


'''simple docstring'''
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		)
		from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class    lowercase	(							__snake_case					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=13    ,   UpperCamelCase_=7    ,   UpperCamelCase_=True    ,   UpperCamelCase_=True    ,   UpperCamelCase_=True    ,   UpperCamelCase_=True    ,   UpperCamelCase_=99    ,   UpperCamelCase_=32    ,   UpperCamelCase_=5    ,   UpperCamelCase_=4    ,   UpperCamelCase_=37    ,   UpperCamelCase_="gelu"    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=512    ,   UpperCamelCase_=16    ,   UpperCamelCase_=2    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=False    ,   UpperCamelCase_=True    ,   UpperCamelCase_="None"    ,   UpperCamelCase_=3    ,   UpperCamelCase_=4    ,   UpperCamelCase_=None    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= parent
							UpperCamelCase__   :Tuple												= batch_size
							UpperCamelCase__   :Union[str, Any]												= seq_length
							UpperCamelCase__   :List[Any]												= is_training
							UpperCamelCase__   :Optional[int]												= use_input_mask
							UpperCamelCase__   :List[Any]												= use_token_type_ids
							UpperCamelCase__   :Optional[int]												= use_labels
							UpperCamelCase__   :str												= vocab_size
							UpperCamelCase__   :int												= hidden_size
							UpperCamelCase__   :List[Any]												= num_hidden_layers
							UpperCamelCase__   :Optional[int]												= num_attention_heads
							UpperCamelCase__   :Optional[int]												= intermediate_size
							UpperCamelCase__   :Optional[int]												= hidden_act
							UpperCamelCase__   :Tuple												= hidden_dropout_prob
							UpperCamelCase__   :Tuple												= attention_probs_dropout_prob
							UpperCamelCase__   :Optional[int]												= max_position_embeddings
							UpperCamelCase__   :str												= type_vocab_size
							UpperCamelCase__   :List[str]												= type_sequence_label_size
							UpperCamelCase__   :Optional[int]												= initializer_range
							UpperCamelCase__   :Any												= num_labels
							UpperCamelCase__   :Dict												= num_choices
							UpperCamelCase__   :Any												= relative_attention
							UpperCamelCase__   :List[str]												= position_biased_input
							UpperCamelCase__   :Dict												= pos_att_type
							UpperCamelCase__   :List[str]												= scope




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size							)

							UpperCamelCase__   :Union[str, Any]												= None
							if self.use_input_mask:
												UpperCamelCase__   :Dict												= ids_tensor([self.batch_size, self.seq_length]    ,   vocab_size=2							)

							UpperCamelCase__   :Optional[int]												= None
							if self.use_token_type_ids:
												UpperCamelCase__   :List[Any]												= ids_tensor([self.batch_size, self.seq_length]    ,   self.type_vocab_size							)

							UpperCamelCase__   :str												= None
							UpperCamelCase__   :Optional[Any]												= None
							UpperCamelCase__   :Optional[Any]												= None
							if self.use_labels:
												UpperCamelCase__   :Union[str, Any]												= ids_tensor([self.batch_size]    ,   self.type_sequence_label_size							)
												UpperCamelCase__   :Any												= ids_tensor([self.batch_size, self.seq_length]    ,   self.num_labels							)
												UpperCamelCase__   :Any												= ids_tensor([self.batch_size]    ,   self.num_choices							)

							UpperCamelCase__   :Any												= self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return DebertaConfig(
							    vocab_size=self.vocab_size    ,   hidden_size=self.hidden_size    ,   num_hidden_layers=self.num_hidden_layers    ,   num_attention_heads=self.num_attention_heads    ,   intermediate_size=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   max_position_embeddings=self.max_position_embeddings    ,   type_vocab_size=self.type_vocab_size    ,   initializer_range=self.initializer_range    ,   relative_attention=self.relative_attention    ,   position_biased_input=self.position_biased_input    ,   pos_att_type=self.pos_att_type    ,   )




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.get_config()
							UpperCamelCase__   :Optional[int]												= 300
							return config




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							self.parent.assertListEqual(list(result.loss.size()							)    ,   []							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :int												= DebertaModel(config=a_							)
							model.to(a_							)
							model.eval()
							UpperCamelCase__   :str												= model(a_    ,   attention_mask=a_    ,   token_type_ids=a_							)[0]
							UpperCamelCase__   :Optional[int]												= model(a_    ,   token_type_ids=a_							)[0]
							UpperCamelCase__   :Optional[Any]												= model(a_							)[0]

							self.parent.assertListEqual(list(sequence_output.size()							)    ,   [self.batch_size, self.seq_length, self.hidden_size]							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= DebertaForMaskedLM(config=a_							)
							model.to(a_							)
							model.eval()
							UpperCamelCase__   :int												= model(a_    ,   attention_mask=a_    ,   token_type_ids=a_    ,   labels=a_							)

