





'''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


__lowerCAmelCase							:     Dict		     =logging.getLogger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : List[Any]	):
				# 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 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : Any=False	):
				A__    =      2
				if unlogit:
								A__    =      torch.pow(_lowerCamelCase						,	_lowerCamelCase	)
				A__    =      p * torch.log(_lowerCamelCase	)
				A__    =      0
				return -plogp.sum(dim=-1	)





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]	):
				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 						UpperCamelCase    (							_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : List[Any]						,	_lowerCamelCase : Any=True						,	_lowerCamelCase : Tuple=True						,	_lowerCamelCase : Tuple=None						,	_lowerCamelCase : Optional[int]=False	):
				A__,     A__    =      model.config.num_hidden_layers, model.config.num_attention_heads
				A__    =      torch.zeros(_lowerCamelCase						,	_lowerCamelCase	).to(args.device	)
				A__    =      torch.zeros(_lowerCamelCase						,	_lowerCamelCase	).to(args.device	)

				if head_mask is None:
								A__    =      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:
								A__    =      None

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

								# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
								A__    =      model(_lowerCamelCase						,	labels=_lowerCamelCase						,	head_mask=_lowerCamelCase	)
								#  (loss), lm_logits, presents, (all hidden_states), (attentions)
								A__,     A__,     A__    =      (
								    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	):
																A__    =      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:
								A__    =      2
								A__    =      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:
								A__    =      (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"	)
				A__    =      torch.zeros(head_importance.numel()						,	dtype=torch.long						,	device=args.device	)
				A__    =      torch.arange(
				    head_importance.numel()						,	device=args.device	)
				A__    =      head_ranks.view_as(_lowerCamelCase	)
				print_ad_tensor(_lowerCamelCase	)
				return attn_entropy, head_importance, total_loss





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : int						,	_lowerCamelCase : Dict	):
				A__,     A__,     A__    =      compute_heads_importance(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	compute_entropy=_lowerCamelCase	)
				A__    =      1 / loss  # instead of downsteam score use the LM loss
				logger.info("Pruning: original score: %f, threshold: %f"						,	_lowerCamelCase						,	original_score * args.masking_threshold	)

				A__    =      torch.ones_like(_lowerCamelCase	)
				A__    =      max(1						,	int(new_head_mask.numel() * args.masking_amount	)	)

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

								if len(_lowerCamelCase	) <= num_to_mask:
												print("BREAK BY num_to_mask"	)
												break

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

								# Compute metric and head importance again
								A__,     A__,     A__    =      compute_heads_importance(
								    _lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	compute_entropy=_lowerCamelCase						,	head_mask=_lowerCamelCase	)
								A__    =      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() * 1_00						,	)

				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 						UpperCamelCase    (							_lowerCamelCase : Optional[int]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : str						,	_lowerCamelCase : List[str]	):
				A__    =      datetime.now()
				A__,     A__,     A__    =      compute_heads_importance(
				    _lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	compute_entropy=_lowerCamelCase						,	compute_importance=_lowerCamelCase						,	head_mask=_lowerCamelCase	)
				A__    =      1 / loss
				A__    =      datetime.now() - before_time

				A__    =      sum(p.numel() for p in model.parameters()	)
				A__    =      {
				    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	):
												A__    =      [
												    v,
												]

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

				A__    =      datetime.now()
				A__,     A__,     A__    =      compute_heads_importance(
				    _lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	compute_entropy=_lowerCamelCase						,	compute_importance=_lowerCamelCase						,	head_mask=_lowerCamelCase						,	actually_pruned=_lowerCamelCase						,	)

				A__    =      1 / loss
				A__    =      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 * 1_00						,	)
				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 * 1_00	)
				save_model(_lowerCamelCase						,	args.output_dir	)





def 						UpperCamelCase    (							):
				A__    =      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=1_28						,	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."	)
				A__    =      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:
								A__    =      torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"	)
								A__    =      0 if args.no_cuda else torch.cuda.device_count()
				else:
								torch.cuda.set_device(args.local_rank	)
								A__    =      torch.device("cuda"						,	args.local_rank	)
								A__    =      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	)	)	)

