




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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE      :				List[Any]				      =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__SCREAMING_SNAKE_CASE      :				Any				      =[]
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}.multihead_attn.out_proj.weight""",
              F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
          )
      )
      rename_keys.append(
          (
              F"""transformer.decoder.layers.{i}.multihead_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"""))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
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"""),
    ]
)

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


      '''simple docstring'''




      lowerCamelCase__ 					=			state_dict.pop(lowerCamelCase  )
      lowerCamelCase__ 					=			val

def      lowerCAmelCase_     (					lowerCamelCase:    Optional[int]  )   ->     int:


      '''simple docstring'''




      lowerCamelCase__ 					=			OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  lowerCamelCase__ 					=			key.replace('backbone.0.body'       ,			'backbone.conv_encoder.model'  )
                  lowerCamelCase__ 					=			value
            else:
                  lowerCamelCase__ 					=			value

      return new_state_dict

def      lowerCAmelCase_     (					lowerCamelCase:    int       ,			lowerCamelCase:    Any=False  )   ->     Optional[Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			''
      if is_panoptic:
            lowerCamelCase__ 					=			'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__ 					=			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""  )
            lowerCamelCase__ 					=			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__ 					=			in_proj_weight[:2_56, :]
            lowerCamelCase__ 					=			in_proj_bias[:2_56]
            lowerCamelCase__ 					=			in_proj_weight[2_56:5_12, :]
            lowerCamelCase__ 					=			in_proj_bias[2_56:5_12]
            lowerCamelCase__ 					=			in_proj_weight[-2_56:, :]
            lowerCamelCase__ 					=			in_proj_bias[-2_56:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6  ):
            # read in weights + bias of input projection layer of self-attention
            lowerCamelCase__ 					=			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""  )
            lowerCamelCase__ 					=			state_dict.pop(f"""{prefix}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[:2_56, :]
            lowerCamelCase__ 					=			in_proj_bias[:2_56]
            lowerCamelCase__ 					=			in_proj_weight[2_56:5_12, :]
            lowerCamelCase__ 					=			in_proj_bias[2_56:5_12]
            lowerCamelCase__ 					=			in_proj_weight[-2_56:, :]
            lowerCamelCase__ 					=			in_proj_bias[-2_56:]
            # read in weights + bias of input projection layer of cross-attention
            lowerCamelCase__ 					=			state_dict.pop(
                f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""  )
            lowerCamelCase__ 					=			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""  )
            # next, add query, keys and values (in that order) of cross-attention to the state dict
            lowerCamelCase__ 					=			in_proj_weight_cross_attn[:2_56, :]
            lowerCamelCase__ 					=			in_proj_bias_cross_attn[:2_56]
            lowerCamelCase__ 					=			in_proj_weight_cross_attn[2_56:5_12, :]
            lowerCamelCase__ 					=			in_proj_bias_cross_attn[2_56:5_12]
            lowerCamelCase__ 					=			in_proj_weight_cross_attn[-2_56:, :]
            lowerCamelCase__ 					=			in_proj_bias_cross_attn[-2_56:]

def      lowerCAmelCase_     (					)   ->     List[str]:


      '''simple docstring'''




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

      return im

@torch.no_grad()
def      lowerCAmelCase_     (					lowerCamelCase:    str       ,			lowerCamelCase:    int  )   ->     Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			DetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            lowerCamelCase__ 					=			'resnet101'
      if "dc5" in model_name:
            lowerCamelCase__ 					=			True
      lowerCamelCase__ 					=			'panoptic' in model_name
      if is_panoptic:
            lowerCamelCase__ 					=			2_50
      else:
            lowerCamelCase__ 					=			91
            lowerCamelCase__ 					=			'huggingface/label-files'
            lowerCamelCase__ 					=			'coco-detection-id2label.json'
            lowerCamelCase__ 					=			json.load(open(hf_hub_download(lowerCamelCase       ,			lowerCamelCase       ,			repo_type='dataset'  )       ,			'r'  )  )
            lowerCamelCase__ 					=			{int(lowerCamelCase  ): v for k, v in idalabel.items()}
            lowerCamelCase__ 					=			idalabel
            lowerCamelCase__ 					=			{v: k for k, v in idalabel.items()}

      # load image processor
      lowerCamelCase__ 					=			'coco_panoptic' if is_panoptic else 'coco_detection'
      lowerCamelCase__ 					=			DetrImageProcessor(format=lowerCamelCase  )

      # prepare image
      lowerCamelCase__ 					=			prepare_img()
      lowerCamelCase__ 					=			image_processor(images=lowerCamelCase       ,			return_tensors='pt'  )
      lowerCamelCase__ 					=			encoding['pixel_values']

