




import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


__SCREAMING_SNAKE_CASE      :				Optional[Any]				      ={
    """vit_b32""": {
        """embed_dim""": 512,
        """image_resolution""": 768,
        """context_length""": 16,
        """vocab_size""": 49_408,
        """vision_layers""": 12,
        """vision_width""": 768,
        """vision_patch_size""": 32,
        """transformer_width""": 512,
        """transformer_heads""": 8,
        """transformer_layers""": 12,
    },
    """vit_b16""": {
        """embed_dim""": 512,
        """image_resolution""": 768,
        """context_length""": 16,
        """vocab_size""": 49_408,
        """vision_layers""": 12,
        """vision_width""": 768,
        """vision_patch_size""": 16,
        """transformer_width""": 512,
        """transformer_heads""": 8,
        """transformer_layers""": 12,
    },
    """vit_l14""": {
        """embed_dim""": 768,
        """image_resolution""": 840,
        """context_length""": 16,
        """vocab_size""": 49_408,
        """vision_layers""": 24,
        """vision_width""": 1_024,
        """vision_patch_size""": 14,
        """transformer_width""": 768,
        """transformer_heads""": 12,
        """transformer_layers""": 12,
    },
}

def      lowerCAmelCase_     (					lowerCamelCase:    List[str]       ,			lowerCamelCase:    List[str]=""       ,			lowerCamelCase:    List[Any]="/"  )   ->     Optional[Any]:


						'''simple docstring'''




						lowerCamelCase__ 					=			[]

						for k, v in params.items():
												lowerCamelCase__ 					=			parent_key + sep + k if parent_key else k

												if isinstance(lowerCamelCase       ,			collections.MutableMapping  ):
																		items.extend(flatten_nested_dict(lowerCamelCase       ,			lowerCamelCase       ,			sep=lowerCamelCase  ).items()  )
												else:
																		items.append((new_key, v)  )
						return dict(lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    int  )   ->     Dict:


						'''simple docstring'''




						return jax.tree_util.tree_map(lambda lowerCamelCase  : x.astype(jnp.floataa  ) if x.dtype == jnp.bfloataa else x       ,			lowerCamelCase  )

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


						'''simple docstring'''




						lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			pt_attn_layer.in_proj_weight.chunk(3       ,			dim=0  )
						lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			pt_attn_layer.in_proj_bias.chunk(3       ,			dim=0  )

						lowerCamelCase__ 					=			pt_attn_layer.out_proj.weight
						lowerCamelCase__ 					=			pt_attn_layer.out_proj.bias

						lowerCamelCase__ 					=			q_proj
						lowerCamelCase__ 					=			q_proj_bias

						lowerCamelCase__ 					=			k_proj
						lowerCamelCase__ 					=			k_proj_bias

						lowerCamelCase__ 					=			v_proj
						lowerCamelCase__ 					=			v_proj_bias

						lowerCamelCase__ 					=			out_proj_weights
						lowerCamelCase__ 					=			out_proj_bias

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


						'''simple docstring'''




						copy_linear(hf_mlp.fca       ,			pt_mlp.c_fc  )
						copy_linear(hf_mlp.fca       ,			pt_mlp.c_proj  )

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


						'''simple docstring'''




						lowerCamelCase__ 					=			pt_linear.weight
						lowerCamelCase__ 					=			pt_linear.bias

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


						'''simple docstring'''




						copy_linear(hf_layer.layer_norma       ,			pt_layer.ln_a  )
						copy_linear(hf_layer.layer_norma       ,			pt_layer.ln_a  )

						# copy MLP
						copy_mlp(hf_layer.mlp       ,			pt_layer.mlp  )

						# copy attn
						copy_attn_layer(hf_layer.self_attn       ,			pt_layer.attn  )

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


						'''simple docstring'''




						for hf_layer, pt_layer in zip(lowerCamelCase       ,			lowerCamelCase  ):
												copy_layer(lowerCamelCase       ,			lowerCamelCase  )

