
"""simple docstring"""


# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
_lowercase											=			1e-10
_lowercase											=			'''This is incredible! I love it, this is the best chicken I have ever had.'''
_lowercase											=			1_00







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : List[str]					,A_       : int					,A_       : List[str]="gpt2-medium"					,A_       : str=False					,A_       : Optional[int]="cpu" )       ->				Union[str, Any]:
							super().__init__()
							A  				=				GPTaTokenizer.from_pretrained(A_ )
							A  				=				GPTaLMHeadModel.from_pretrained(A_ )
							A  				=				self.encoder.transformer.config.hidden_size
							A  				=				ClassificationHead(class_size=A_					,embed_size=self.embed_size )
							A  				=				cached_mode
							A  				=				device


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				List[Any]:
							return self.classifier_head


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Dict:
							for param in self.encoder.parameters():
								A  				=				False
							self.classifier_head.train()


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : Union[str, Any] )       ->				Optional[int]:
							A  				=				x.ne(0 ).unsqueeze(2 ).repeat(1					,1					,self.embed_size ).float().to(self.device ).detach()
							A  				=				self.encoder.transformer(A_ )['last_hidden_state']
							A  				=				hidden * mask
							A  				=				torch.sum(A_					,dim=1 ) / (torch.sum(A_					,dim=1 ).detach() + EPSILON)
							return avg_hidden




						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : Dict )       ->				str:
							if self.cached_mode:
								A  				=				x.to(self.device )
							else:
								A  				=				self.avg_representation(x.to(self.device ) )

							A  				=				self.classifier_head(A_ )
							A  				=				nn.functional.log_softmax(A_					,dim=-1 )

							return probs







class 			lowerCAmelCase_			( data.Dataset      ):



						'''simple docstring'''


						def __init__(	self       : Optional[Any]					,A_       : Union[str, Any]					,A_       : Union[str, Any] )       ->				Optional[Any]:
							A  				=				X
							A  				=				y


						def __len__(	self       : List[str] )       ->				str:
							return len(self.X )




						def __getitem__(	self       : Optional[int]					,A_       : List[str] )       ->				List[Any]:
							A  				=				{}
							A  				=				self.X[index]
							A  				=				self.y[index]
							return data






def  _snake_case			(						snake_case__				:       List[str]       ):
	def pad_sequences(snake_case__				:       str       ):
		A  				=				[len(snake_case__       ) for seq in sequences]

		A  				=				torch.zeros(len(snake_case__       )		, max(snake_case__       )       ).long()  # padding value = 0

		for i, seq in enumerate(snake_case__       ):
			A  				=				lengths[i]
			A  				=				seq[:end]

		return padded_sequences, lengths

	A  				=				{}
	for key in data[0].keys():
		A  				=				[d[key] for d in data]

	A				, A  				=				pad_sequences(item_info['X']       )
	A  				=				torch.tensor(item_info['y']		, dtype=torch.long       )

	return x_batch, y_batch






def  _snake_case			(						snake_case__				:       List[str]       ):
	A  				=				{}
	for key in data[0].keys():
		A  				=				[d[key] for d in data]

	A  				=				torch.cat(item_info['X']		, 0       )
	A  				=				torch.tensor(item_info['y']		, dtype=torch.long       )

	return x_batch, y_batch






def  _snake_case			(						snake_case__				:       Optional[int]		, snake_case__				:       str		, snake_case__				:       List[str]		, snake_case__				:       int=0		, snake_case__				:       Optional[Any]=10		, snake_case__				:       Union[str, Any]="cpu"       ):
	A  				=				0
	discriminator.train_custom()
	for batch_idx, (input_t, target_t) in enumerate(snake_case__       ):
		A				, A  				=				input_t.to(snake_case__       ), target_t.to(snake_case__       )

		optimizer.zero_grad()

