


"""simple docstring"""






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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase							:    Dict					   =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase							:    str					   =	[]
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
          f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
          f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

  # q, k, v projections in self/cross-attention in decoder for conditional DETR
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
  )

  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
  )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Optional[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     state_dict.pop(_lowerCamelCase       )
   _lowerCamelCase   :				str							=     val







def        lowerCamelCase_(		_lowerCamelCase       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _lowerCamelCase   :				Dict							=     key.replace("backbone.0.body"			,		"backbone.conv_encoder.model"       )
         _lowerCamelCase   :				str							=     value
      else:
         _lowerCamelCase   :				Any							=     value

   return new_state_dict







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase=False       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				str							=     ""
   if is_panoptic:
      _lowerCamelCase   :				Any							=     "conditional_detr."

   # first: transformer encoder
   for i in range(6       ):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      _lowerCamelCase   :				Optional[int]							=     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowerCamelCase   :				Union[str, Any]							=     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""       )
      # next, add query, keys and values (in that order) to the state dict
      _lowerCamelCase   :				int							=     in_proj_weight[:256, :]
      _lowerCamelCase   :				Dict							=     in_proj_bias[:256]
      _lowerCamelCase   :				int							=     in_proj_weight[256:512, :]
      _lowerCamelCase   :				Tuple							=     in_proj_bias[256:512]
      _lowerCamelCase   :				Tuple							=     in_proj_weight[-256:, :]
      _lowerCamelCase   :				Optional[int]							=     in_proj_bias[-256:]







def        lowerCamelCase_(		)    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     "http://images.cocodataset.org/val2017/000000039769.jpg"
   _lowerCamelCase   :				List[Any]							=     Image.open(requests.get(_lowerCamelCase			,		stream=_lowerCamelCase       ).raw       )

   return im







@torch.no_grad()
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Optional[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[int]							=     ConditionalDetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      _lowerCamelCase   :				Optional[int]							=     "resnet101"
   if "dc5" in model_name:
      _lowerCamelCase   :				List[Any]							=     True
   _lowerCamelCase   :				Optional[int]							=     "panoptic" in model_name
   if is_panoptic:
      _lowerCamelCase   :				Optional[int]							=     250
   else:
      _lowerCamelCase   :				Optional[Any]							=     91
      _lowerCamelCase   :				List[Any]							=     "huggingface/label-files"
      _lowerCamelCase   :				List[Any]							=     "coco-detection-id2label.json"
      _lowerCamelCase   :				Union[str, Any]							=     json.load(open(hf_hub_download(_lowerCamelCase			,		_lowerCamelCase			,		repo_type="dataset"       )			,		"r"       )       )
      _lowerCamelCase   :				str							=     {int(_lowerCamelCase       ): v for k, v in idalabel.items()}
      _lowerCamelCase   :				Any							=     idalabel
      _lowerCamelCase   :				List[str]							=     {v: k for k, v in idalabel.items()}

   # load image processor
   _lowerCamelCase   :				Any							=     "coco_panoptic" if is_panoptic else "coco_detection"
   _lowerCamelCase   :				Tuple							=     ConditionalDetrImageProcessor(format=_lowerCamelCase       )

   # prepare image
   _lowerCamelCase   :				List[Any]							=     prepare_img()
   _lowerCamelCase   :				str							=     image_processor(images=_lowerCamelCase			,		return_tensors="pt"       )
   _lowerCamelCase   :				Union[str, Any]							=     encoding["pixel_values"]

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

   # load original model from torch hub
   _lowerCamelCase   :				int							=     torch.hub.load("DeppMeng/ConditionalDETR"			,		_lowerCamelCase			,		pretrained=_lowerCamelCase       ).eval()
   _lowerCamelCase   :				Optional[Any]							=     conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowerCamelCase   :				Tuple							=     "conditional_detr." + src
      rename_key(_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )
   _lowerCamelCase   :				int							=     rename_backbone_keys(_lowerCamelCase       )
   # query, key and value matrices need special treatment
   read_in_q_k_v(_lowerCamelCase			,		is_panoptic=_lowerCamelCase       )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   _lowerCamelCase   :				Tuple							=     "conditional_detr.model." if is_panoptic else "model."
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith("conditional_detr"       )
             and not key.startswith("class_labels_classifier"       )
             and not key.startswith("bbox_predictor"       )
         ):
            _lowerCamelCase   :				List[Any]							=     state_dict.pop(_lowerCamelCase       )
            _lowerCamelCase   :				Optional[Any]							=     val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowerCamelCase   :				List[str]							=     state_dict.pop(_lowerCamelCase       )
            _lowerCamelCase   :				Tuple							=     val
         elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
            continue
         else:
            _lowerCamelCase   :				int							=     state_dict.pop(_lowerCamelCase       )
            _lowerCamelCase   :				Optional[int]							=     val
      else:
         if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
            _lowerCamelCase   :				int							=     state_dict.pop(_lowerCamelCase       )
            _lowerCamelCase   :				Dict							=     val
    # finally, create HuggingFace model and load state dict
   _lowerCamelCase   :				Any							=     ConditionalDetrForSegmentation(_lowerCamelCase       ) if is_panoptic else ConditionalDetrForObjectDetection(_lowerCamelCase       )
   model.load_state_dict(_lowerCamelCase       )
   model.eval()
   model.push_to_hub(repo_id=_lowerCamelCase			,		organization="DepuMeng"			,		commit_message="Add model"       )
   # verify our conversion
   _lowerCamelCase   :				List[Any]							=     conditional_detr(_lowerCamelCase       )
   _lowerCamelCase   :				Optional[Any]							=     model(_lowerCamelCase       )
   assert torch.allclose(outputs.logits			,		original_outputs["pred_logits"]			,		atol=1e-4       )
   assert torch.allclose(outputs.pred_boxes			,		original_outputs["pred_boxes"]			,		atol=1e-4       )
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks			,		original_outputs["pred_masks"]			,		atol=1e-4       )

   # Save model and image processor
   logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
   Path(_lowerCamelCase       ).mkdir(exist_ok=_lowerCamelCase       )
   model.save_pretrained(_lowerCamelCase       )
   image_processor.save_pretrained(_lowerCamelCase       )


if __name__ == "__main__":
  _lowerCAmelCase							:    Any					   =	argparse.ArgumentParser()

  parser.add_argument(
      '''--model_name''',
      default='''conditional_detr_resnet50''',
      type=str,
      help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
  )
  _lowerCAmelCase							:    Tuple					   =	parser.parse_args()
  convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(
											    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass