer_norm1" in key:
                  UpperCAmelCase__								=  key.replace("layer_norm1",  "layer_norm_1"							)
            if "layer_norm2" in key:
                  UpperCAmelCase__								=  key.replace("layer_norm2",  "layer_norm_2"							)
            if "block" in key:
                  # replace for example block1 by block.0
                  UpperCAmelCase__								=  key[key.find("block"							) + len("block"							)]
                  UpperCAmelCase__								=  key.replace(f"""block{idx}""",  f"""block.{int(__A							)-1}"""							)
            if "attn.q" in key:
                  UpperCAmelCase__								=  key.replace("attn.q",  "attention.self.query"							)
            if "attn.proj" in key:
                  UpperCAmelCase__								=  key.replace("attn.proj",  "attention.output.dense"							)
            if "attn" in key:
                  UpperCAmelCase__								=  key.replace("attn",  "attention.self"							)
            if "fc1" in key:
                  UpperCAmelCase__								=  key.replace("fc1",  "dense1"							)
            if "fc2" in key:
                  UpperCAmelCase__								=  key.replace("fc2",  "dense2"							)
            if "linear_pred" in key:
                  UpperCAmelCase__								=  key.replace("linear_pred",  "classifier"							)
            if "linear_fuse" in key:
                  UpperCAmelCase__								=  key.replace("linear_fuse.conv",  "linear_fuse"							)
                  UpperCAmelCase__								=  key.replace("linear_fuse.bn",  "batch_norm"							)
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  UpperCAmelCase__								=  key[key.find("linear_c"							) + len("linear_c"							)]
                  UpperCAmelCase__								=  key.replace(f"""linear_c{idx}""",  f"""linear_c.{int(__A							)-1}"""							)
            if key.startswith("head"							):
                  UpperCAmelCase__								=  key.replace("head",  "classifier"							)
            UpperCAmelCase__								=  value

      return new_state_dict




def    lowerCAmelCase_ (     __A,  __A							)     ->				Any:







      '''simple docstring'''


      for i in range(config.num_encoder_blocks							):
            for j in range(config.depths[i]							):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  UpperCAmelCase__								=  state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""							)
                  UpperCAmelCase__								=  state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias"""							)
                  # next, add keys and values (in that order) to the state dict
                  UpperCAmelCase__								=  kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCAmelCase__								=  kv_bias[: config.hidden_sizes[i]]
                  UpperCAmelCase__								=  kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCAmelCase__								=  kv_bias[
                      config.hidden_sizes[i] :
                  ]




def    lowerCAmelCase_ (     )     ->				List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  "http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase__								=  Image.open(requests.get(__A,  stream=__A							).raw							)

      return image




@torch.no_grad()
def    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  SegformerConfig()
      UpperCAmelCase__								=  False

      # set attributes based on model_name
      UpperCAmelCase__								=  "huggingface/label-files"
      if "segformer" in model_name:
            UpperCAmelCase__								=  model_name[len("segformer."							) : len("segformer."							) + 2]
            if "ade" in model_name:
                  UpperCAmelCase__								=  150
                  UpperCAmelCase__								=  "ade20k-id2label.json"
                  UpperCAmelCase__								=  (1, 150, 128, 128)
            elif "city" in model_name:
                  UpperCAmelCase__								=  19
                  UpperCAmelCase__								=  "cityscapes-id2label.json"
                  UpperCAmelCase__								=  (1, 19, 128, 128)
            else:
                  raise ValueError(f"""Model {model_name} not supported"""							)
      elif "mit" in model_name:
            UpperCAmelCase__								=  True
            UpperCAmelCase__								=  model_name[4:6]
            UpperCAmelCase__								=  1_000
            UpperCAmelCase__								=  "imagenet-1k-id2label.json"
            UpperCAmelCase__								=  (1, 1_000)
      else:
            raise ValueError(f"""Model {model_name} not supported"""							)