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


						'''simple docstring'''




						lowerCamelCase__ 					=			pt_model.token_embedding.weight
						lowerCamelCase__ 					=			pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm       ,			pt_model.ln_final  )

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers       ,			pt_model.transformer.resblocks  )

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


						'''simple docstring'''




						lowerCamelCase__ 					=			pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model       ,			lowerCamelCase  )

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


						'''simple docstring'''




						lowerCamelCase__ 					=			pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layernorm       ,			pt_model.visual.ln_pre  )
						copy_linear(hf_model.vision_model.post_layernorm       ,			pt_model.visual.ln_post  )

						# copy embeds
						lowerCamelCase__ 					=			pt_model.visual.conva.weight.data
						lowerCamelCase__ 					=			pt_model.visual.class_embedding
						lowerCamelCase__ 					=			pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers       ,			pt_model.visual.transformer.resblocks  )

def      lowerCAmelCase_     (					lowerCamelCase:    List[Any]       ,			lowerCamelCase:    Optional[Any]  )   ->     Dict:


						'''simple docstring'''




						lowerCamelCase__ 					=			flatten_nested_dict(flax_params['backbone']['merged_class_token']  )

						lowerCamelCase__ 					=			torch.from_numpy(flax_class_token_params['scale']  )
						lowerCamelCase__ 					=			torch.from_numpy(flax_class_token_params['bias']  )
						lowerCamelCase__ 					=			nn.Parameter(lowerCamelCase  )
						lowerCamelCase__ 					=			nn.Parameter(lowerCamelCase  )

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


						'''simple docstring'''




						lowerCamelCase__ 					=			hf_model.state_dict()
						lowerCamelCase__ 					=			{}

						# Rename class prediction head flax params to pytorch HF
						lowerCamelCase__ 					=			flatten_nested_dict(flax_params['class_head']  )

						for flax_key, v in flax_class_params.items():
												lowerCamelCase__ 					=			flax_key.replace('/'       ,			'.'  )
												lowerCamelCase__ 					=			torch_key.replace('.kernel'       ,			'.weight'  )
												lowerCamelCase__ 					=			torch_key.replace('Dense_0'       ,			'dense0'  )
												lowerCamelCase__ 					=			'class_head.' + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		lowerCamelCase__ 					=			v.T

												lowerCamelCase__ 					=			nn.Parameter(torch.from_numpy(lowerCamelCase  )  )

						# Rename box prediction box flax params to pytorch HF
						lowerCamelCase__ 					=			flatten_nested_dict(flax_params['obj_box_head']  )

						for flax_key, v in flax_box_params.items():
												lowerCamelCase__ 					=			flax_key.replace('/'       ,			'.'  )
												lowerCamelCase__ 					=			torch_key.replace('.kernel'       ,			'.weight'  )
												lowerCamelCase__ 					=			torch_key.replace('_'       ,			''  ).lower()
												lowerCamelCase__ 					=			'box_head.' + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		lowerCamelCase__ 					=			v.T

												lowerCamelCase__ 					=			nn.Parameter(torch.from_numpy(lowerCamelCase  )  )

						# Copy flax params to PyTorch params
						for name, param in new_params.items():
												if name in pt_params.keys():
																		pt_params[name].copy_(lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    Any       ,			lowerCamelCase:    Dict  )   ->     str:


						'''simple docstring'''




						for k, v in flax_attn_params.items():
												if k.startswith('transformer'  ):
																		lowerCamelCase__ 					=			k.replace('transformer.resblocks'       ,			'text_model.encoder.layers'  )
												else:
																		lowerCamelCase__ 					=			k.replace('visual.transformer.resblocks'       ,			'vision_model.encoder.layers'  )

												lowerCamelCase__ 					=			torch_key.replace('attn'       ,			'self_attn'  )
												lowerCamelCase__ 					=			torch_key.replace('key'       ,			'k_proj'  )
												lowerCamelCase__ 					=			torch_key.replace('value'       ,			'v_proj'  )
												lowerCamelCase__ 					=			torch_key.replace('query'       ,			'q_proj'  )
												lowerCamelCase__ 					=			torch_key.replace('out'       ,			'out_proj'  )

