

'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)


def       snake_case   (	snake_case      : List[Any]			) ->  Any:


    """simple docstring"""


    lowerCAmelCase 						=							SwinConfig(
        embed_dim=192					,		depths=(2, 2, 18, 2)					,		num_heads=(6, 12, 24, 48)					,		window_size=12					,		out_features=['stage2', 'stage3', 'stage4']					,		)

    lowerCAmelCase 						=							DetaConfig(
        backbone_config=snake_case					,		num_queries=900					,		encoder_ffn_dim=2048					,		decoder_ffn_dim=2048					,		num_feature_levels=5					,		assign_first_stage=snake_case					,		with_box_refine=snake_case					,		two_stage=snake_case					,		)

    # set labels
    lowerCAmelCase 						=							'huggingface/label-files'
    if "o365" in model_name:
        lowerCAmelCase 						=							366
        lowerCAmelCase 						=							'object365-id2label.json'
    else:
        lowerCAmelCase 						=							91
        lowerCAmelCase 						=							'coco-detection-id2label.json'

    lowerCAmelCase 						=							num_labels
    lowerCAmelCase 						=							json.load(open(cached_download(hf_hub_url(snake_case					,		snake_case					,		repo_type='dataset'			)			)					,		'r'			)			)
    lowerCAmelCase 						=							{int(snake_case			): v for k, v in idalabel.items()}
    lowerCAmelCase 						=							idalabel
    lowerCAmelCase 						=							{v: k for k, v in idalabel.items()}

    return config


def       snake_case   (	snake_case      : Optional[int]			) ->  Optional[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							[]

    # stem
    # fmt: off
    rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')			)
    rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')			)
    rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')			)
    rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias')			)
    # stages
    for i in range(len(config.backbone_config.depths			)			):
        for j in range(config.backbone_config.depths[i]			):
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')			)

        if i < 3:
            rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')			)
            rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')			)

    rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')			)
    rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')			)
    rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')			)
    rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')			)
    rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')			)
    rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')			)

    # transformer encoder
    for i in range(config.encoder_layers			):
        rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight')			)
        rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias')			)

    # transformer decoder
    for i in range(config.decoder_layers			):
        rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight')			)
        rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias')			)

    # fmt: on

    return rename_keys


def       snake_case   (	snake_case      : Optional[int]					,		snake_case      : Any					,		snake_case      : Dict			) ->  Any:


    """simple docstring"""


    lowerCAmelCase 						=							dct.pop(snake_case			)
    lowerCAmelCase 						=							val


def       snake_case   (	snake_case      : Union[str, Any]					,		snake_case      : Union[str, Any]			) ->  Tuple:


    """simple docstring"""


    lowerCAmelCase 						=							[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
    for i in range(len(backbone_config.depths			)			):
        lowerCAmelCase 						=							num_features[i]
        for j in range(backbone_config.depths[i]			):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            lowerCAmelCase 						=							state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'			)
            lowerCAmelCase 						=							state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'			)
            # next, add query, keys and values (in that order) to the state dict
            lowerCAmelCase 						=							in_proj_weight[:dim, :]
            lowerCAmelCase 						=							in_proj_bias[: dim]
            lowerCAmelCase 						=							in_proj_weight[
                dim : dim * 2, :
            ]
            lowerCAmelCase 						=							in_proj_bias[
                dim : dim * 2
            ]
            lowerCAmelCase 						=							in_proj_weight[
                -dim :, :
            ]
            lowerCAmelCase 						=							in_proj_bias[-dim :]
            # fmt: on


def       snake_case   (	snake_case      : List[Any]					,		snake_case      : Union[str, Any]			) ->  str:


    """simple docstring"""


    lowerCAmelCase 						=							config.d_model
    for i in range(config.decoder_layers			):
        # read in weights + bias of input projection layer of self-attention
        lowerCAmelCase 						=							state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
        lowerCAmelCase 						=							state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_bias'			)
        # next, add query, keys and values (in that order) to the state dict
        lowerCAmelCase 						=							in_proj_weight[:hidden_size, :]
        lowerCAmelCase 						=							in_proj_bias[:hidden_size]
        lowerCAmelCase 						=							in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        lowerCAmelCase 						=							in_proj_bias[hidden_size : hidden_size * 2]
        lowerCAmelCase 						=							in_proj_weight[-hidden_size:, :]
        lowerCAmelCase 						=							in_proj_bias[-hidden_size:]


def       snake_case   (	) ->  Any:


    """simple docstring"""


    lowerCAmelCase 						=							'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCAmelCase 						=							Image.open(requests.get(snake_case					,		stream=snake_case			).raw			)

    return im


@torch.no_grad()
def       snake_case   (	snake_case      : Tuple					,		snake_case      : Optional[Any]					,		snake_case      : Dict			) ->  Optional[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							get_deta_config(snake_case			)