      # set config attributes
      UpperCAmelCase__								=  json.load(open(hf_hub_download(__A,  __A,  repo_type="dataset"							),  "r"							)							)
      UpperCAmelCase__								=  {int(__A							): v for k, v in idalabel.items()}
      UpperCAmelCase__								=  idalabel
      UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}
      if size == "b0":
            pass
      elif size == "b1":
            UpperCAmelCase__								=  [64, 128, 320, 512]
            UpperCAmelCase__								=  256
      elif size == "b2":
            UpperCAmelCase__								=  [64, 128, 320, 512]
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  [3, 4, 6, 3]
      elif size == "b3":
            UpperCAmelCase__								=  [64, 128, 320, 512]
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  [3, 4, 18, 3]
      elif size == "b4":
            UpperCAmelCase__								=  [64, 128, 320, 512]
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  [3, 8, 27, 3]
      elif size == "b5":
            UpperCAmelCase__								=  [64, 128, 320, 512]
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  [3, 6, 40, 3]
      else:
            raise ValueError(f"""Size {size} not supported"""							)

      # load image processor (only resize + normalize)
      UpperCAmelCase__								=  SegformerImageProcessor(
          image_scale=(512, 512),  keep_ratio=__A,  align=__A,  do_random_crop=__A							)

      # prepare image
      UpperCAmelCase__								=  prepare_img()
      UpperCAmelCase__								=  image_processor(images=__A,  return_tensors="pt"							).pixel_values

      logger.info(f"""Converting model {model_name}..."""							)

      # load original state dict
      if encoder_only:
            UpperCAmelCase__								=  torch.load(__A,  map_location=torch.device("cpu"							)							)
      else:
            UpperCAmelCase__								=  torch.load(__A,  map_location=torch.device("cpu"							)							)["state_dict"]

      # rename keys
      UpperCAmelCase__								=  rename_keys(__A,  encoder_only=__A							)
      if not encoder_only:
            del state_dict["decode_head.conv_seg.weight"]
            del state_dict["decode_head.conv_seg.bias"]

      # key and value matrices need special treatment
      read_in_k_v(__A,  __A							)

      # create HuggingFace model and load state dict
      if encoder_only:
            UpperCAmelCase__								=  False
            UpperCAmelCase__								=  SegformerForImageClassification(__A							)
      else:
            UpperCAmelCase__								=  SegformerForSemanticSegmentation(__A							)
      model.load_state_dict(__A							)
      model.eval()

      # forward pass
      UpperCAmelCase__								=  model(__A							)
      UpperCAmelCase__								=  outputs.logits

      # set expected_slice based on model name
      # ADE20k checkpoints
      if model_name == "segformer.b0.512x512.ade.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                    [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                    [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
                ]							)
      elif model_name == "segformer.b1.512x512.ade.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                    [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                    [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
                ]							)
      elif model_name == "segformer.b2.512x512.ade.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
                    [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
                    [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
                ]							)
      elif model_name == "segformer.b3.512x512.ade.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                    [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                    [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
                ]							)
      elif model_name == "segformer.b4.512x512.ade.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
                    [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
                    [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
                ]							)
      elif model_name == "segformer.b5.640x640.ade.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
                    [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
                    [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
                ]							)
      # Cityscapes checkpoints
      elif model_name == "segformer.b0.1024x1024.city.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
                    [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
                    [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
                ]							)
      elif model_name == "segformer.b0.512x1024.city.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
                    [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
                    [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
                ]							)
      elif model_name == "segformer.b0.640x1280.city.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [
                        [-1.1_372e01, -1.2_787e01, -1.3_477e01],
                        [-1.2_536e01, -1.4_194e01, -1.4_409e01],
                        [-1.3_217e01, -1.4_888e01, -1.5_327e01],
                    ],
                    [
                        [-1.4_791e01, -1.7_122e01, -1.8_277e01],
                        [-1.7_163e01, -1.9_192e01, -1.9_533e01],
                        [-1.7_897e01, -1.9_991e01, -2.0_315e01],
                    ],
                    [
                        [7.6_723e-01, 4.1_921e-01, -7.7_878e-02],
                        [4.7_772e-01, 9.5_557e-03, -2.8_082e-01],
                        [3.6_032e-01, -2.4_826e-01, -5.1_168e-01],
                    ],
                ]							)
      elif model_name == "segformer.b0.768x768.city.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
                    [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
                    [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
                ]							)
      elif model_name == "segformer.b1.1024x1024.city.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                    [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                    [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
                ]							)
      elif model_name == "segformer.b2.1024x1024.city.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
                    [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
                    [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
                ]							)
      elif model_name == "segformer.b3.1024x1024.city.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
                    [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
                    [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
                ]							)
      elif model_name == "segformer.b4.1024x1024.city.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                    [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                    [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
                ]							)
      elif model_name == "segformer.b5.1024x1024.city.160k":
            UpperCAmelCase__								=  torch.tensor(
                [
                    [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
                    [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
                    [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
                ]							)
      else:
            UpperCAmelCase__								=  logits.argmax(-1							).item()
            print("Predicted class:",  model.config.idalabel[predicted_class_idx]							)

      # verify logits
      if not encoder_only:
            assert logits.shape == expected_shape
            assert torch.allclose(logits[0, :3, :3, :3],  __A,  atol=1e-2							)

      # finally, save model and image processor
      logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""							)
      Path(__A							).mkdir(exist_ok=__A							)
      model.save_pretrained(__A							)
      image_processor.save_pretrained(__A							)


if __name__ == "__main__":
      UpperCamelCase__											=					argparse.ArgumentParser()

      parser.add_argument(
          '--model_name',
          default='segformer.b0.512x512.ade.160k',
          type=str,
          help='Name of the model you\'d like to convert.',
      )
      parser.add_argument(
          '--checkpoint_path', default=None, 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 folder to output PyTorch model.'
      )
      UpperCamelCase__											=					parser.parse_args()
      convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : torch.FloatTensor
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None




def    lowerCAmelCase_ (     __A,  __A=0.999,  __A="cosine",  )     ->				Tuple:







      '''simple docstring'''


      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

      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_     ):


   @register_to_config
   def __init__(self     :						List[str]       ,  __UpperCAmelCase     :						int = 1_0_0_0       ,  __UpperCAmelCase     :						str = "fixed_small_log"       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Optional[float] = 1.0       ,  __UpperCAmelCase     :						str = "epsilon"       ,  __UpperCAmelCase     :						str = "squaredcos_cap_v2"       ,  )						->				Optional[int]:
         """simple docstring"""



         if beta_schedule != "squaredcos_cap_v2":
               raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"		)

         UpperCAmelCase__								=  betas_for_alpha_bar(__UpperCAmelCase		)

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

         # standard deviation of the initial noise distribution
         UpperCAmelCase__								=  1.0

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

         UpperCAmelCase__								=  variance_type


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						Optional[int] = None		)						->				torch.FloatTensor:
         """simple docstring"""



         return sample


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Union[str, torch.device] = None		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  num_inference_steps
         UpperCAmelCase__								=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         UpperCAmelCase__								=  (np.arange(0       ,  __UpperCAmelCase		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
         UpperCAmelCase__								=  torch.from_numpy(__UpperCAmelCase		).to(__UpperCAmelCase		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						Optional[int]=None       ,  __UpperCAmelCase     :						Tuple=None       ,  __UpperCAmelCase     :						List[str]=None		)						->				Tuple:
         """simple docstring"""



         if prev_timestep is None:
               UpperCAmelCase__								=  t - 1

         UpperCAmelCase__								=  self.alphas_cumprod[t]
         UpperCAmelCase__								=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         UpperCAmelCase__								=  1 - alpha_prod_t
         UpperCAmelCase__								=  1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               UpperCAmelCase__								=  self.betas[t]
         else:
               UpperCAmelCase__								=  1 - alpha_prod_t / 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
         UpperCAmelCase__								=  beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
               UpperCAmelCase__								=  self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
               UpperCAmelCase__								=  torch.log(torch.clamp(__UpperCAmelCase       ,  min=1E-20		)		)
               UpperCAmelCase__								=  torch.exp(0.5 * variance		)
         elif variance_type == "learned_range":
               # NOTE difference with DDPM scheduler
               UpperCAmelCase__								=  variance.log()
               UpperCAmelCase__								=  beta.log()

