


import argparse
import json
from pathlib import Path

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

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case       :					Dict        =  logging.get_logger(__name__)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	Optional[Any]=False				,						UpperCAmelCase_		:	List[str]=False		):




      """simple docstring"""





      a		:int											= '''backbone.''' if is_semantic else ''''''

      a		:Optional[Any]											= []
      for i in range(config.num_hidden_layers		):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')		)
            rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')		)
            rename_keys.append(
                (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')		)
            rename_keys.append(
                (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')		)
            rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')		)
            rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')		)
            rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')		)
            rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')		)
            rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')		)
            rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              (F'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
              (F'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
              (F'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
          ]		)

      if has_lm_head:
            # mask token + shared relative position bias + layernorm
            rename_keys.extend(
                [
                    ('''mask_token''', '''beit.embeddings.mask_token'''),
                    (
                        '''rel_pos_bias.relative_position_bias_table''',
                        '''beit.encoder.relative_position_bias.relative_position_bias_table''',
                    ),
                    (
                        '''rel_pos_bias.relative_position_index''',
                        '''beit.encoder.relative_position_bias.relative_position_index''',
                    ),
                    ('''norm.weight''', '''layernorm.weight'''),
                    ('''norm.bias''', '''layernorm.bias'''),
                ]		)
      elif is_semantic:
            # semantic segmentation classification heads
            rename_keys.extend(
                [
                    ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
                    ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
                    ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
                    ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
                ]		)
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                    ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                    ('''head.weight''', '''classifier.weight'''),
                    ('''head.bias''', '''classifier.bias'''),
                ]		)

      return rename_keys
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[Any]				,						UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	Any=False				,						UpperCAmelCase_		:	int=False		):




      """simple docstring"""





      for i in range(config.num_hidden_layers		):
            a		:str											= '''backbone.''' if is_semantic else ''''''
            # queries, keys and values
            a		:List[str]											= state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
            a		:Union[str, Any]											= state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
            a		:List[Any]											= state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

            a		:List[str]											= in_proj_weight[
                : config.hidden_size, :
            ]
            a		:str											= q_bias
            a		:Any											= in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            a		:Optional[Any]											= in_proj_weight[
                -config.hidden_size :, :
            ]
            a		:Any											= v_bias

            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            a		:Any											= state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
            a		:Union[str, Any]											= state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

            a		:Optional[int]											= gamma_a
            a		:Optional[Any]											= gamma_a

            # relative_position bias table + index
            if not has_lm_head:
                  # each layer has its own relative position bias
                  a		:Tuple											= state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''		)
                  a		:Union[str, Any]											= state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''		)

                  a		:str											= table
                  a		:Dict											= index
def        __lowerCamelCase		(				UpperCAmelCase_		:	Tuple				,						UpperCAmelCase_		:	List[Any]				,						UpperCAmelCase_		:	List[Any]		):




      """simple docstring"""





      a		:str											= dct.pop(UpperCAmelCase_		)
      a		:str											= val
def        __lowerCamelCase		(				):




      """simple docstring"""





      a		:int											= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      a		:Optional[Any]											= Image.open(requests.get(UpperCAmelCase_				,						stream=UpperCAmelCase_		).raw		)
      return im
@torch.no_grad()
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Union[str, Any]		):




      """simple docstring"""