												if "bias" in torch_key and v.ndim == 2:
																		lowerCamelCase__ 					=			v.shape[0] * v.shape[1]
																		lowerCamelCase__ 					=			v.reshape(lowerCamelCase  )

												if "weight" in torch_key and "out" in torch_key:
																		lowerCamelCase__ 					=			(v.shape[0] * v.shape[1], v.shape[2])
																		lowerCamelCase__ 					=			v.reshape(lowerCamelCase  ).T

												if "weight" in torch_key and "out" not in torch_key:
																		lowerCamelCase__ 					=			(v.shape[0], v.shape[1] * v.shape[2])
																		lowerCamelCase__ 					=			v.reshape(lowerCamelCase  ).T

												# Copy flax CLIP attn params to HF PyTorch params
												lowerCamelCase__ 					=			torch.from_numpy(lowerCamelCase  )
												hf_backbone.state_dict()[torch_key].copy_(lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    str  )   ->     Optional[Any]:


						'''simple docstring'''




						lowerCamelCase__ 					=			{}
						lowerCamelCase__ 					=			[]

						for k, v in params.items():
												if "attn." in k:
																		lowerCamelCase__ 					=			k[: k.rindex('attn.'  ) + 5]
																		if base in processed_attn_layers:
																								continue

																		processed_attn_layers.append(lowerCamelCase  )
																		lowerCamelCase__ 					=			params[base + 'out.weight'].shape[-1]
																		lowerCamelCase__ 					=			params[base + 'out.weight'].reshape(lowerCamelCase       ,			lowerCamelCase  ).T
																		lowerCamelCase__ 					=			params[base + 'out.bias']
												else:
																		lowerCamelCase__ 					=			v
						return new_params

def      lowerCAmelCase_     (					lowerCamelCase:    Tuple       ,			lowerCamelCase:    Any  )   ->     Dict:


						'''simple docstring'''




						lowerCamelCase__ 					=			CLIP(**lowerCamelCase  )
						torch_model.eval()
						lowerCamelCase__ 					=			torch_model.state_dict()

						lowerCamelCase__ 					=			flatten_nested_dict(flax_params['backbone']['clip']  )
						lowerCamelCase__ 					=			{}

						for flax_key, v in flax_clip_params.items():
												lowerCamelCase__ 					=			flax_key.replace('/'       ,			'.'  )
												lowerCamelCase__ 					=			torch_key.replace('text.token_embedding.embedding'       ,			'token_embedding.kernel'  )

												if (
												    torch_key.startswith('text.transformer'  )
												    or torch_key.startswith('text.text_projection'  )
												    or torch_key.startswith('text.ln_final'  )
												    or torch_key.startswith('text.positional_embedding'  )
												):
																		lowerCamelCase__ 					=			torch_key[5:]

												lowerCamelCase__ 					=			torch_key.replace('text_projection.kernel'       ,			'text_projection'  )
												lowerCamelCase__ 					=			torch_key.replace('visual.proj.kernel'       ,			'visual.proj'  )
												lowerCamelCase__ 					=			torch_key.replace('.scale'       ,			'.weight'  )
												lowerCamelCase__ 					=			torch_key.replace('.kernel'       ,			'.weight'  )

												if "conv" in torch_key or "downsample.0.weight" in torch_key:
																		lowerCamelCase__ 					=			v.transpose(3       ,			2       ,			0       ,			1  )

												elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
																		# Fully connected layers are transposed, embeddings are not
																		lowerCamelCase__ 					=			v.T

												lowerCamelCase__ 					=			v

						lowerCamelCase__ 					=			_convert_attn_layers(lowerCamelCase  )
						new_torch_params.update(lowerCamelCase  )
						lowerCamelCase__ 					=			{}

