






import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   XCLIPTextConfig()

				# derive patch size from model name
				__magic_name__		:Union[str, Any]   =   model_name.find('''patch'''			)
				__magic_name__		:Optional[Any]   =   int(model_name[start_idx + len('''patch'''			) : start_idx + len('''patch'''			) + 2]			)
				__magic_name__		:int   =   XCLIPVisionConfig(patch_size=snake_case,	num_frames=snake_case			)

				if "large" in model_name:
								__magic_name__		:Dict   =   7_6_8
								__magic_name__		:int   =   3_0_7_2
								__magic_name__		:List[Any]   =   1_2

								__magic_name__		:str   =   1_0_2_4
								__magic_name__		:Any   =   4_0_9_6
								__magic_name__		:Optional[Any]   =   1_6
								__magic_name__		:Union[str, Any]   =   2_4
								__magic_name__		:Union[str, Any]   =   7_6_8
								__magic_name__		:Tuple   =   3_0_7_2

				if model_name == "xclip-large-patch14-16-frames":
								__magic_name__		:List[str]   =   3_3_6

				__magic_name__		:Any   =   XCLIPConfig.from_text_vision_configs(snake_case,	snake_case			)

				if "large" in model_name:
								__magic_name__		:str   =   7_6_8

				return config

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if name == "token_embedding.weight":
								__magic_name__		:Any   =   name.replace('''token_embedding.weight''',	'''text_model.embeddings.token_embedding.weight'''			)
				if name == "positional_embedding":
								__magic_name__		:Any   =   name.replace('''positional_embedding''',	'''text_model.embeddings.position_embedding.weight'''			)
				if "ln_1" in name:
								__magic_name__		:List[str]   =   name.replace('''ln_1''',	'''layer_norm1'''			)
				if "ln_2" in name:
								__magic_name__		:str   =   name.replace('''ln_2''',	'''layer_norm2'''			)
				if "c_fc" in name:
								__magic_name__		:List[Any]   =   name.replace('''c_fc''',	'''fc1'''			)
				if "c_proj" in name:
								__magic_name__		:Any   =   name.replace('''c_proj''',	'''fc2'''			)
				if name.startswith('''transformer.resblocks'''			):
								__magic_name__		:Any   =   name.replace('''transformer.resblocks''',	'''text_model.encoder.layers'''			)
				if "attn.out_proj" in name and "message" not in name:
								__magic_name__		:Union[str, Any]   =   name.replace('''attn.out_proj''',	'''self_attn.out_proj'''			)
				if "ln_final" in name:
								__magic_name__		:Tuple   =   name.replace('''ln_final''',	'''text_model.final_layer_norm'''			)
				# visual encoder
				if name == "visual.class_embedding":
								__magic_name__		:List[Any]   =   name.replace('''visual.class_embedding''',	'''vision_model.embeddings.class_embedding'''			)
				if name == "visual.positional_embedding":
								__magic_name__		:Any   =   name.replace('''visual.positional_embedding''',	'''vision_model.embeddings.position_embedding.weight'''			)
				if name.startswith('''visual.transformer.resblocks'''			):
								__magic_name__		:Union[str, Any]   =   name.replace('''visual.transformer.resblocks''',	'''vision_model.encoder.layers'''			)
				if "visual.conv1" in name:
								__magic_name__		:Tuple   =   name.replace('''visual.conv1''',	'''vision_model.embeddings.patch_embedding'''			)
				if "visual.ln_pre" in name:
								__magic_name__		:Tuple   =   name.replace('''visual.ln_pre''',	'''vision_model.pre_layernorm'''			)
				if "visual.ln_post" in name:
								__magic_name__		:Optional[Any]   =   name.replace('''visual.ln_post''',	'''vision_model.post_layernorm'''			)
				if "visual.proj" in name:
								__magic_name__		:Tuple   =   name.replace('''visual.proj''',	'''visual_projection.weight'''			)
				if "text_projection" in name:
								__magic_name__		:int   =   name.replace('''text_projection''',	'''text_projection.weight'''			)
				# things on top
				if "prompts_visual_proj" in name:
								__magic_name__		:int   =   name.replace('''prompts_visual_proj''',	'''prompts_visual_projection'''			)
				if "prompts_visual_ln" in name:
								__magic_name__		:Dict   =   name.replace('''prompts_visual_ln''',	'''prompts_visual_layernorm'''			)
				# mit
				if name == "mit.positional_embedding":
								__magic_name__		:List[Any]   =   name.replace('''positional''',	'''position'''			)
				if name.startswith('''mit.resblocks'''			):
								__magic_name__		:Union[str, Any]   =   name.replace('''mit.resblocks''',	'''mit.encoder.layers'''			)
				# prompts generator
				if name.startswith('''prompts_generator.norm'''			):
								__magic_name__		:str   =   name.replace('''prompts_generator.norm''',	'''prompts_generator.layernorm'''			)