    # load original state dict
    if model_name == "deta-swin-large":
        lowerCAmelCase 						=							hf_hub_download(repo_id='nielsr/deta-checkpoints'					,		filename='adet_swin_ft.pth'			)
    elif model_name == "deta-swin-large-o365":
        lowerCAmelCase 						=							hf_hub_download(repo_id='jozhang97/deta-swin-l-o365'					,		filename='deta_swin_pt_o365.pth'			)
    else:
        raise ValueError(F'Model name {model_name} not supported'			)

    lowerCAmelCase 						=							torch.load(snake_case					,		map_location='cpu'			)['model']

    # original state dict
    for name, param in state_dict.items():
        print(snake_case					,		param.shape			)

    # rename keys
    lowerCAmelCase 						=							create_rename_keys(snake_case			)
    for src, dest in rename_keys:
        rename_key(snake_case					,		snake_case					,		snake_case			)
    read_in_swin_q_k_v(snake_case					,		config.backbone_config			)
    read_in_decoder_q_k_v(snake_case					,		snake_case			)

    # fix some prefixes
    for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
            lowerCAmelCase 						=							state_dict.pop(snake_case			)
            lowerCAmelCase 						=							val
        if "input_proj" in key:
            lowerCAmelCase 						=							state_dict.pop(snake_case			)
            lowerCAmelCase 						=							val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            lowerCAmelCase 						=							state_dict.pop(snake_case			)
            lowerCAmelCase 						=							val

    # finally, create HuggingFace model and load state dict
    lowerCAmelCase 						=							DetaForObjectDetection(snake_case			)
    model.load_state_dict(snake_case			)
    model.eval()

    lowerCAmelCase 						=							'cuda' if torch.cuda.is_available() else 'cpu'
    model.to(snake_case			)

    # load image processor
    lowerCAmelCase 						=							DetaImageProcessor(format='coco_detection'			)

    # verify our conversion on image
    lowerCAmelCase 						=							prepare_img()
    lowerCAmelCase 						=							processor(images=snake_case					,		return_tensors='pt'			)
    lowerCAmelCase 						=							encoding['pixel_values']
    lowerCAmelCase 						=							model(pixel_values.to(snake_case			)			)

    # verify logits
    print('Logits:'					,		outputs.logits[0, :3, :3]			)
    print('Boxes:'					,		outputs.pred_boxes[0, :3, :3]			)
    if model_name == "deta-swin-large":
        lowerCAmelCase 						=							torch.tensor(
            [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]			)
        lowerCAmelCase 						=							torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]			)
    elif model_name == "deta-swin-large-o365":
        lowerCAmelCase 						=							torch.tensor(
            [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]			)
        lowerCAmelCase 						=							torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]			)
    assert torch.allclose(outputs.logits[0, :3, :3]					,		expected_logits.to(snake_case			)					,		atol=1e-4			)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,		expected_boxes.to(snake_case			)					,		atol=1e-4			)
    print('Everything ok!'			)

    if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'			)
        Path(snake_case			).mkdir(exist_ok=snake_case			)
        model.save_pretrained(snake_case			)
        processor.save_pretrained(snake_case			)

    # Push to hub
    if push_to_hub:
        print('Pushing model and processor to hub...'			)
        model.push_to_hub(F'jozhang97/{model_name}'			)
        processor.push_to_hub(F'jozhang97/{model_name}'			)


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

     parser.add_argument(
         "--model_name",
         type=str,
         default="deta-swin-large",
         choices=["deta-swin-large", "deta-swin-large-o365"],
         help="Name of the 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.",
     )
     parser.add_argument(
         "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
     )
     _UpperCamelCase						:  List[Any]							       =				parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     (DDPMScheduler,)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]	):
          self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE      , beta_end=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE	)
      for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=_SCREAMING_SNAKE_CASE      , prediction_type=_SCREAMING_SNAKE_CASE      , sample_max_value=_SCREAMING_SNAKE_CASE      , )

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for t in [0, 5_00, 9_99]:
          self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87	) - 0.00_979	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99	) - 0.02	)	) < 1e-5

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

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

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 258.9_606	) < 1e-2
      assert abs(result_mean.item() - 0.3_372	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(prediction_type='v_prediction'	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

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

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 202.0_296	) < 1e-2
      assert abs(result_mean.item() - 0.2_631	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							scheduler.timesteps

      for i, timestep in enumerate(_SCREAMING_SNAKE_CASE	):
          if i == len(_SCREAMING_SNAKE_CASE	) - 1:
              lowerCAmelCase 						=							-1
          else:
              lowerCAmelCase 						=							timesteps[i + 1]

          lowerCAmelCase 						=							scheduler.previous_timestep(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							prev_t.item()

          self.assertEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[1_00, 87, 50, 51, 0]

      with self.assertRaises(_SCREAMING_SNAKE_CASE      , msg='`custom_timesteps` must be in descending order.'	):
          scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]
      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      with self.assertRaises(_SCREAMING_SNAKE_CASE      , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'	):
          scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE      , timesteps=_SCREAMING_SNAKE_CASE	)


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          _SCREAMING_SNAKE_CASE      , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      , ):
          scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)