						# Copy flax CLIP backbone params to PyTorch params
						for name, param in new_torch_params.items():
												if name in torch_clip_params.keys():
																		lowerCamelCase__ 					=			torch.from_numpy(new_torch_params[name]  )
																		torch_clip_params[name].copy_(lowerCamelCase  )
												else:
																		lowerCamelCase__ 					=			param

						return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def      lowerCAmelCase_     (					lowerCamelCase:    str       ,			lowerCamelCase:    str       ,			lowerCamelCase:    str       ,			lowerCamelCase:    Any       ,			lowerCamelCase:    str=None  )   ->     Union[str, Any]:


						'''simple docstring'''




						lowerCamelCase__ 					=			Repository(lowerCamelCase       ,			clone_from=f"""google/{pytorch_dump_folder_path}"""  )
						repo.git_pull()

						if config_path is not None:
												lowerCamelCase__ 					=			OwlViTConfig.from_pretrained(lowerCamelCase  )
						else:
												lowerCamelCase__ 					=			OwlViTConfig()

						lowerCamelCase__ 					=			OwlViTModel(lowerCamelCase  ).eval()
						lowerCamelCase__ 					=			OwlViTForObjectDetection(lowerCamelCase  ).eval()

						copy_text_model_and_projection(lowerCamelCase       ,			lowerCamelCase  )
						copy_vision_model_and_projection(lowerCamelCase       ,			lowerCamelCase  )
						lowerCamelCase__ 					=			pt_backbone.logit_scale
						copy_flax_attn_params(lowerCamelCase       ,			lowerCamelCase  )

						lowerCamelCase__ 					=			hf_backbone
						copy_class_merge_token(lowerCamelCase       ,			lowerCamelCase  )
						copy_class_box_heads(lowerCamelCase       ,			lowerCamelCase  )

						# Save HF model
						hf_model.save_pretrained(repo.local_dir  )

						# Initialize image processor
						lowerCamelCase__ 					=			OwlViTImageProcessor(
						    size=config.vision_config.image_size       ,			crop_size=config.vision_config.image_size  )
						# Initialize tokenizer
						lowerCamelCase__ 					=			CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'       ,			pad_token='!'       ,			model_max_length=16  )

						# Initialize processor
						lowerCamelCase__ 					=			OwlViTProcessor(image_processor=lowerCamelCase       ,			tokenizer=lowerCamelCase  )
						image_processor.save_pretrained(repo.local_dir  )
						processor.save_pretrained(repo.local_dir  )

						repo.git_add()
						repo.git_commit('Upload model and processor'  )
						repo.git_push()


if __name__ == "__main__":
						__SCREAMING_SNAKE_CASE      :				Optional[Any]				      =argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--owlvit_version""",
						    default=None,
						    type=str,
						    required=True,
						    help="""OWL-ViT model name [clip_b16, clip_b32, clip_l14].""",
						)
						parser.add_argument(
						    """--owlvit_checkpoint""", default=None, type=str, required=True, help="""Path to flax model checkpoint."""
						)
						parser.add_argument("""--hf_config""", default=None, type=str, required=True, help="""Path to HF model config.""")
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default="""hf_model""", type=str, help="""Path to the output PyTorch model."""
						)
						__SCREAMING_SNAKE_CASE      :				Optional[int]				      =parser.parse_args()

						# Initialize PyToch clip model
						__SCREAMING_SNAKE_CASE      :				str				      =args.owlvit_version
						if model_name == "clip_b16":
												__SCREAMING_SNAKE_CASE      :				Optional[int]				      =CONFIGS["""vit_b16"""]
						elif model_name == "clip_b32":
												__SCREAMING_SNAKE_CASE      :				Optional[Any]				      =CONFIGS["""vit_b32"""]
						elif model_name == "clip_l14":
												__SCREAMING_SNAKE_CASE      :				List[str]				      =CONFIGS["""vit_l14"""]

						# Load from checkpoint and convert params to float-32
						__SCREAMING_SNAKE_CASE      :				Optional[int]				      =checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
						__SCREAMING_SNAKE_CASE      :				Tuple				      =jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
						del variables

						# Convert CLIP backbone
						__SCREAMING_SNAKE_CASE      :				Optional[int]				      =convert_clip_backbone(flax_params, torch_config)

						convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)





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