		A  				=				discriminator(snake_case__       )
		A  				=				nn.functional.nll_loss(snake_case__		, snake_case__       )
		loss.backward(retain_graph=snake_case__       )
		optimizer.step()

		samples_so_far += len(snake_case__       )

		if batch_idx % log_interval == 0:
			print(
			    'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
			        epoch + 1		, snake_case__		, len(data_loader.dataset       )		, 100 * samples_so_far / len(data_loader.dataset       )		, loss.item()		, )       )






def  _snake_case			(						snake_case__				:       int		, snake_case__				:       str		, snake_case__				:       Optional[Any]="cpu"       ):
	discriminator.eval()
	A  				=				0
	A  				=				0
	with torch.no_grad():
		for input_t, target_t in data_loader:
			A				, A  				=				input_t.to(snake_case__       ), target_t.to(snake_case__       )
			A  				=				discriminator(snake_case__       )
			# sum up batch loss
			test_loss += nn.functional.nll_loss(snake_case__		, snake_case__		, reduction='sum'       ).item()
			# get the index of the max log-probability
			A  				=				output_t.argmax(dim=1		, keepdim=snake_case__       )
			correct += pred_t.eq(target_t.view_as(snake_case__       )       ).sum().item()

	test_loss /= len(data_loader.dataset       )

	print(
	    'Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
	        snake_case__		, snake_case__		, len(data_loader.dataset       )		, 100.0 * correct / len(data_loader.dataset       )       )       )






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       Union[str, Any]		, snake_case__				:       Union[str, Any]		, snake_case__				:       Optional[int]=False		, snake_case__				:       List[str]="cpu"       ):
	A  				=				model.tokenizer.encode(snake_case__       )
	A  				=				torch.tensor([input_t]		, dtype=torch.long		, device=snake_case__       )
	if cached:
		A  				=				model.avg_representation(snake_case__       )

	A  				=				model(snake_case__       ).data.cpu().numpy().flatten().tolist()
	print('Input sentence:'		, snake_case__       )
	print(
	    'Predictions:'		, ', '.join('{}: {:.4f}'.format(snake_case__		, math.exp(snake_case__       )       ) for c, log_prob in zip(snake_case__		, snake_case__       )       )		, )






def  _snake_case			(						snake_case__				:       Optional[Any]		, snake_case__				:       Optional[Any]		, snake_case__				:       Any		, snake_case__				:       Tuple=False		, snake_case__				:       Union[str, Any]="cpu"       ):
	A  				=				torch.utils.data.DataLoader(dataset=snake_case__		, batch_size=snake_case__		, collate_fn=snake_case__       )

	A  				=				[]
	A  				=				[]
	for batch_idx, (x, y) in enumerate(tqdm(snake_case__		, ascii=snake_case__       )       ):
		with torch.no_grad():
			A  				=				x.to(snake_case__       )
			A  				=				discriminator.avg_representation(snake_case__       ).cpu().detach()
			A  				=				torch.unbind(avg_rep.unsqueeze(1       )       )
			xs += avg_rep_list
			ys += y.cpu().numpy().tolist()

	A  				=				torch.utils.data.DataLoader(
	    dataset=Dataset(snake_case__		, snake_case__       )		, batch_size=snake_case__		, shuffle=snake_case__		, collate_fn=snake_case__       )

	return data_loader






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       Any=None		, snake_case__				:       Any="gpt2-medium"		, snake_case__				:       List[str]=10		, snake_case__				:       Dict=64		, snake_case__				:       Dict=10		, snake_case__				:       Optional[int]=False		, snake_case__				:       List[Any]=False		, snake_case__				:       List[Any]=False		, ):
	A  				=				'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

	print('Preprocessing {} dataset...'.format(snake_case__       )       )
	A  				=				time.time()

	if dataset == "SST":
		A  				=				['positive', 'negative', 'very positive', 'very negative', 'neutral']
		A  				=				{c: i for i, c in enumerate(snake_case__       )}

		A  				=				Discriminator(
		    class_size=len(snake_case__       )		, pretrained_model=snake_case__		, cached_mode=snake_case__		, device=snake_case__       ).to(snake_case__       )

		A  				=				torchtext_data.Field()
		A  				=				torchtext_data.Field(sequential=snake_case__       )
		A				, A				, A  				=				datasets.SST.splits(
		    snake_case__		, snake_case__		, fine_grained=snake_case__		, train_subtrees=snake_case__		, )

		A  				=				[]
		A  				=				[]
		for i in trange(len(snake_case__       )		, ascii=snake_case__       ):
			A  				=				TreebankWordDetokenizer().detokenize(vars(train_data[i]       )['text']       )
			A  				=				discriminator.tokenizer.encode(snake_case__       )
			A  				=				torch.tensor([5_0256] + seq		, device=snake_case__		, dtype=torch.long       )
			x.append(snake_case__       )
			y.append(classaidx[vars(train_data[i]       )['label']]       )
		A  				=				Dataset(snake_case__		, snake_case__       )