				return name

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				for key in orig_state_dict.copy().keys():
								__magic_name__		:Any   =   orig_state_dict.pop(snake_case			)

								if "attn.in_proj" in key:
												__magic_name__		:str   =   key.split('''.'''			)
												if key.startswith('''visual'''			):
																__magic_name__		:List[Any]   =   key_split[3]
																__magic_name__		:List[Any]   =   config.vision_config.hidden_size
																if "message_attn" in key:
																				if "weight" in key:
																								__magic_name__		:List[Any]   =   val[
																								    :dim, :
																								]
																								__magic_name__		:List[str]   =   val[
																								    dim : dim * 2, :
																								]
																								__magic_name__		:List[str]   =   val[
																								    -dim:, :
																								]
																				else:
																								__magic_name__		:str   =   val[
																								    :dim
																								]
																								__magic_name__		:Optional[int]   =   val[
																								    dim : dim * 2
																								]
																								__magic_name__		:Any   =   val[
																								    -dim:
																								]
																else:
																				if "weight" in key:
																								__magic_name__		:int   =   val[
																								    :dim, :
																								]
																								__magic_name__		:Union[str, Any]   =   val[
																								    dim : dim * 2, :
																								]
																								__magic_name__		:List[Any]   =   val[
																								    -dim:, :
																								]
																				else:
																								__magic_name__		:Union[str, Any]   =   val[:dim]
																								__magic_name__		:str   =   val[
																								    dim : dim * 2
																								]
																								__magic_name__		:Dict   =   val[-dim:]
												elif key.startswith('''mit'''			):
																__magic_name__		:List[Any]   =   key_split[2]
																__magic_name__		:Any   =   config.vision_config.mit_hidden_size
																if "weight" in key:
																				__magic_name__		:Union[str, Any]   =   val[:dim, :]
																				__magic_name__		:Optional[int]   =   val[dim : dim * 2, :]
																				__magic_name__		:int   =   val[-dim:, :]
																else:
																				__magic_name__		:Tuple   =   val[:dim]
																				__magic_name__		:Optional[int]   =   val[dim : dim * 2]
																				__magic_name__		:Optional[int]   =   val[-dim:]
												else:
																__magic_name__		:Any   =   key_split[2]
																__magic_name__		:List[Any]   =   config.text_config.hidden_size
																if "weight" in key:
																				__magic_name__		:Union[str, Any]   =   val[:dim, :]
																				__magic_name__		:Tuple   =   val[
																				    dim : dim * 2, :
																				]
																				__magic_name__		:str   =   val[-dim:, :]
																else:
																				__magic_name__		:int   =   val[:dim]
																				__magic_name__		:Any   =   val[
																				    dim : dim * 2
																				]
																				__magic_name__		:str   =   val[-dim:]
								else:
												__magic_name__		:Tuple   =   rename_key(snake_case			)
												if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																__magic_name__		:List[Any]   =   val.T
												__magic_name__		:Optional[Any]   =   val