				A__    =      GPTaLMHeadModel.from_pretrained(args.model_name_or_path	)

				# Distributed and parallel training
				model.to(args.device	)
				if args.local_rank != -1:
								A__    =      nn.parallel.DistributedDataParallel(
								    _lowerCamelCase						,	device_ids=[args.local_rank]						,	output_device=args.local_rank						,	find_unused_parameters=_lowerCamelCase	)
				elif args.n_gpu > 1:
								A__    =      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
				A__    =      np.concatenate(
				    [
				        np.loadtxt(args.data_dir						,	dtype=np.intaa	),
				    ]	)
				A__    =      (torch.from_numpy(_lowerCamelCase	),)
				A__    =      TensorDataset(*_lowerCamelCase	)
				A__    =      RandomSampler(_lowerCamelCase	)
				A__    =      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:
								A__    =      mask_heads(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
								prune_heads(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)


if __name__ == "__main__":
		main()










'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    (PNDMScheduler,)
 __lowercase       		=    (("""num_inference_steps""", 50),)
 def 					UpperCAmelCase_							(    self				:List[str]						,	**lowercase_				:Optional[int]							)->							List[str]:
     A__    =      {
         "num_train_timesteps": 10_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
     }

     config.update(**lowercase_							)
     return config
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[int]=0						,	**lowercase_				:Any							)->							int:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Optional[Any]=0						,	**lowercase_				:List[str]							)->							str:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

         # copy over dummy past residuals (must be after setting timesteps)
         A__    =      dummy_past_residuals[:]

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

             # copy over dummy past residual (must be after setting timesteps)
             A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	**lowercase_				:Dict							)->							Optional[int]:
     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(**lowercase_							)
     A__    =      scheduler_class(**lowercase_							)

     A__    =      10
     A__    =      self.dummy_model()
     A__    =      self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_							)

     for i, t in enumerate(scheduler.prk_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     return sample
 def 					UpperCAmelCase_							(    self				:int							)->							Union[str, Any]:
     A__    =      dict(self.forward_default_kwargs							)

     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)

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

         A__    =      self.dummy_sample
         A__    =      0.1 * sample

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

         # copy over dummy past residuals (must be done after set_timesteps)
         A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     for timesteps in [1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
     for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=lowercase_							)

     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(steps_offset=1							)
     A__    =      scheduler_class(**lowercase_							)
     scheduler.set_timesteps(10							)
     assert torch.equal(
         scheduler.timesteps						,	torch.LongTensor(
             [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]							)						,	)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Any:
     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]						,	[0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Tuple:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Tuple:
     for t in [1, 5, 10]:
         self.check_over_forward(time_step=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[int]:
     for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 1_00]							):
         self.check_over_forward(num_inference_steps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
     A__    =      27

     for scheduler_class in self.scheduler_classes:
         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.set_timesteps(lowercase_							)

         # before power of 3 fix, would error on first step, so we only need to do two
         for i, t in enumerate(scheduler.prk_timesteps[:2]							):
             A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     with self.assertRaises(lowercase_							):
         A__    =      self.scheduler_classes[0]
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample							).prev_sample
 def 					UpperCAmelCase_							(    self				:int							)->							str:
     A__    =      self.full_loop()
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_9_8.1_3_1_8							) < 1E-2
     assert abs(result_mean.item() - 0.2_5_8_0							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      self.full_loop(prediction_type="v_prediction"							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 6_7.3_9_8_6							) < 1E-2
     assert abs(result_mean.item() - 0.0_8_7_8							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 2_3_0.0_3_9_9							) < 1E-2
     assert abs(result_mean.item() - 0.2_9_9_5							) < 1E-3



 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_8_6.9_4_8_2							) < 1E-2
     assert abs(result_mean.item() - 0.2_4_3_4							) < 1E-3