      a		:Optional[int]											= BeitConfig()
      a		:Optional[Any]											= False
      a		:Optional[int]											= False
      a		:Union[str, Any]											= '''huggingface/label-files'''
      # set config parameters based on URL
      if checkpoint_url[-9:-4] == "pt22k":
            # masked image modeling
            a		:Tuple											= True
            a		:str											= True
            a		:Optional[Any]											= True
      elif checkpoint_url[-9:-4] == "ft22k":
            # intermediate fine-tuning on ImageNet-22k
            a		:str											= True
            a		:Dict											= 2_1841
            a		:int											= '''imagenet-22k-id2label.json'''
            a		:List[str]											= json.load(open(hf_hub_download(UpperCAmelCase_				,						UpperCAmelCase_				,						repo_type='''dataset'''		)				,						'''r'''		)		)
            a		:List[Any]											= {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
            # this dataset contains 21843 labels but the model only has 21841
            # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
            del idalabel[9205]
            del idalabel[1_5027]
            a		:Optional[int]											= idalabel
            a		:Optional[Any]											= {v: k for k, v in idalabel.items()}
      elif checkpoint_url[-8:-4] == "to1k":
            # fine-tuning on ImageNet-1k
            a		:Any											= True
            a		:Union[str, Any]											= 1000
            a		:int											= '''imagenet-1k-id2label.json'''
            a		:List[str]											= json.load(open(hf_hub_download(UpperCAmelCase_				,						UpperCAmelCase_				,						repo_type='''dataset'''		)				,						'''r'''		)		)
            a		:str											= {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
            a		:str											= idalabel
            a		:Dict											= {v: k for k, v in idalabel.items()}
            if "384" in checkpoint_url:
                  a		:Tuple											= 384
            if "512" in checkpoint_url:
                  a		:Dict											= 512
      elif "ade20k" in checkpoint_url:
            # fine-tuning
            a		:int											= True
            a		:str											= 150
            a		:List[Any]											= '''ade20k-id2label.json'''
            a		:Optional[int]											= json.load(open(hf_hub_download(UpperCAmelCase_				,						UpperCAmelCase_				,						repo_type='''dataset'''		)				,						'''r'''		)		)
            a		:Tuple											= {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
            a		:str											= idalabel
            a		:int											= {v: k for k, v in idalabel.items()}
            a		:List[Any]											= 640
            a		:str											= True
      else:
            raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''''		)

      # size of the architecture
      if "base" in checkpoint_url:
            pass
      elif "large" in checkpoint_url:
            a		:Any											= 1024
            a		:Any											= 4096
            a		:str											= 24
            a		:str											= 16
            if "ade20k" in checkpoint_url:
                  a		:Tuple											= 640
                  a		:List[Any]											= [7, 11, 15, 23]
      else:
            raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''		)

      # load state_dict of original model, remove and rename some keys
      a		:Any											= torch.hub.load_state_dict_from_url(UpperCAmelCase_				,						map_location='''cpu'''				,						check_hash=UpperCAmelCase_		)
      a		:List[str]											= state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

      a		:str											= create_rename_keys(UpperCAmelCase_				,						has_lm_head=UpperCAmelCase_				,						is_semantic=UpperCAmelCase_		)
      for src, dest in rename_keys:
            rename_key(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)
      read_in_q_k_v(UpperCAmelCase_				,						UpperCAmelCase_				,						has_lm_head=UpperCAmelCase_				,						is_semantic=UpperCAmelCase_		)
      if is_semantic:
            # add prefix to decoder keys
            for key, val in state_dict.copy().items():
                  a		:Tuple											= state_dict.pop(UpperCAmelCase_		)
                  if key.startswith('''backbone.fpn'''		):
                        a		:Optional[int]											= key.replace('''backbone.fpn'''				,						'''fpn'''		)
                  a		:Any											= val

    # load HuggingFace model
      if checkpoint_url[-9:-4] == "pt22k":
            a		:List[str]											= BeitForMaskedImageModeling(UpperCAmelCase_		)
      elif "ade20k" in checkpoint_url:
            a		:Optional[Any]											= BeitForSemanticSegmentation(UpperCAmelCase_		)
      else:
            a		:List[str]											= BeitForImageClassification(UpperCAmelCase_		)
      model.eval()
      model.load_state_dict(UpperCAmelCase_		)