				return orig_state_dict

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if num_frames == 8:
								__magic_name__		:Any   =   '''eating_spaghetti_8_frames.npy'''
				elif num_frames == 1_6:
								__magic_name__		:List[Any]   =   '''eating_spaghetti.npy'''
				elif num_frames == 3_2:
								__magic_name__		:Tuple   =   '''eating_spaghetti_32_frames.npy'''
				__magic_name__		:str   =   hf_hub_download(
				    repo_id='''hf-internal-testing/spaghetti-video''',	filename=snake_case,	repo_type='''dataset''',	)
				__magic_name__		:List[Any]   =   np.load(snake_case			)
				return list(snake_case			)

def   __lowercase       (			snake_case,	snake_case=None,	snake_case=False			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   {
				    # fully supervised kinetics-400 checkpoints
				    '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
				    '''xclip-base-patch32-16-frames''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
				    ),
				    '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
				    '''xclip-base-patch16-16-frames''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
				    ),
				    '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
				    '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
				    # fully supervised kinetics-600 checkpoints
				    '''xclip-base-patch16-kinetics-600''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
				    ),
				    '''xclip-base-patch16-kinetics-600-16-frames''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
				    ),
				    '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
				    # few shot
				    '''xclip-base-patch16-hmdb-2-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
				    ),
				    '''xclip-base-patch16-hmdb-4-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
				    ),
				    '''xclip-base-patch16-hmdb-8-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
				    ),
				    '''xclip-base-patch16-hmdb-16-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
				    ),
				    '''xclip-base-patch16-ucf-2-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
				    ),
				    '''xclip-base-patch16-ucf-4-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
				    ),
				    '''xclip-base-patch16-ucf-8-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
				    ),
				    '''xclip-base-patch16-ucf-16-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
				    ),
				    # zero shot
				    '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
				}

				__magic_name__		:Optional[int]   =   model_to_url[model_name]
				__magic_name__		:List[str]   =   8
				if "16-frames" in model_name:
								__magic_name__		:List[Any]   =   1_6
				elif "shot" in model_name:
								__magic_name__		:Dict   =   3_2

				__magic_name__		:str   =   get_xclip_config(snake_case,	snake_case			)
				__magic_name__		:List[Any]   =   XCLIPModel(snake_case			)
				model.eval()

				if "drive" in checkpoint_url:
								__magic_name__		:Any   =   '''pytorch_model.bin'''
								gdown.cached_download(snake_case,	snake_case,	quiet=snake_case			)
								__magic_name__		:Optional[Any]   =   torch.load(snake_case,	map_location='''cpu'''			)['''model''']
				else:
								__magic_name__		:Optional[int]   =   torch.hub.load_state_dict_from_url(snake_case			)['''model''']

				__magic_name__		:List[str]   =   convert_state_dict(snake_case,	snake_case			)

				__magic_name__		:List[Any]   =   XCLIPModel(snake_case			)
				__magic_name__  ,					__magic_name__		:Optional[Any]   =   model.load_state_dict(snake_case,	strict=snake_case			)
				assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
				model.eval()

				__magic_name__		:str   =   3_3_6 if model_name == '''xclip-large-patch14-16-frames''' else 2_2_4
				__magic_name__		:Optional[int]   =   VideoMAEImageProcessor(size=snake_case			)
				__magic_name__		:Optional[int]   =   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''			)
				__magic_name__		:Tuple   =   CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''			)
				__magic_name__		:Optional[int]   =   XCLIPProcessor(image_processor=snake_case,	tokenizer=snake_case			)

				__magic_name__		:List[Any]   =   prepare_video(snake_case			)
				__magic_name__		:str   =   processor(
				    text=['''playing sports''', '''eating spaghetti''', '''go shopping'''],	videos=snake_case,	return_tensors='''pt''',	padding=snake_case			)

				print('''Shape of pixel values:''',	inputs.pixel_values.shape			)

				with torch.no_grad():
								__magic_name__		:Tuple   =   model(**snake_case			)

				# Verify outputs
				__magic_name__		:Any   =   outputs.logits_per_video
				__magic_name__		:str   =   logits_per_video.softmax(dim=1			)
				print('''Probs:''',	snake_case			)
				# kinetics-400
				if model_name == "xclip-base-patch32":
								__magic_name__		:Dict   =   torch.tensor([[0.0019, 0.9951, 0.0030]]			)
				elif model_name == "xclip-base-patch32-16-frames":
								__magic_name__		:str   =   torch.tensor([[7.0_9_9_9E-0_4, 9.9_8_8_3E-0_1, 4.5_5_8_0E-0_4]]			)
				elif model_name == "xclip-base-patch16":
								__magic_name__		:Tuple   =   torch.tensor([[0.0083, 0.9681, 0.0236]]			)
				elif model_name == "xclip-base-patch16-16-frames":
								__magic_name__		:Tuple   =   torch.tensor([[7.6_9_3_7E-0_4, 9.9_7_2_8E-0_1, 1.9_4_7_3E-0_3]]			)
				elif model_name == "xclip-large-patch14":
								__magic_name__		:str   =   torch.tensor([[0.0062, 0.9864, 0.0075]]			)
				elif model_name == "xclip-large-patch14-16-frames":
								__magic_name__		:Optional[int]   =   torch.tensor([[3.3_8_7_7E-0_4, 9.9_9_3_7E-0_1, 2.8_8_8_8E-0_4]]			)
				# kinetics-600
				elif model_name == "xclip-base-patch16-kinetics-600":
								__magic_name__		:Optional[int]   =   torch.tensor([[0.0555, 0.8914, 0.0531]]			)
				elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
								__magic_name__		:List[str]   =   torch.tensor([[3.8_5_5_4E-0_4, 9.9_9_2_9E-0_1, 3.2_7_5_4E-0_4]]			)
				elif model_name == "xclip-large-patch14-kinetics-600":
								__magic_name__		:List[str]   =   torch.tensor([[0.0036, 0.9920, 0.0045]]			)
				# few shot
				elif model_name == "xclip-base-patch16-hmdb-2-shot":
								__magic_name__		:Tuple   =   torch.tensor([[7.1_8_9_0E-0_6, 9.9_9_9_4E-0_1, 5.6_5_5_9E-0_5]]			)
				elif model_name == "xclip-base-patch16-hmdb-4-shot":
								__magic_name__		:List[str]   =   torch.tensor([[1.0_3_2_0E-0_5, 9.9_9_9_3E-0_1, 6.2_4_3_5E-0_5]]			)
				elif model_name == "xclip-base-patch16-hmdb-8-shot":
								__magic_name__		:Optional[int]   =   torch.tensor([[4.1_3_7_7E-0_6, 9.9_9_9_0E-0_1, 9.8_3_8_6E-0_5]]			)
				elif model_name == "xclip-base-patch16-hmdb-16-shot":
								__magic_name__		:Optional[int]   =   torch.tensor([[4.1_3_4_7E-0_5, 9.9_9_6_2E-0_1, 3.3_4_1_1E-0_4]]			)
				elif model_name == "xclip-base-patch16-ucf-2-shot":
								__magic_name__		:Union[str, Any]   =   torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]			)
				elif model_name == "xclip-base-patch16-ucf-4-shot":
								__magic_name__		:Union[str, Any]   =   torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]			)
				elif model_name == "xclip-base-patch16-ucf-8-shot":
								__magic_name__		:Optional[int]   =   torch.tensor([[0.0027, 0.9904, 0.0070]]			)
				elif model_name == "xclip-base-patch16-ucf-16-shot":
								__magic_name__		:Any   =   torch.tensor([[9.8_2_1_9E-0_4, 9.9_5_9_3E-0_1, 3.0_8_6_3E-0_3]]			)
				# zero shot
				elif model_name == "xclip-base-patch16-zero-shot":
								__magic_name__		:Optional[int]   =   torch.tensor([[3.5_0_8_2E-0_4, 9.9_7_8_5E-0_1, 1.7_9_6_6E-0_3]]			)
				else:
								raise ValueError(f'''Model name {model_name} not supported'''			)
				assert torch.allclose(snake_case,	snake_case,	atol=1E-3			)
				print('''Looks ok!'''			)

				if pytorch_dump_folder_path is not None:
								print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''			)
								model.save_pretrained(snake_case			)

				if push_to_hub:
								print('''Pushing model, processor and slow tokenizer files to the hub...'''			)
								model.push_to_hub(snake_case,	organization='''nielsr'''			)
								processor.push_to_hub(snake_case,	organization='''nielsr'''			)
								slow_tokenizer.push_to_hub(snake_case,	organization='''nielsr'''			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""xclip-base-patch32""",
					    type=str,
					    help="""Name of the model.""",
					)
					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 or not to push the converted model to the 🤗 hub."""
					)

					SCREAMING_SNAKE_CASE__   :      List[Any]				=		parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					int    		=	(DDPMParallelScheduler,)







	def  snake_case__     (     self      :Any   ,     **lowercase      :str			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {
				    '''num_train_timesteps''': 1_0_0_0,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**lowercase			)
				return config







	def  snake_case__     (     self      :Dict			)		->      List[Any]:







				"""simple docstring"""






				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,     [0.0_02, 0.02, 0.2, 2]			):
							self.check_over_configs(beta_start=lowercase   ,     beta_end=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[str]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Any			)		->      List[Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :str			)		->      str:







				"""simple docstring"""






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







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







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[str]			)		->      Optional[int]:







				"""simple docstring"""






				for t in [0, 5_0_0, 9_9_9]:
							self.check_over_forward(time_step=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.0_09_79			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.02			)			) < 1e-5







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter + 0.1
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter - 0.1

				SCREAMING_SNAKE_CASE     						= samplea.shape[0]
				SCREAMING_SNAKE_CASE     						= torch.stack([samplea, samplea, samplea]   ,     dim=0			)
				SCREAMING_SNAKE_CASE     						= torch.arange(lowercase			)[0:3, None].repeat(1   ,     lowercase			)

				SCREAMING_SNAKE_CASE     						= model(samples.flatten(0   ,     1			)   ,     timesteps.flatten(0   ,     1			)			)
				SCREAMING_SNAKE_CASE     						= scheduler.batch_step_no_noise(lowercase   ,     timesteps.flatten(0   ,     1			)   ,     samples.flatten(0   ,     1			)			)

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 11_53.18_33			) < 1e-2
				assert abs(result_mean.item() - 0.50_05			) < 1e-3







	def  snake_case__     (     self      :str			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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

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

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

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_58.96_06			) < 1e-2
				assert abs(result_mean.item() - 0.33_72			) < 1e-3







	def  snake_case__     (     self      :int			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config(prediction_type='''v_prediction'''			)
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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

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

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

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_02.02_96			) < 1e-2
				assert abs(result_mean.item() - 0.26_31			) < 1e-3







	def  snake_case__     (     self      :Optional[int]			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]

				scheduler.set_timesteps(timesteps=lowercase			)

				SCREAMING_SNAKE_CASE     						= scheduler.timesteps

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

							SCREAMING_SNAKE_CASE     						= scheduler.previous_timestep(lowercase			)
							SCREAMING_SNAKE_CASE     						= prev_t.item()

							self.assertEqual(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 5_1, 0]

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







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]
				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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







	def  snake_case__     (     self      :str			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [scheduler.config.num_train_timesteps]

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