		A  				=				[]
		A  				=				[]
		for i in trange(len(snake_case__       )		, ascii=snake_case__       ):
			A  				=				TreebankWordDetokenizer().detokenize(vars(test_data[i]       )['text']       )
			A  				=				discriminator.tokenizer.encode(snake_case__       )
			A  				=				torch.tensor([5_0256] + seq		, device=snake_case__		, dtype=torch.long       )
			test_x.append(snake_case__       )
			test_y.append(classaidx[vars(test_data[i]       )['label']]       )
		A  				=				Dataset(snake_case__		, snake_case__       )

		A  				=				{
		    'class_size': len(snake_case__       ),
		    'embed_size': discriminator.embed_size,
		    'pretrained_model': pretrained_model,
		    'class_vocab': classaidx,
		    'default_class': 2,
		}

	elif dataset == "clickbait":
		A  				=				['non_clickbait', 'clickbait']
		A  				=				{c: i for i, c in enumerate(snake_case__       )}

		A  				=				Discriminator(
		    class_size=len(snake_case__       )		, pretrained_model=snake_case__		, cached_mode=snake_case__		, device=snake_case__       ).to(snake_case__       )

		with open('datasets/clickbait/clickbait_train_prefix.txt'       ) as f:
			A  				=				[]
			for i, line in enumerate(snake_case__       ):
				try:
					data.append(eval(snake_case__       )       )
				except Exception:
					print('Error evaluating line {}: {}'.format(snake_case__		, snake_case__       )       )
					continue
		A  				=				[]
		A  				=				[]
		with open('datasets/clickbait/clickbait_train_prefix.txt'       ) as f:
			for i, line in enumerate(tqdm(snake_case__		, ascii=snake_case__       )       ):
				try:
					A  				=				eval(snake_case__       )
					A  				=				discriminator.tokenizer.encode(d['text']       )

					if len(snake_case__       ) < max_length_seq:
						A  				=				torch.tensor([5_0256] + seq		, device=snake_case__		, dtype=torch.long       )
					else:
						print('Line {} is longer than maximum length {}'.format(snake_case__		, snake_case__       )       )
						continue
					x.append(snake_case__       )
					y.append(d['label']       )
				except Exception:
					print('Error evaluating / tokenizing line {}, skipping it'.format(snake_case__       )       )
					pass

		A  				=				Dataset(snake_case__		, snake_case__       )
		A  				=				int(0.9 * len(snake_case__       )       )
		A  				=				len(snake_case__       ) - train_size
		A				, A  				=				torch.utils.data.random_split(snake_case__		, [train_size, test_size]       )

		A  				=				{
		    'class_size': len(snake_case__       ),
		    'embed_size': discriminator.embed_size,
		    'pretrained_model': pretrained_model,
		    'class_vocab': classaidx,
		    'default_class': 1,
		}

	elif dataset == "toxic":
		A  				=				['non_toxic', 'toxic']
		A  				=				{c: i for i, c in enumerate(snake_case__       )}

		A  				=				Discriminator(
		    class_size=len(snake_case__       )		, pretrained_model=snake_case__		, cached_mode=snake_case__		, device=snake_case__       ).to(snake_case__       )

		A  				=				[]
		A  				=				[]
		with open('datasets/toxic/toxic_train.txt'       ) as f:
			for i, line in enumerate(tqdm(snake_case__		, ascii=snake_case__       )       ):
				try:
					A  				=				eval(snake_case__       )
					A  				=				discriminator.tokenizer.encode(d['text']       )

					if len(snake_case__       ) < max_length_seq:
						A  				=				torch.tensor([5_0256] + seq		, device=snake_case__		, dtype=torch.long       )
					else:
						print('Line {} is longer than maximum length {}'.format(snake_case__		, snake_case__       )       )
						continue
					x.append(snake_case__       )
					y.append(int(np.sum(d['label']       ) > 0       )       )
				except Exception:
					print('Error evaluating / tokenizing line {}, skipping it'.format(snake_case__       )       )
					pass

		A  				=				Dataset(snake_case__		, snake_case__       )
		A  				=				int(0.9 * len(snake_case__       )       )
		A  				=				len(snake_case__       ) - train_size
		A				, A  				=				torch.utils.data.random_split(snake_case__		, [train_size, test_size]       )

		A  				=				{
		    'class_size': len(snake_case__       ),
		    'embed_size': discriminator.embed_size,
		    'pretrained_model': pretrained_model,
		    'class_vocab': classaidx,
		    'default_class': 0,
		}

	else:  # if dataset == "generic":
		# This assumes the input dataset is a TSV with the following structure:
		# class \t text

		if dataset_fp is None:
			raise ValueError('When generic dataset is selected, dataset_fp needs to be specified aswell.'       )

		A  				=				set()
		with open(snake_case__       ) as f:
			A  				=				csv.reader(snake_case__		, delimiter='\t'       )
			for row in tqdm(snake_case__		, ascii=snake_case__       ):
				if row:
					classes.add(row[0]       )

		A  				=				sorted(snake_case__       )
		A  				=				{c: i for i, c in enumerate(snake_case__       )}

		A  				=				Discriminator(
		    class_size=len(snake_case__       )		, pretrained_model=snake_case__		, cached_mode=snake_case__		, device=snake_case__       ).to(snake_case__       )

		A  				=				[]
		A  				=				[]
		with open(snake_case__       ) as f:
			A  				=				csv.reader(snake_case__		, delimiter='\t'       )
			for i, row in enumerate(tqdm(snake_case__		, ascii=snake_case__       )       ):
				if row:
					A  				=				row[0]
					A  				=				row[1]

					try:
						A  				=				discriminator.tokenizer.encode(snake_case__       )
						if len(snake_case__       ) < max_length_seq:
							A  				=				torch.tensor([5_0256] + seq		, device=snake_case__		, dtype=torch.long       )

						else:
							print('Line {} is longer than maximum length {}'.format(snake_case__		, snake_case__       )       )
							continue

						x.append(snake_case__       )
						y.append(classaidx[label]       )

					except Exception:
						print('Error tokenizing line {}, skipping it'.format(snake_case__       )       )
						pass

		A  				=				Dataset(snake_case__		, snake_case__       )
		A  				=				int(0.9 * len(snake_case__       )       )
		A  				=				len(snake_case__       ) - train_size
		A				, A  				=				torch.utils.data.random_split(snake_case__		, [train_size, test_size]       )

		A  				=				{
		    'class_size': len(snake_case__       ),
		    'embed_size': discriminator.embed_size,
		    'pretrained_model': pretrained_model,
		    'class_vocab': classaidx,
		    'default_class': 0,
		}

	A  				=				time.time()
	print('Preprocessed {} data points'.format(len(snake_case__       ) + len(snake_case__       )       )       )
	print('Data preprocessing took: {:.3f}s'.format(end - start       )       )

	if cached:
		print('Building representation cache...'       )

		A  				=				time.time()

		A  				=				get_cached_data_loader(snake_case__		, snake_case__		, snake_case__		, shuffle=snake_case__		, device=snake_case__       )

		A  				=				get_cached_data_loader(snake_case__		, snake_case__		, snake_case__		, device=snake_case__       )

		A  				=				time.time()
		print('Building representation cache took: {:.3f}s'.format(end - start       )       )

	else:
		A  				=				torch.utils.data.DataLoader(
		    dataset=snake_case__		, batch_size=snake_case__		, shuffle=snake_case__		, collate_fn=snake_case__       )
		A  				=				torch.utils.data.DataLoader(dataset=snake_case__		, batch_size=snake_case__		, collate_fn=snake_case__       )

	if save_model:
		with open('{}_classifier_head_meta.json'.format(snake_case__       )		, 'w'       ) as meta_file:
			json.dump(snake_case__		, snake_case__       )

	A  				=				optim.Adam(discriminator.parameters()		, lr=0.0001       )

	for epoch in range(snake_case__       ):
		A  				=				time.time()
		print('\nEpoch'		, epoch + 1       )

		train_epoch(
		    discriminator=snake_case__		, data_loader=snake_case__		, optimizer=snake_case__		, epoch=snake_case__		, log_interval=snake_case__		, device=snake_case__		, )
		evaluate_performance(data_loader=snake_case__		, discriminator=snake_case__		, device=snake_case__       )

