


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a : str		=				logging.get_logger(__name__)


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=False  ):


			'''simple docstring'''







			__lowercase 					=   []

			# fmt: off
			# stem:
			rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')  )
			rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')  )

			rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')  )
			rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')  )

			# backbone
			rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')  )
			rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')  )
			rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')  )

			for stage_idx in range(len(config.backbone_config.depths  )  ):
						for layer_idx in range(config.backbone_config.depths[stage_idx]  ):
									rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')  )
									rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')  )
									rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')  )
									rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')  )
									rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')  )
									rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')  )
									rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')  )
									rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')  )
									rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')  )

						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')  )
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')  )
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')  )

			# transformer encoder
			for i in range(config.num_hidden_layers  ):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight')  )
						rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias')  )
						rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight')  )
						rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias')  )
						rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight')  )
						rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias')  )
						rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight')  )
						rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias')  )
						rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight')  )
						rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias')  )

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ('''norm.weight''', '''layernorm.weight'''),
						        ('''norm.bias''', '''layernorm.bias'''),
						        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
						        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
						    ]  )

						# if just the base model, we should remove "vit" from all keys that start with "vit"
						__lowercase 					=   [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''  ) else pair for pair in rename_keys]
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ('''norm.weight''', '''vit.layernorm.weight'''),
						        ('''norm.bias''', '''vit.layernorm.bias'''),
						        ('''head.weight''', '''classifier.weight'''),
						        ('''head.bias''', '''classifier.bias'''),
						    ]  )
			# fmt: on

			return rename_keys


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase=False  ):


			'''simple docstring'''







			for i in range(config.num_hidden_layers  ):
						if base_model:
									__lowercase 					=   ''''''
						else:
									__lowercase 					=   '''vit.'''
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						__lowercase 					=   state_dict.pop(F'blocks.{i}.attn.qkv.weight'  )
						__lowercase 					=   state_dict.pop(F'blocks.{i}.attn.qkv.bias'  )
						# next, add query, keys and values (in that order) to the state dict
						__lowercase 					=   in_proj_weight[
						    : config.hidden_size, :
						]
						__lowercase 					=   in_proj_bias[: config.hidden_size]
						__lowercase 					=   in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						__lowercase 					=   in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						__lowercase 					=   in_proj_weight[
						    -config.hidden_size :, :
						]
						__lowercase 					=   in_proj_bias[-config.hidden_size :]


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   ['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(_UpperCamelCase       ,			_UpperCamelCase  )


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   dct.pop(_UpperCamelCase  )
			__lowercase 					=   val


def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowercase 					=   Image.open(requests.get(_UpperCamelCase       ,			stream=_UpperCamelCase  ).raw  )
			return im


@torch.no_grad()
def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase=False  ):


			'''simple docstring'''







			__lowercase 					=   BitConfig(
			    global_padding='''same'''       ,			layer_type='''bottleneck'''       ,			depths=(3, 4, 9)       ,			out_features=['''stage3''']       ,			embedding_dynamic_padding=_UpperCamelCase       ,			)
			__lowercase 					=   ViTHybridConfig(backbone_config=_UpperCamelCase       ,			image_size=3_84       ,			num_labels=10_00  )
			__lowercase 					=   False

			# load original model from timm
			__lowercase 					=   timm.create_model(_UpperCamelCase       ,			pretrained=_UpperCamelCase  )
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			__lowercase 					=   timm_model.state_dict()
			if base_model:
						remove_classification_head_(_UpperCamelCase  )
			__lowercase 					=   create_rename_keys(_UpperCamelCase       ,			_UpperCamelCase  )
			for src, dest in rename_keys:
						rename_key(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )
			read_in_q_k_v(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

			__lowercase 					=   '''huggingface/label-files'''
			__lowercase 					=   '''imagenet-1k-id2label.json'''
			__lowercase 					=   json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type='''dataset'''  )       ,			'''r'''  )  )
			__lowercase 					=   {int(_UpperCamelCase  ): v for k, v in idalabel.items()}
			__lowercase 					=   idalabel
			__lowercase 					=   {v: k for k, v in idalabel.items()}

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						__lowercase 					=   ViTHybridModel(_UpperCamelCase  ).eval()
			else:
						__lowercase 					=   ViTHybridForImageClassification(_UpperCamelCase  ).eval()
			model.load_state_dict(_UpperCamelCase  )

			# create image processor
			__lowercase 					=   create_transform(**resolve_data_config({}       ,			model=_UpperCamelCase  )  )
			__lowercase 					=   transform.transforms

			__lowercase 					=   {
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

			__lowercase 					=   ViTHybridImageProcessor(
			    do_resize=_UpperCamelCase       ,			size={'''shortest_edge''': timm_transforms[0].size}       ,			resample=pillow_resamplings[timm_transforms[0].interpolation.value]       ,			do_center_crop=_UpperCamelCase       ,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}       ,			do_normalize=_UpperCamelCase       ,			image_mean=timm_transforms[-1].mean.tolist()       ,			image_std=timm_transforms[-1].std.tolist()       ,			)

