
"""simple docstring"""


import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


A_    							=					logging.getLogger(__name__)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			# save results
			if os.path.exists(lowerCAmelCase__						):
						if os.path.exists(os.path.join(lowerCAmelCase__	,'''config.json'''						)						) and os.path.isfile(
						    os.path.join(lowerCAmelCase__	,'''config.json'''						)						):
									os.remove(os.path.join(lowerCAmelCase__	,'''config.json'''						)						)
						if os.path.exists(os.path.join(lowerCAmelCase__	,'''pytorch_model.bin'''						)						) and os.path.isfile(
						    os.path.join(lowerCAmelCase__	,'''pytorch_model.bin'''						)						):
									os.remove(os.path.join(lowerCAmelCase__	,'''pytorch_model.bin'''						)						)
			else:
						os.makedirs(lowerCAmelCase__						)
			model.save_pretrained(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  2
			if unlogit:
						lowerCamelCase_					=  torch.pow(lowerCAmelCase__	,lowerCAmelCase__						)
			lowerCamelCase_					=  p * torch.log(lowerCAmelCase__						)
			lowerCamelCase_					=  0
			return -plogp.sum(dim=-1						)






def  lowercase						(	lowerCAmelCase__						):
			logger.info('''lv, h >\t''' + '''\t'''.join(f"{x + 1}" for x in range(len(lowerCAmelCase__						)						)						)						)
			for row in range(len(lowerCAmelCase__						)						):
						if tensor.dtype != torch.long:
									logger.info(f"layer {row + 1}:\t" + '''\t'''.join(f"{x:.5f}" for x in tensor[row].cpu().data						)						)
						else:
									logger.info(f"layer {row + 1}:\t" + '''\t'''.join(f"{x:d}" for x in tensor[row].cpu().data						)						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=True	,lowerCAmelCase__=True	,lowerCAmelCase__=None	,lowerCAmelCase__=False						):
			lowerCamelCase_		, lowerCamelCase_					=  model.config.num_hidden_layers, model.config.num_attention_heads
			lowerCamelCase_					=  torch.zeros(lowerCAmelCase__	,lowerCAmelCase__						).to(args.device						)
			lowerCamelCase_					=  torch.zeros(lowerCAmelCase__	,lowerCAmelCase__						).to(args.device						)

			if head_mask is None:
						lowerCamelCase_					=  torch.ones(lowerCAmelCase__	,lowerCAmelCase__						).to(args.device						)

			head_mask.requires_grad_(requires_grad=lowerCAmelCase__						)
			# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
			if actually_pruned:
						lowerCamelCase_					=  None

			lowerCamelCase_					=  0.0
			lowerCamelCase_					=  0.0
			for step, inputs in enumerate(tqdm(lowerCAmelCase__	,desc='''Iteration'''	,disable=args.local_rank not in [-1, 0]						)						):
						lowerCamelCase_					=  tuple(t.to(args.device						) for t in inputs						)
						((lowerCamelCase_)		, )					=  inputs

						# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
						lowerCamelCase_					=  model(lowerCAmelCase__	,labels=lowerCAmelCase__	,head_mask=lowerCAmelCase__						)
						#  (loss), lm_logits, presents, (all hidden_states), (attentions)
						lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  (
						    outputs[0],
						    outputs[1],
						    outputs[-1],
						)  # Loss and logits are the first, attention the last
						loss.backward()  # Backpropagate to populate the gradients in the head mask
						total_loss += loss.detach().cpu().numpy()
						if compute_entropy:
									for layer, attn in enumerate(lowerCAmelCase__						):
												lowerCamelCase_					=  entropy(attn.detach()	,lowerCAmelCase__						)
												attn_entropy[layer] += masked_entropy.sum(-1						).sum(0						).sum(0						).detach()

						if compute_importance:
									head_importance += head_mask.grad.abs().detach()
						tot_tokens += torch.ones_like(lowerCAmelCase__						).float().detach().sum().data

			# Normalize
			attn_entropy /= tot_tokens
			head_importance /= tot_tokens
			# Layerwise importance normalization
			if not args.dont_normalize_importance_by_layer:
						lowerCamelCase_					=  2
						lowerCamelCase_					=  torch.pow(torch.pow(lowerCAmelCase__	,lowerCAmelCase__						).sum(-1						)	,1 / exponent						)
						head_importance /= norm_by_layer.unsqueeze(-1						) + 1E-20

			if not args.dont_normalize_global_importance:
						lowerCamelCase_					=  (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

			# Print matrices
			if compute_entropy:
						logger.info('''Attention entropies'''						)
						print_ad_tensor(lowerCAmelCase__						)
			if compute_importance:
						logger.info('''Head importance scores'''						)
						print_ad_tensor(lowerCAmelCase__						)
			logger.info('''Head ranked by importance scores'''						)
			lowerCamelCase_					=  torch.zeros(head_importance.numel()	,dtype=torch.long	,device=args.device						)
			lowerCamelCase_					=  torch.arange(
			    head_importance.numel()	,device=args.device						)
			lowerCamelCase_					=  head_ranks.view_as(lowerCAmelCase__						)
			print_ad_tensor(lowerCAmelCase__						)
			return attn_entropy, head_importance, total_loss






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  compute_heads_importance(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,compute_entropy=lowerCAmelCase__						)
			lowerCamelCase_					=  1 / loss  # instead of downsteam score use the LM loss
			logger.info('''Pruning: original score: %f, threshold: %f'''	,lowerCAmelCase__	,original_score * args.masking_threshold						)

			lowerCamelCase_					=  torch.ones_like(lowerCAmelCase__						)
			lowerCamelCase_					=  max(1	,int(new_head_mask.numel() * args.masking_amount						)						)

			lowerCamelCase_					=  original_score
			while current_score >= original_score * args.masking_threshold:
						lowerCamelCase_					=  new_head_mask.clone().detach()  # save current head mask
						# heads from least important to most - keep only not-masked heads
						lowerCamelCase_					=  float('''Inf'''						)
						lowerCamelCase_					=  head_importance.view(-1						).sort()[1]

						if len(lowerCAmelCase__						) <= num_to_mask:
									print('''BREAK BY num_to_mask'''						)
									break

						# mask heads
						lowerCamelCase_					=  current_heads_to_mask[:num_to_mask]
						logger.info('''Heads to mask: %s'''	,str(current_heads_to_mask.tolist()						)						)
						lowerCamelCase_					=  new_head_mask.view(-1						)
						lowerCamelCase_					=  0.0
						lowerCamelCase_					=  new_head_mask.view_as(lowerCAmelCase__						)
						lowerCamelCase_					=  new_head_mask.clone().detach()
						print_ad_tensor(lowerCAmelCase__						)

						# Compute metric and head importance again
						lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  compute_heads_importance(
						    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,compute_entropy=lowerCAmelCase__	,head_mask=lowerCAmelCase__						)
						lowerCamelCase_					=  1 / loss
						logger.info(
						    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''	,lowerCAmelCase__	,new_head_mask.sum()	,new_head_mask.sum() / new_head_mask.numel() * 100	,)

			logger.info('''Final head mask'''						)
			print_ad_tensor(lowerCAmelCase__						)
			np.save(os.path.join(args.output_dir	,'''head_mask.npy'''						)	,head_mask.detach().cpu().numpy()						)

			return head_mask






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  datetime.now()
			lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  compute_heads_importance(
			    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,compute_entropy=lowerCAmelCase__	,compute_importance=lowerCAmelCase__	,head_mask=lowerCAmelCase__						)
			lowerCamelCase_					=  1 / loss
			lowerCamelCase_					=  datetime.now() - before_time

			lowerCamelCase_					=  sum(p.numel() for p in model.parameters()						)
			lowerCamelCase_					=  {
			    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCAmelCase__						)						)
			}

			for k, v in heads_to_prune.items():
						if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
									lowerCamelCase_					=  [
									    v,
									]

			assert sum(len(lowerCAmelCase__						) for h in heads_to_prune.values()						) == (1 - head_mask.long()).sum().item()
			model.prune_heads(lowerCAmelCase__						)
			lowerCamelCase_					=  sum(p.numel() for p in model.parameters()						)

			lowerCamelCase_					=  datetime.now()
			lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  compute_heads_importance(
			    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,compute_entropy=lowerCAmelCase__	,compute_importance=lowerCAmelCase__	,head_mask=lowerCAmelCase__	,actually_pruned=lowerCAmelCase__	,)

			lowerCamelCase_					=  1 / loss
			lowerCamelCase_					=  datetime.now() - before_time

			logger.info(
			    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''	,lowerCAmelCase__	,lowerCAmelCase__	,pruned_num_params / original_num_params * 100	,)
			logger.info('''Pruning: score with masking: %f score with pruning: %f'''	,lowerCAmelCase__	,lowerCAmelCase__						)
			logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''	,original_time / new_time * 100						)
			save_model(lowerCAmelCase__	,args.output_dir						)






def  lowercase						(	):
			lowerCamelCase_					=  argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--data_dir'''	,default=lowerCAmelCase__	,type=lowerCAmelCase__	,required=lowerCAmelCase__	,help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''	,)
			parser.add_argument(
			    '''--model_name_or_path'''	,default=lowerCAmelCase__	,type=lowerCAmelCase__	,required=lowerCAmelCase__	,help='''Path to pretrained model or model identifier from huggingface.co/models'''	,)
			parser.add_argument(
			    '''--output_dir'''	,default=lowerCAmelCase__	,type=lowerCAmelCase__	,required=lowerCAmelCase__	,help='''The output directory where the model predictions and checkpoints will be written.'''	,)

			# Other parameters
			parser.add_argument(
			    '''--config_name'''	,default=''''''	,type=lowerCAmelCase__	,help='''Pretrained config name or path if not the same as model_name_or_path'''	,)
			parser.add_argument(
			    '''--tokenizer_name'''	,default=''''''	,type=lowerCAmelCase__	,help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''	,)
			parser.add_argument(
			    '''--cache_dir'''	,default=lowerCAmelCase__	,type=lowerCAmelCase__	,help='''Where do you want to store the pre-trained models downloaded from s3'''	,)
			parser.add_argument(
			    '''--data_subset'''	,type=lowerCAmelCase__	,default=-1	,help='''If > 0: limit the data to a subset of data_subset instances.'''						)
			parser.add_argument(
			    '''--overwrite_output_dir'''	,action='''store_true'''	,help='''Whether to overwrite data in output directory'''						)
			parser.add_argument(
			    '''--overwrite_cache'''	,action='''store_true'''	,help='''Overwrite the cached training and evaluation sets'''						)

			parser.add_argument(
			    '''--dont_normalize_importance_by_layer'''	,action='''store_true'''	,help='''Don\'t normalize importance score by layers'''						)
			parser.add_argument(
			    '''--dont_normalize_global_importance'''	,action='''store_true'''	,help='''Don\'t normalize all importance scores between 0 and 1'''	,)

			parser.add_argument(
			    '''--try_masking'''	,action='''store_true'''	,help='''Whether to try to mask head until a threshold of accuracy.'''						)
			parser.add_argument(
			    '''--masking_threshold'''	,default=0.9	,type=lowerCAmelCase__	,help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''	,)
			parser.add_argument(
			    '''--masking_amount'''	,default=0.1	,type=lowerCAmelCase__	,help='''Amount to heads to masking at each masking step.'''						)
			parser.add_argument('''--metric_name'''	,default='''acc'''	,type=lowerCAmelCase__	,help='''Metric to use for head masking.'''						)

			parser.add_argument(
			    '''--max_seq_length'''	,default=128	,type=lowerCAmelCase__	,help=(
			        '''The maximum total input sequence length after WordPiece tokenization. \n'''
			        '''Sequences longer than this will be truncated, sequences shorter padded.'''
			    )	,)
			parser.add_argument('''--batch_size'''	,default=1	,type=lowerCAmelCase__	,help='''Batch size.'''						)

			parser.add_argument('''--seed'''	,type=lowerCAmelCase__	,default=42						)
			parser.add_argument('''--local_rank'''	,type=lowerCAmelCase__	,default=-1	,help='''local_rank for distributed training on gpus'''						)
			parser.add_argument('''--no_cuda'''	,action='''store_true'''	,help='''Whether not to use CUDA when available'''						)
			parser.add_argument('''--server_ip'''	,type=lowerCAmelCase__	,default=''''''	,help='''Can be used for distant debugging.'''						)
			parser.add_argument('''--server_port'''	,type=lowerCAmelCase__	,default=''''''	,help='''Can be used for distant debugging.'''						)
			lowerCamelCase_					=  parser.parse_args()

			if args.server_ip and args.server_port:
						# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
						import ptvsd

						print('''Waiting for debugger attach'''						)
						ptvsd.enable_attach(address=(args.server_ip, args.server_port)	,redirect_output=lowerCAmelCase__						)
						ptvsd.wait_for_attach()

			# Setup devices and distributed training
			if args.local_rank == -1 or args.no_cuda:
						lowerCamelCase_					=  torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''						)
						lowerCamelCase_					=  0 if args.no_cuda else torch.cuda.device_count()
			else:
						torch.cuda.set_device(args.local_rank						)
						lowerCamelCase_					=  torch.device('''cuda'''	,args.local_rank						)
						lowerCamelCase_					=  1
						torch.distributed.init_process_group(backend='''nccl'''						)  # Initializes the distributed backend

			# Setup logging
			logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN						)
			logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device	,args.n_gpu	,bool(args.local_rank != -1						)						)						)

			lowerCamelCase_					=  GPTaLMHeadModel.from_pretrained(args.model_name_or_path						)

			# Distributed and parallel training
			model.to(args.device						)
			if args.local_rank != -1:
						lowerCamelCase_					=  nn.parallel.DistributedDataParallel(
						    lowerCAmelCase__	,device_ids=[args.local_rank]	,output_device=args.local_rank	,find_unused_parameters=lowerCAmelCase__						)
			elif args.n_gpu > 1:
						lowerCamelCase_					=  nn.DataParallel(lowerCAmelCase__						)

			# Print/save training arguments
			os.makedirs(args.output_dir	,exist_ok=lowerCAmelCase__						)
			torch.save(lowerCAmelCase__	,os.path.join(args.output_dir	,'''run_args.bin'''						)						)
			logger.info('''Training/evaluation parameters %s'''	,lowerCAmelCase__						)

			# Prepare dataset
			lowerCamelCase_					=  np.concatenate(
			    [
			        np.loadtxt(args.data_dir	,dtype=np.intaa						),
			    ]						)
			lowerCamelCase_					=  (torch.from_numpy(lowerCAmelCase__						),)
			lowerCamelCase_					=  TensorDataset(*lowerCAmelCase__						)
			lowerCamelCase_					=  RandomSampler(lowerCAmelCase__						)
			lowerCamelCase_					=  DataLoader(lowerCAmelCase__	,sampler=lowerCAmelCase__	,batch_size=args.batch_size						)

			# Compute head entropy and importance score
			compute_heads_importance(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)

			# Try head masking (set heads to zero until the score goes under a threshole)
			# and head pruning (remove masked heads and see the effect on the network)
			if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
						lowerCamelCase_					=  mask_heads(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)
						prune_heads(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)


if __name__ == "__main__":
						main()





"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Any          =    (DDPMScheduler,)




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  {
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0_0_0_1,
							    '''beta_end''': 0.0_2,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCAmelCase   )
							return config




				def        UpperCAmelCase__						(				self   ):
							for timesteps in [1, 5, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,						[0.0_0_2, 0.0_2, 0.2, 2]   ):
										self.check_over_configs(beta_start=UpperCAmelCase							,						beta_end=UpperCAmelCase   )




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




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




				def        UpperCAmelCase__						(				self   ):
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=UpperCAmelCase   )




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




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




				def        UpperCAmelCase__						(				self   ):
							for t in [0, 500, 999]:
										self.check_over_forward(time_step=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0_9_7_9   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.0_2   )   ) < 1e-5




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  len(UpperCAmelCase   )

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

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

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

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										lowerCamelCase_					=  pred_prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
							assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config(prediction_type='''v_prediction'''   )
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  len(UpperCAmelCase   )

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

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

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

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										lowerCamelCase_					=  pred_prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
							assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=UpperCAmelCase   )

							lowerCamelCase_					=  scheduler.timesteps

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

										lowerCamelCase_					=  scheduler.previous_timestep(UpperCAmelCase   )
										lowerCamelCase_					=  prev_t.item()

										self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 51, 0]

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 1, 0]
							lowerCamelCase_					=  len(UpperCAmelCase   )

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





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [scheduler.config.num_train_timesteps]

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