		A  				=				time.time()
		print('Epoch took: {:.3f}s'.format(end - start       )       )

		print('\nExample prediction'       )
		predict(snake_case__		, snake_case__		, snake_case__		, cached=snake_case__		, device=snake_case__       )

		if save_model:
			# torch.save(discriminator.state_dict(),
			#           "{}_discriminator_{}.pt".format(
			#               args.dataset, epoch + 1
			#               ))
			torch.save(
			    discriminator.get_classifier().state_dict()		, '{}_classifier_head_epoch_{}.pt'.format(snake_case__		, epoch + 1       )		, )


if __name__ == "__main__":
		_lowercase											=			argparse.ArgumentParser(description='''Train a discriminator on top of GPT-2 representations''')
		parser.add_argument(
		    '''--dataset''',
		    type=str,
		    default='''SST''',
		    choices=('''SST''', '''clickbait''', '''toxic''', '''generic'''),
		    help=(
		        '''dataset to train the discriminator on.'''
		        '''In case of generic, the dataset is expected'''
		        '''to be a TSBV file with structure: class \\t text'''
		    ),
		)
		parser.add_argument(
		    '''--dataset_fp''',
		    type=str,
		    default='''''',
		    help='''File path of the dataset to use. Needed only in case of generic datadset''',
		)
		parser.add_argument(
		    '''--pretrained_model''', type=str, default='''gpt2-medium''', help='''Pretrained model to use as encoder'''
		)
		parser.add_argument('''--epochs''', type=int, default=10, metavar='''N''', help='''Number of training epochs''')
		parser.add_argument(
		    '''--batch_size''', type=int, default=64, metavar='''N''', help='''input batch size for training (default: 64)'''
		)
		parser.add_argument(
		    '''--log_interval''',
		    type=int,
		    default=10,
		    metavar='''N''',
		    help='''how many batches to wait before logging training status''',
		)
		parser.add_argument('''--save_model''', action='''store_true''', help='''whether to save the model''')
		parser.add_argument('''--cached''', action='''store_true''', help='''whether to cache the input representations''')
		parser.add_argument('''--no_cuda''', action='''store_true''', help='''use to turn off cuda''')
		_lowercase											=			parser.parse_args()

		train_discriminator(**(vars(args)))

"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


_lowercase											=			logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      torch.FloatTensor







class 			lowerCAmelCase_			( _lowercase						,   _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      Dict =      1


						@register_to_config
						def __init__(	self       : List[Any]					,A_       : int = 40					,A_       : float = 0.0_02					,A_       : float = 80.0					,A_       : float = 0.5					,A_       : float = 1.0					,A_       : float = 7.0					,A_       : bool = True					,)       ->				List[str]:
							# standard deviation of the initial noise distribution
							A  				=				sigma_max

							A  				=				np.linspace(0					,1					,A_ )
							A  				=				self._convert_to_karras(A_ )
							A  				=				self.sigma_to_t(A_ )

							# setable values
							A  				=				None
							A  				=				torch.from_numpy(A_ )
							A  				=				torch.from_numpy(A_ )
							A  				=				False
							A  				=				False


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : List[Any]					,A_       : List[Any]=None )       ->				List[Any]:
							if schedule_timesteps is None:
								A  				=				self.timesteps

							A  				=				(schedule_timesteps == timestep).nonzero()
							return indices.item()


						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor] )       ->				torch.FloatTensor:
							# Get sigma corresponding to timestep
							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )
							A  				=				self.index_for_timestep(A_ )
							A  				=				self.sigmas[step_idx]

							A  				=				sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

							A  				=				True
							return sample


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : Union[float, np.ndarray] )       ->				Tuple:
							if not isinstance(A_					,np.ndarray ):
								A  				=				np.array(A_					,dtype=np.floataa )

							A  				=				1000 * 0.25 * np.log(sigmas + 1e-44 )

							return timesteps


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : Optional[int] = None					,A_       : Union[str, torch.device] = None					,A_       : Optional[List[int]] = None					,)       ->				Optional[Any]:
							if num_inference_steps is None and timesteps is None:
								raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.' )

							if num_inference_steps is not None and timesteps is not None:
								raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.' )

							# Follow DDPMScheduler custom timesteps logic
							if timesteps is not None:
								for i in range(1					,len(A_ ) ):
									if timesteps[i] >= timesteps[i - 1]:
										raise ValueError('`timesteps` must be in descending order.' )

								if timesteps[0] >= self.config.num_train_timesteps:
									raise ValueError(
									    F'`timesteps` must start before `self.config.train_timesteps`:'
									    F' {self.config.num_train_timesteps}.' )

								A  				=				np.array(A_					,dtype=np.intaa )
								A  				=				True
							else:
								if num_inference_steps > self.config.num_train_timesteps:
									raise ValueError(
									    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
									    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
									    F' maximal {self.config.num_train_timesteps} timesteps.' )

								A  				=				num_inference_steps

								A  				=				self.config.num_train_timesteps // self.num_inference_steps
								A  				=				(np.arange(0					,A_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
								A  				=				False

							# Map timesteps to Karras sigmas directly for multistep sampling
							# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
							A  				=				self.config.num_train_timesteps
							A  				=				timesteps[::-1].copy()
							A  				=				ramp / (num_train_timesteps - 1)
							A  				=				self._convert_to_karras(A_ )
							A  				=				self.sigma_to_t(A_ )

							A  				=				np.concatenate([sigmas, [self.sigma_min]] ).astype(np.floataa )
							A  				=				torch.from_numpy(A_ ).to(device=A_ )

							if str(A_ ).startswith('mps' ):
								# mps does not support float64
								A  				=				torch.from_numpy(A_ ).to(A_					,dtype=torch.floataa )
							else:
								A  				=				torch.from_numpy(A_ ).to(device=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Any:
							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_max

							A  				=				self.config.rho
							A  				=				sigma_min ** (1 / rho)
							A  				=				sigma_max ** (1 / rho)
							A  				=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : Dict )       ->				Any:
							A  				=				self.config.sigma_data

							A  				=				sigma_data**2 / (sigma**2 + sigma_data**2)
							A  				=				sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Dict:
							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_data

							A  				=				sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
							A  				=				(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor]					,A_       : torch.FloatTensor					,A_       : Optional[torch.Generator] = None					,A_       : bool = True					,)       ->				Union[CMStochasticIterativeSchedulerOutput, Tuple]:

							if (
							    isinstance(A_					,A_ )
							    or isinstance(A_					,torch.IntTensor )
							    or isinstance(A_					,torch.LongTensor )
							):
								raise ValueError(
								    (
								        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
								        F' `{self.__class__}.step()` is not supported. Make sure to pass'
								        ' one of the `scheduler.timesteps` as a timestep.'
								    )					,)

							if not self.is_scale_input_called:
								logger.warning(
								    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
								    'See `StableDiffusionPipeline` for a usage example.' )

							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )

							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_max

							A  				=				self.index_for_timestep(A_ )

							# sigma_next corresponds to next_t in original implementation
							A  				=				self.sigmas[step_index]
							if step_index + 1 < self.config.num_train_timesteps:
								A  				=				self.sigmas[step_index + 1]
							else:
								# Set sigma_next to sigma_min
								A  				=				self.sigmas[-1]

							# Get scalings for boundary conditions
							A				, A  				=				self.get_scalings_for_boundary_condition(A_ )

							# 1. Denoise model output using boundary conditions
							A  				=				c_out * model_output + c_skip * sample
							if self.config.clip_denoised:
								A  				=				denoised.clamp(-1					,1 )

							# 2. Sample z ~ N(0, s_noise^2 * I)
							# Noise is not used for onestep sampling.
							if len(self.timesteps ) > 1:
								A  				=				randn_tensor(
								    model_output.shape					,dtype=model_output.dtype					,device=model_output.device					,generator=A_ )
							else:
								A  				=				torch.zeros_like(A_ )
							A  				=				noise * self.config.s_noise

							A  				=				sigma_next.clamp(min=A_					,max=A_ )

							# 3. Return noisy sample
							# tau = sigma_hat, eps = sigma_min
							A  				=				denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

							if not return_dict:
								return (prev_sample,)

							return CMStochasticIterativeSchedulerOutput(prev_sample=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor					,)       ->				torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							A  				=				self.sigmas.to(device=original_samples.device					,dtype=original_samples.dtype )
							if original_samples.device.type == "mps" and torch.is_floating_point(A_ ):
								# mps does not support float64
								A  				=				self.timesteps.to(original_samples.device					,dtype=torch.floataa )
								A  				=				timesteps.to(original_samples.device					,dtype=torch.floataa )
							else:
								A  				=				self.timesteps.to(original_samples.device )
								A  				=				timesteps.to(original_samples.device )

							A  				=				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

							A  				=				sigmas[step_indices].flatten()
							while len(sigma.shape ) < len(original_samples.shape ):
								A  				=				sigma.unsqueeze(-1 )

							A  				=				original_samples + noise * sigma
							return noisy_samples




						def __len__(	self       : List[str] )       ->				List[Any]:
							return self.config.num_train_timesteps