

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()
lowerCAmelCase     :   Union[str, Any]						     =  logging.get_logger(__name__)

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     SwinConfig(
          embed_dim=1_9_2					,   depths=(2, 2, 1_8, 2)					,   num_heads=(6, 1_2, 2_4, 4_8)					,   window_size=1_2					,   out_features=['stage2', 'stage3', 'stage4']					,   )

      SCREAMING_SNAKE_CASE_				:  int           =     DetaConfig(
          backbone_config=a					,   num_queries=9_0_0					,   encoder_ffn_dim=2_0_4_8					,   decoder_ffn_dim=2_0_4_8					,   num_feature_levels=5					,   assign_first_stage=a					,   with_box_refine=a					,   two_stage=a					,   )

      # set labels
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     'huggingface/label-files'
      if "o365" in model_name:
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     3_6_6
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'object365-id2label.json'
      else:
            SCREAMING_SNAKE_CASE_				:  str           =     9_1
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     'coco-detection-id2label.json'

      SCREAMING_SNAKE_CASE_				:  Any           =     num_labels
      SCREAMING_SNAKE_CASE_				:  List[str]           =     json.load(open(cached_download(hf_hub_url(a					,   a					,   repo_type='dataset'  )  )					,   'r'  )  )
      SCREAMING_SNAKE_CASE_				:  int           =     {int(a  ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     idalabel
      SCREAMING_SNAKE_CASE_				:  str           =     {v: k for k, v in idalabel.items()}

      return config

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     []

      # 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    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     dct.pop(a  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     val

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
      for i in range(len(backbone_config.depths  )  ):
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     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)
                  SCREAMING_SNAKE_CASE_				:  str           =     state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"  )
                  SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     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
                  SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     in_proj_weight[:dim, :]
                  SCREAMING_SNAKE_CASE_				:  Optional[int]           =     in_proj_bias[: dim]
                  SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     in_proj_weight[
                      dim : dim * 2, :
                  ]
                  SCREAMING_SNAKE_CASE_				:  Dict           =     in_proj_bias[
                      dim : dim * 2
                  ]
                  SCREAMING_SNAKE_CASE_				:  int           =     in_proj_weight[
                      -dim :, :
                  ]
                  SCREAMING_SNAKE_CASE_				:  List[str]           =     in_proj_bias[-dim :]
                  # fmt: on

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  List[str]           =     config.d_model
      for i in range(config.decoder_layers  ):
            # read in weights + bias of input projection layer of self-attention
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     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
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     in_proj_weight[:hidden_size, :]
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     in_proj_bias[:hidden_size]
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE_				:  Dict           =     in_proj_bias[hidden_size : hidden_size * 2]
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     in_proj_weight[-hidden_size:, :]
            SCREAMING_SNAKE_CASE_				:  Dict           =     in_proj_bias[-hidden_size:]

def    A_  (							):

      """simple docstring"""



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

      return im

@torch.no_grad()
def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     get_deta_config(a  )

      # load original state dict
      if model_name == "deta-swin-large":
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     hf_hub_download(repo_id='nielsr/deta-checkpoints'					,   filename='adet_swin_ft.pth'  )
      elif model_name == "deta-swin-large-o365":
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     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"  )

      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.load(a					,   map_location='cpu'  )['model']

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

      # rename keys
      SCREAMING_SNAKE_CASE_				:  List[str]           =     create_rename_keys(a  )
      for src, dest in rename_keys:
            rename_key(a					,   a					,   a  )
      read_in_swin_q_k_v(a					,   config.backbone_config  )
      read_in_decoder_q_k_v(a					,   a  )

      # fix some prefixes
      for key in state_dict.copy().keys():
            if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                  SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     state_dict.pop(a  )
                  SCREAMING_SNAKE_CASE_				:  int           =     val
            if "input_proj" in key:
                  SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     state_dict.pop(a  )
                  SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                  SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     state_dict.pop(a  )
                  SCREAMING_SNAKE_CASE_				:  str           =     val

    # finally, create HuggingFace model and load state dict
      SCREAMING_SNAKE_CASE_				:  List[str]           =     DetaForObjectDetection(a  )
      model.load_state_dict(a  )
      model.eval()

      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     'cuda' if torch.cuda.is_available() else 'cpu'
      model.to(a  )

      # load image processor
      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     DetaImageProcessor(format='coco_detection'  )

      # verify our conversion on image
      SCREAMING_SNAKE_CASE_				:  Tuple           =     prepare_img()
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     processor(images=a					,   return_tensors='pt'  )
      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     encoding['pixel_values']
      SCREAMING_SNAKE_CASE_				:  List[str]           =     model(pixel_values.to(a  )  )

      # verify logits
      print('Logits:'					,   outputs.logits[0, :3, :3]  )
      print('Boxes:'					,   outputs.pred_boxes[0, :3, :3]  )
      if model_name == "deta-swin-large":
            SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.tensor(
                [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]  )
            SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]  )
      elif model_name == "deta-swin-large-o365":
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.tensor(
                [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]  )
            SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]  )
      assert torch.allclose(outputs.logits[0, :3, :3]					,   expected_logits.to(a  )					,   atol=1e-4  )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,   expected_boxes.to(a  )					,   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(a  ).mkdir(exist_ok=a  )
            model.save_pretrained(a  )
            processor.save_pretrained(a  )

      # 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__":
    lowerCAmelCase     :   Union[str, 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.'
    )
    lowerCAmelCase     :   Optional[Any]						     =  parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[str]       =  (PNDMScheduler,)
  SCREAMING_SNAKE_CASE    :      Dict       =  (('''num_inference_steps''', 50),)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     {
            'num_train_timesteps': 1000,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**_SCREAMING_SNAKE_CASE					)
        return config

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Any           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              # copy over dummy past residuals
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Any           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  int           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  int           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

              # copy over dummy past residuals (must be after setting timesteps)
              SCREAMING_SNAKE_CASE_				:  Tuple           =     dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Dict           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

                    # copy over dummy past residual (must be after setting timesteps)
                    SCREAMING_SNAKE_CASE_				:  str           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  List[str]           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     10
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_sample_deter
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

        for i, t in enumerate(scheduler.prk_timesteps					):
              SCREAMING_SNAKE_CASE_				:  Any           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps					):
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        return sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     dict(self.forward_default_kwargs					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  int           =     0.1 * sample

              if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              SCREAMING_SNAKE_CASE_				:  Dict           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              SCREAMING_SNAKE_CASE_				:  str           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for timesteps in [100, 1000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.get_scheduler_config(steps_offset=1					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
        scheduler.set_timesteps(10					)
        assert torch.equal(
            scheduler.timesteps ,					torch.LongTensor(
                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					) ,					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for beta_start, beta_end in zip([0.0001, 0.001] ,					[0.002, 0.02]					):
              self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE ,					beta_end=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for t in [1, 5, 10]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for t, num_inference_steps in zip([1, 5, 10] ,					[10, 50, 100]					):
              self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     27

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  int           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample

              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

              # before power of 3 fix, would error on first step, so we only need to do two
              for i, t in enumerate(scheduler.prk_timesteps[:2]					):
                    SCREAMING_SNAKE_CASE_				:  int           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        with self.assertRaises(_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  str           =     self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.step_plms(self.dummy_sample ,					1 ,					self.dummy_sample					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.full_loop()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 198.1318					) < 1e-2
        assert abs(result_mean.item() - 0.2580					) < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.full_loop(prediction_type='v_prediction'					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 67.3986					) < 1e-2
        assert abs(result_mean.item() - 0.0878					) < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					beta_start=0.01					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 230.0399					) < 1e-2
        assert abs(result_mean.item() - 0.2995					) < 1e-3



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					beta_start=0.01					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 186.9482					) < 1e-2
        assert abs(result_mean.item() - 0.2434					) < 1e-3