      # Check outputs on an image
      if is_semantic:
            a		:Any											= BeitImageProcessor(size=config.image_size				,						do_center_crop=UpperCAmelCase_		)
            a		:Optional[Any]											= load_dataset('''hf-internal-testing/fixtures_ade20k'''				,						split='''test'''		)
            a		:List[Any]											= Image.open(ds[0]['''file''']		)
      else:
            a		:Tuple											= BeitImageProcessor(
                size=config.image_size				,						resample=PILImageResampling.BILINEAR				,						do_center_crop=UpperCAmelCase_		)
            a		:str											= prepare_img()

      a		:List[str]											= image_processor(images=UpperCAmelCase_				,						return_tensors='''pt'''		)
      a		:Any											= encoding['''pixel_values''']

      a		:Optional[int]											= model(UpperCAmelCase_		)
      a		:List[Any]											= outputs.logits

      # verify logits
      a		:List[str]											= torch.Size([1, 1000]		)
      if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''		):
            a		:List[str]											= torch.Size([1, 196, 8192]		)
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''		):
            a		:int											= torch.Size([1, 196, 8192]		)
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''		):
            a		:str											= torch.Size([1, 2_1841]		)
            a		:Tuple											= torch.tensor([2.2288, 2.4671, 0.7395]		)
            a		:Union[str, Any]											= 2397
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''		):
            a		:str											= torch.Size([1, 2_1841]		)
            a		:Union[str, Any]											= torch.tensor([1.6881, -0.2787, 0.5901]		)
            a		:Any											= 2396
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''		):
            a		:Union[str, Any]											= torch.tensor([0.1241, 0.0798, -0.6569]		)
            a		:str											= 285
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''		):
            a		:str											= torch.tensor([-1.2385, -1.0987, -1.0108]		)
            a		:Union[str, Any]											= 281
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''		):
            a		:List[str]											= torch.tensor([-1.5303, -0.9484, -0.3147]		)
            a		:str											= 761
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''		):
            a		:Tuple											= torch.tensor([0.4610, -0.0928, 0.2086]		)
            a		:Optional[Any]											= 761
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''		):
            a		:List[str]											= torch.tensor([-0.4804, 0.6257, -0.1837]		)
            a		:Optional[int]											= 761
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''		):
            a		:List[str]											= torch.tensor([[-0.5122, 0.5117, -0.2113]]		)
            a		:List[str]											= 761
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''		):
            a		:Dict											= torch.tensor([-0.3062, 0.7261, 0.4852]		)
            a		:int											= 761
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''		):
            a		:Union[str, Any]											= (1, 150, 160, 160)
            a		:int											= torch.tensor(
                [
                    [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                    [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                    [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
                ]		)
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''		):
            a		:Dict											= (1, 150, 160, 160)
            a		:int											= torch.tensor(
                [
                    [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
                    [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
                    [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
                ]		)
      else:
            raise ValueError('''Can\'t verify logits as model is not supported'''		)

      assert logits.shape == expected_shape, "Shape of logits not as expected"
      if not has_lm_head:
            if is_semantic:
                  assert torch.allclose(
                      logits[0, :3, :3, :3]				,						UpperCAmelCase_				,						atol=1E-3		), "First elements of logits not as expected"
            else:
                  print('''Predicted class idx:'''				,						logits.argmax(-1		).item()		)
                  assert torch.allclose(
                      logits[0, :3]				,						UpperCAmelCase_				,						atol=1E-3		), "First elements of logits not as expected"
                  assert logits.argmax(-1		).item() == expected_class_idx, "Predicted class index not as expected"

      Path(UpperCAmelCase_		).mkdir(exist_ok=UpperCAmelCase_		)
      print(F'''Saving model to {pytorch_dump_folder_path}'''		)
      model.save_pretrained(UpperCAmelCase_		)
      print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
      image_processor.save_pretrained(UpperCAmelCase_		)


if __name__ == "__main__":
     snake_case       :					Tuple        =  argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
         type=str,
         help='''URL 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.'''
     )
     snake_case       :					Tuple        =  parser.parse_args()
     convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 	_snake_case						(   unittest.TestCase							):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 SCREAMING_SNAKE_CASE__   = (ScoreSdeVeScheduler,)
 SCREAMING_SNAKE_CASE__   = ()