			__lowercase 					=   prepare_img()
			__lowercase 					=   transform(_UpperCamelCase  ).unsqueeze(0  )
			__lowercase 					=   processor(_UpperCamelCase       ,			return_tensors='''pt'''  ).pixel_values

			# verify pixel values
			assert torch.allclose(_UpperCamelCase       ,			_UpperCamelCase  )

			# verify logits
			with torch.no_grad():
						__lowercase 					=   model(_UpperCamelCase  )
						__lowercase 					=   outputs.logits

			print('''Predicted class:'''       ,			logits.argmax(-1  ).item()  )
			if base_model:
						__lowercase 					=   timm_model.forward_features(_UpperCamelCase  )
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(_UpperCamelCase       ,			outputs.pooler_output       ,			atol=1E-3  )
			else:
						__lowercase 					=   timm_model(_UpperCamelCase  )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(_UpperCamelCase       ,			outputs.logits       ,			atol=1E-3  )
			print('''Looks ok!'''  )

			if pytorch_dump_folder_path is not None:
						Path(_UpperCamelCase  ).mkdir(exist_ok=_UpperCamelCase  )
						print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'  )
						model.save_pretrained(_UpperCamelCase  )
						print(F'Saving processor to {pytorch_dump_folder_path}'  )
						processor.save_pretrained(_UpperCamelCase  )

			if push_to_hub:
						print(F'Pushing model and processor to the hub {vit_name}'  )
						model.push_to_hub(F'ybelkada/{vit_name}'  )
						processor.push_to_hub(F'ybelkada/{vit_name}'  )


if __name__ == "__main__":
	a : Optional[int]		=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--vit_name''',
	    default='''vit_base_r50_s16_384''',
	    type=str,
	    help='''Name of the hybrid ViT timm model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
	)

	a : Tuple		=				parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				(DEISMultistepScheduler,)
					__UpperCAmelCase            =				(("num_inference_steps", 25),)






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								}

								config.update(**snake_case_						)
								return config






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								pass






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config()
											__lowercase 					=   scheduler_class(**snake_case_						)
											scheduler.set_timesteps(snake_case_						)

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

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

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

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample
											__lowercase 					=   new_scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample

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






					def A      (				self ,       snake_case_=None ,       **snake_case_						)   ->			Optional[int]:







								'''simple docstring'''

								if scheduler is None:
											__lowercase 					=   self.scheduler_classes[0]
											__lowercase 					=   self.get_scheduler_config(**snake_case_						)
											__lowercase 					=   scheduler_class(**snake_case_						)

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

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter
								scheduler.set_timesteps(snake_case_						)

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

								return sample






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)

								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config()
											__lowercase 					=   scheduler_class(**snake_case_						)

											__lowercase 					=   self.dummy_sample
											__lowercase 					=   0.1 * sample

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

											# copy over dummy past residuals (must be done after set_timesteps)
											__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
											__lowercase 					=   dummy_past_residuals[: scheduler.config.solver_order]

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

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample
											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample

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






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   DEISMultistepScheduler(**self.get_scheduler_config()						)
								__lowercase 					=   self.full_loop(scheduler=snake_case_						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3

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

								__lowercase 					=   self.full_loop(scheduler=snake_case_						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=snake_case_						)






					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

								self.check_over_configs(thresholding=snake_case_						)
								for order in [1, 2, 3]:
											for solver_type in ["logrho"]:
														for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    thresholding=snake_case_ ,       prediction_type=snake_case_ ,       sample_max_value=snake_case_ ,       algorithm_type='''deis''' ,       solver_order=snake_case_ ,       solver_type=snake_case_ ,       )






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

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






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								for algorithm_type in ["deis"]:
											for solver_type in ["logrho"]:
														for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    solver_order=snake_case_ ,       solver_type=snake_case_ ,       prediction_type=snake_case_ ,       algorithm_type=snake_case_ ,       )
																				__lowercase 					=   self.full_loop(
																				    solver_order=snake_case_ ,       solver_type=snake_case_ ,       prediction_type=snake_case_ ,       algorithm_type=snake_case_ ,       )
																				assert not torch.isnan(snake_case_						).any(), "Samples have nan numbers"






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								self.check_over_configs(lower_order_final=snake_case_						)
								self.check_over_configs(lower_order_final=snake_case_						)






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								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=snake_case_ ,       time_step=0						)






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   self.full_loop()
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(prediction_type='''v_prediction'''						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.0_9_1						) < 1e-3






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(thresholding=snake_case_ ,       dynamic_thresholding_ratio=0						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter.half()
								scheduler.set_timesteps(snake_case_						)

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

								assert sample.dtype == torch.floataa