               UpperCAmelCase__								=  (predicted_variance + 1) / 2
               UpperCAmelCase__								=  frac * max_log + (1 - frac) * min_log

         return variance


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						Optional[int] = None       ,  __UpperCAmelCase     :						List[str]=None       ,  __UpperCAmelCase     :						bool = True       ,  )						->				Union[UnCLIPSchedulerOutput, Tuple]:
         """simple docstring"""



         UpperCAmelCase__								=  timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
               UpperCAmelCase__		,      UpperCAmelCase__								=  torch.split(__UpperCAmelCase       ,  sample.shape[1]       ,  dim=1		)
         else:
               UpperCAmelCase__								=  None

         # 1. compute alphas, betas
         if prev_timestep is None:
               UpperCAmelCase__								=  t - 1

         UpperCAmelCase__								=  self.alphas_cumprod[t]
         UpperCAmelCase__								=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         UpperCAmelCase__								=  1 - alpha_prod_t
         UpperCAmelCase__								=  1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               UpperCAmelCase__								=  self.betas[t]
               UpperCAmelCase__								=  self.alphas[t]
         else:
               UpperCAmelCase__								=  1 - alpha_prod_t / alpha_prod_t_prev
               UpperCAmelCase__								=  1 - beta

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
               UpperCAmelCase__								=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
               UpperCAmelCase__								=  model_output
         else:
               raise ValueError(
                   f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
                   " for the UnCLIPScheduler."		)

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
               UpperCAmelCase__								=  torch.clamp(
                   __UpperCAmelCase       ,  -self.config.clip_sample_range       ,  self.config.clip_sample_range		)

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         UpperCAmelCase__								=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         UpperCAmelCase__								=  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         UpperCAmelCase__								=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         UpperCAmelCase__								=  0
         if t > 0:
               UpperCAmelCase__								=  randn_tensor(
                   model_output.shape       ,  dtype=model_output.dtype       ,  generator=__UpperCAmelCase       ,  device=model_output.device		)

               UpperCAmelCase__								=  self._get_variance(
                   __UpperCAmelCase       ,  predicted_variance=__UpperCAmelCase       ,  prev_timestep=__UpperCAmelCase       ,  )

               if self.variance_type == "fixed_small_log":
                     UpperCAmelCase__								=  variance
               elif self.variance_type == "learned_range":
                     UpperCAmelCase__								=  (0.5 * variance).exp()
               else:
                     raise ValueError(
                         f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                         " for the UnCLIPScheduler."		)

               UpperCAmelCase__								=  variance * variance_noise

         UpperCAmelCase__								=  pred_prev_sample + variance

         if not return_dict:
               return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=__UpperCAmelCase       ,  pred_original_sample=__UpperCAmelCase		)





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						torch.IntTensor       ,  )						->				torch.FloatTensor:
         """simple docstring"""



         UpperCAmelCase__								=  self.alphas_cumprod.to(device=original_samples.device       ,  dtype=original_samples.dtype		)
         UpperCAmelCase__								=  timesteps.to(original_samples.device		)

         UpperCAmelCase__								=  alphas_cumprod[timesteps] ** 0.5
         UpperCAmelCase__								=  sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape		) < len(original_samples.shape		):
               UpperCAmelCase__								=  sqrt_alpha_prod.unsqueeze(-1		)

         UpperCAmelCase__								=  (1 - alphas_cumprod[timesteps]) ** 0.5
         UpperCAmelCase__								=  sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape		) < len(original_samples.shape		):
               UpperCAmelCase__								=  sqrt_one_minus_alpha_prod.unsqueeze(-1		)

         UpperCAmelCase__								=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples