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

      # load original model from torch hub
      lowerCamelCase__ 					=			torch.hub.load('facebookresearch/detr'       ,			lowerCamelCase       ,			pretrained=lowerCamelCase  ).eval()
      lowerCamelCase__ 					=			detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  lowerCamelCase__ 					=			'detr.' + src
            rename_key(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
      lowerCamelCase__ 					=			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__ 					=			'detr.model.' if is_panoptic else 'model.'
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith('detr'  )
                      and not key.startswith('class_labels_classifier'  )
                      and not key.startswith('bbox_predictor'  )
                  ):
                        lowerCamelCase__ 					=			state_dict.pop(lowerCamelCase  )
                        lowerCamelCase__ 					=			val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        lowerCamelCase__ 					=			state_dict.pop(lowerCamelCase  )
                        lowerCamelCase__ 					=			val
                  elif key.startswith('bbox_attention'  ) or key.startswith('mask_head'  ):
                        continue
                  else:
                        lowerCamelCase__ 					=			state_dict.pop(lowerCamelCase  )
                        lowerCamelCase__ 					=			val
            else:
                  if not key.startswith('class_labels_classifier'  ) and not key.startswith('bbox_predictor'  ):
                        lowerCamelCase__ 					=			state_dict.pop(lowerCamelCase  )
                        lowerCamelCase__ 					=			val
    # finally, create HuggingFace model and load state dict
      lowerCamelCase__ 					=			DetrForSegmentation(lowerCamelCase  ) if is_panoptic else DetrForObjectDetection(lowerCamelCase  )
      model.load_state_dict(lowerCamelCase  )
      model.eval()
      # verify our conversion
      lowerCamelCase__ 					=			detr(lowerCamelCase  )
      lowerCamelCase__ 					=			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__":
      __SCREAMING_SNAKE_CASE      :				str				      =argparse.ArgumentParser()

      parser.add_argument(
          """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the 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."""
      )
      __SCREAMING_SNAKE_CASE      :				Union[str, Any]				      =parser.parse_args()
      convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Optional[int]       =(DPMSolverMultistepScheduler,)
     _snake_case:		Union[str, Any]       =(("num_inference_steps", 25),)




     def 	__snake_case				(      self					:				str		,					**A__					:				Union[str, Any]     )			->    str:
           lowerCamelCase__ 					=			{
               'num_train_timesteps': 1_0_0_0,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
               'solver_order': 2,
               'prediction_type': 'epsilon',
               'thresholding': False,
               'sample_max_value': 1.0,
               'algorithm_type': 'dpmsolver++',
               'solver_type': 'midpoint',
               'lower_order_final': False,
               'lambda_min_clipped': -float('inf'     ),
               'variance_type': None,
           }

           config.update(**A__     )
           return config




     def 	__snake_case				(      self					:				Dict		,					A__					:				Dict=0		,					**A__					:				Dict     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )
           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )
           lowerCamelCase__ 					=			self.dummy_sample
           lowerCamelCase__ 					=			0.1 * sample
           lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )
                 scheduler.set_timesteps(A__     )
                 # copy over dummy past residuals
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A__     )
                       lowerCamelCase__ 					=			scheduler_class.from_pretrained(A__     )
                       new_scheduler.set_timesteps(A__     )
                       # copy over dummy past residuals
                       lowerCamelCase__ 					=			dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase__					,						lowerCamelCase__ 					=			sample, sample
                 for t in range(A__		,					time_step + scheduler.config.solver_order + 1     ):
                       lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                       lowerCamelCase__ 					=			new_scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				List[str]     )			->    Union[str, Any]:
           pass




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				List[str]=0		,					**A__					:				Optional[Any]     )			->    Optional[Any]:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )
           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )
           lowerCamelCase__ 					=			self.dummy_sample
           lowerCamelCase__ 					=			0.1 * sample
           lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config()
                 lowerCamelCase__ 					=			scheduler_class(**A__     )
                 scheduler.set_timesteps(A__     )

                 # copy over dummy past residuals (must be after setting timesteps)
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

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

                       # copy over dummy past residual (must be after setting timesteps)
                       lowerCamelCase__ 					=			dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                 lowerCamelCase__ 					=			new_scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Any=None		,					**A__					:				Any     )			->    List[str]:
           if scheduler is None:
                 lowerCamelCase__ 					=			self.scheduler_classes[0]
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

           lowerCamelCase__ 					=			1_0
           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter
           scheduler.set_timesteps(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			model(A__		,					A__     )
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     ).prev_sample

           return sample




     def 	__snake_case				(      self					:				Dict     )			->    Any:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )

           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config()
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

                 lowerCamelCase__ 					=			self.dummy_sample
                 lowerCamelCase__ 					=			0.1 * sample

                 if num_inference_steps is not None and hasattr(A__		,					'set_timesteps'     ):
                       scheduler.set_timesteps(A__     )
                 elif num_inference_steps is not None and not hasattr(A__		,					'set_timesteps'     ):
                       lowerCamelCase__ 					=			num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 lowerCamelCase__ 					=			scheduler.timesteps[5]
                 lowerCamelCase__ 					=			scheduler.timesteps[6]

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				Dict     )			->    Tuple:
           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=A__     )




     def 	__snake_case				(      self					:				int     )			->    str:
           self.check_over_configs(thresholding=A__     )
           for order in [1, 2, 3]:
                 for solver_type in ["midpoint", "heun"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=A__		,					prediction_type=A__		,					sample_max_value=A__		,					algorithm_type='dpmsolver++'		,					solver_order=A__		,					solver_type=A__		,					)




     def 	__snake_case				(      self					:				Any     )			->    Optional[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    str:
           for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                 for solver_type in ["midpoint", "heun"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                         if order == 3:
                                               continue
                                   else:
                                         self.check_over_configs(
                                             solver_order=A__		,					solver_type=A__		,					prediction_type=A__		,					algorithm_type=A__		,					)
                                   lowerCamelCase__ 					=			self.full_loop(
                                       solver_order=A__		,					solver_type=A__		,					prediction_type=A__		,					algorithm_type=A__		,					)
                                   assert not torch.isnan(A__     ).any(), "Samples have nan numbers"




     def 	__snake_case				(      self					:				List[Any]     )			->    int:
           self.check_over_configs(lower_order_final=A__     )
           self.check_over_configs(lower_order_final=A__     )




     def 	__snake_case				(      self					:				List[Any]     )			->    Dict:
           self.check_over_configs(lambda_min_clipped=-float('inf'     )     )
           self.check_over_configs(lambda_min_clipped=-5.1     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    int:
           self.check_over_configs(variance_type=A__     )
           self.check_over_configs(variance_type='learned_range'     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    List[str]:
           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=A__		,					time_step=0     )




     def 	__snake_case				(      self					:				List[Any]     )			->    str:
           lowerCamelCase__ 					=			self.full_loop()
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3




     def 	__snake_case				(      self					:				Tuple     )			->    int:
           lowerCamelCase__ 					=			self.full_loop(thresholding=A__		,					dynamic_thresholding_ratio=0.87		,					sample_max_value=0.5     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 1.1364     ) < 1E-3




     def 	__snake_case				(      self					:				Optional[int]     )			->    List[Any]:
           lowerCamelCase__ 					=			self.full_loop(prediction_type='v_prediction'     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.2251     ) < 1E-3




     def 	__snake_case				(      self					:				Tuple     )			->    Optional[Any]:
           lowerCamelCase__ 					=			self.full_loop(prediction_type='v_prediction'		,					use_karras_sigmas=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.2096     ) < 1E-3




     def 	__snake_case				(      self					:				Any     )			->    Optional[int]:
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           lowerCamelCase__ 					=			DPMSolverMultistepScheduler(**self.get_scheduler_config()     )
           lowerCamelCase__ 					=			self.full_loop(scheduler=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3

           lowerCamelCase__ 					=			DPMSolverSinglestepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			UniPCMultistepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			DEISMultistepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			DPMSolverMultistepScheduler.from_config(scheduler.config     )

           lowerCamelCase__ 					=			self.full_loop(scheduler=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3




     def 	__snake_case				(      self					:				int     )			->    Optional[int]:
           lowerCamelCase__ 					=			self.scheduler_classes[0]
           lowerCamelCase__ 					=			self.get_scheduler_config(thresholding=A__		,					dynamic_thresholding_ratio=0     )
           lowerCamelCase__ 					=			scheduler_class(**A__     )

           lowerCamelCase__ 					=			1_0
           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter.half()
           scheduler.set_timesteps(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			model(A__		,					A__     )
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     ).prev_sample

           assert sample.dtype == torch.floataa




     def 	__snake_case				(      self					:				List[str]		,					**A__					:				int     )			->    Optional[int]:
           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

                 scheduler.set_timesteps(scheduler.config.num_train_timesteps     )
                 assert len(scheduler.timesteps.unique()     ) == scheduler.num_inference_steps