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= 4
       a		:List[str]											= 3
       a		:Any											= 8
       a		:Any											= 8

       a		:List[str]											= torch.rand((batch_size, num_channels, height, width)					)

       return sample




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= 4
       a		:Union[str, Any]											= 3
       a		:Optional[Any]											= 8
       a		:Tuple											= 8

       a		:Dict											= batch_size * num_channels * height * width
       a		:Optional[int]											= torch.arange(_lowerCamelCase					)
       a		:List[Any]											= sample.reshape(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= sample / num_elems
       a		:Dict											= sample.permute(3      ,						0      ,						1      ,						2					)

       return sample




 def  SCREAMING_SNAKE_CASE__       (	self					):
       def model(_lowerCamelCase      ,						_lowerCamelCase      ,						*_lowerCamelCase					):
             return sample * t / (t + 1)

       return model




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:str											= {
           '''num_train_timesteps''': 2000,
           '''snr''': 0.15,
           '''sigma_min''': 0.01,
           '''sigma_max''': 1348,
           '''sampling_eps''': 1e-5,
       }

       config.update(**_lowerCamelCase					)
       return config




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Optional[int]											= dict(self.forward_default_kwargs					)

       for scheduler_class in self.scheduler_classes:
             a		:Union[str, Any]											= self.dummy_sample
             a		:Tuple											= 0.1 * sample

             a		:Union[str, Any]											= self.get_scheduler_config(**_lowerCamelCase					)
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Optional[int]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:List[str]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Dict											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[Any]											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Dict											= dict(self.forward_default_kwargs					)
       kwargs.update(_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:int											= self.dummy_sample
             a		:str											= 0.1 * sample

             a		:Optional[Any]											= self.get_scheduler_config()
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Union[str, Any]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:Union[str, Any]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[int]											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:List[str]											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for timesteps in [10, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]      ,						[1, 100, 1000]					):
             self.check_over_configs(sigma_min=_lowerCamelCase      ,						sigma_max=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for t in [0.1, 0.5, 0.75]:
             self.check_over_forward(time_step=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= dict(self.forward_default_kwargs					)

       a		:Optional[Any]											= self.scheduler_classes[0]
       a		:Union[str, Any]											= self.get_scheduler_config()
       a		:List[Any]											= scheduler_class(**_lowerCamelCase					)

       a		:Union[str, Any]											= 3

       a		:List[Any]											= self.dummy_model()
       a		:Dict											= self.dummy_sample_deter

       scheduler.set_sigmas(_lowerCamelCase					)
       scheduler.set_timesteps(_lowerCamelCase					)
       a		:str											= torch.manual_seed(0					)

       for i, t in enumerate(scheduler.timesteps					):
             a		:Optional[int]											= scheduler.sigmas[i]

             for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                         a		:Dict											= model(_lowerCamelCase      ,						_lowerCamelCase					)
                   a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					).prev_sample

             with torch.no_grad():
                   a		:Any											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Optional[Any]											= scheduler.step_pred(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					)
             a							,       a		:Any											= output.prev_sample, output.prev_sample_mean

       a		:Dict											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:int											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert np.isclose(result_sum.item()      ,						143_7275_8528.0					)
       assert np.isclose(result_mean.item()      ,						1871_4530.0					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= dict(self.forward_default_kwargs					)

       a		:Union[str, Any]											= kwargs.pop('''num_inference_steps'''      ,						_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:Optional[int]											= self.get_scheduler_config()
             a		:str											= scheduler_class(**_lowerCamelCase					)

             a		:List[str]											= self.dummy_sample
             a		:List[Any]											= 0.1 * sample

             if num_inference_steps is not None and hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   scheduler.set_timesteps(_lowerCamelCase					)
             elif num_inference_steps is not None and not hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   a		:Dict											= num_inference_steps

             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						0      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						1      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             self.assertEqual(output_a.shape      ,						sample.shape					)
             self.assertEqual(output_a.shape      ,						output_a.shape					)