							self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.vocab_size)							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self.num_labels
							UpperCamelCase__   :str												= DebertaForSequenceClassification(a_							)
							model.to(a_							)
							model.eval()
							UpperCamelCase__   :Dict												= model(a_    ,   attention_mask=a_    ,   token_type_ids=a_    ,   labels=a_							)
							self.parent.assertListEqual(list(result.logits.size()							)    ,   [self.batch_size, self.num_labels]							)
							self.check_loss_output(a_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self.num_labels
							UpperCamelCase__   :Dict												= DebertaForTokenClassification(config=a_							)
							model.to(a_							)
							model.eval()
							UpperCamelCase__   :List[str]												= model(a_    ,   attention_mask=a_    ,   token_type_ids=a_    ,   labels=a_							)
							self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.num_labels)							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= DebertaForQuestionAnswering(config=a_							)
							model.to(a_							)
							model.eval()
							UpperCamelCase__   :int												= model(
							    a_    ,   attention_mask=a_    ,   token_type_ids=a_    ,   start_positions=a_    ,   end_positions=a_    ,   )
							self.parent.assertEqual(result.start_logits.shape    ,   (self.batch_size, self.seq_length)							)
							self.parent.assertEqual(result.end_logits.shape    ,   (self.batch_size, self.seq_length)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self.prepare_config_and_inputs()
							(
							    UpperCamelCase__							
							)   :List[str]												= config_and_inputs
							UpperCamelCase__   :str												= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
							return config, inputs_dict



@require_torch
class    lowercase	(							__snake_case	,		__snake_case	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				(
		    (
		        DebertaModel,
		        DebertaForMaskedLM,
		        DebertaForSequenceClassification,
		        DebertaForTokenClassification,
		        DebertaForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		_a     =				(
		    {
		        'feature-extraction': DebertaModel,
		        'fill-mask': DebertaForMaskedLM,
		        'question-answering': DebertaForQuestionAnswering,
		        'text-classification': DebertaForSequenceClassification,
		        'token-classification': DebertaForTokenClassification,
		        'zero-shot': DebertaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)

		_a     =				True
		_a     =				False
		_a     =				False
		_a     =				False
		_a     =				False




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= DebertaModelTester(self							)
							UpperCamelCase__   :Optional[Any]												= ConfigTester(self    ,   config_class=a_    ,   hidden_size=37							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							self.config_tester.run_common_tests()




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*a_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*a_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*a_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*a_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*a_							)




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__   :Optional[int]												= DebertaModel.from_pretrained(a_							)
												self.assertIsNotNone(a_							)



@require_torch
@require_sentencepiece
@require_tokenizers
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		@unittest.skip(reason='''Model not available yet'''							)
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							pass




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= DebertaModel.from_pretrained('''microsoft/deberta-base'''							)

							UpperCamelCase__   :Optional[int]												= torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							)
							UpperCamelCase__   :List[Any]												= torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
							with torch.no_grad():
												UpperCamelCase__   :List[str]												= model(a_    ,   attention_mask=a_							)[0]
							# compare the actual values for a slice.
							UpperCamelCase__   :Union[str, Any]												= torch.tensor(
							    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]							)
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,   a_    ,   atol=1e-4							)    ,   F'''{output[:, 1:4, 1:4]}'''							)



"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


SCREAMING_SNAKE_CASE :    List[str]									=				logging.get_logger(__name__)  # pylint: disable=invalid-name



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       ):





			'''simple docstring'''

			super().__init__()
			self.register_modules(
			    vae=a_				,       text_encoder=a_				,       tokenizer=a_				,       unet=a_				,       scheduler=a_				,       safety_checker=a_				,       feature_extractor=a_				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_ = "auto"					):





			'''simple docstring'''

			if slice_size == "auto":
				# half the attention head size is usually a good trade-off between
				# speed and memory
				__snake_case	:						Optional[int]						=				self.unet.config.attention_head_dim // 2
			self.unet.set_attention_slice(a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.enable_attention_slicing(a_					)



		@torch.no_grad()
		def __call__(self				,       a_				,       a_ = 5_12				,       a_ = 5_12				,       a_ = 50				,       a_ = 7.5				,       a_ = None				,       a_ = 1				,       a_ = 0.0				,       a_ = None				,       a_ = None				,       a_ = "pil"				,       a_ = True				,       a_ = None				,       a_ = 1				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			if isinstance(a_				,       a_					):
				__snake_case	:						Any						=				1
			elif isinstance(a_				,       a_					):
				__snake_case	:						Any						=				len(a_					)
			else:
				raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a_					)}"""					)

			if height % 8 != 0 or width % 8 != 0:
				raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""					)

			if (callback_steps is None) or (
			    callback_steps is not None and (not isinstance(a_				,       a_					) or callback_steps <= 0)
			):
				raise ValueError(
				    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
				    f""" {type(a_					)}."""					)

			# get prompt text embeddings
			__snake_case	:						int						=				self.tokenizer(
			    a_				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       return_tensors='''pt'''				,       )
			__snake_case	:						int						=				text_inputs.input_ids

			if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
				__snake_case	:						List[Any]						=				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
				logger.warning(
				    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
				    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""					)
				__snake_case	:						Optional[int]						=				text_input_ids[:, : self.tokenizer.model_max_length]

			if text_embeddings is None:
				__snake_case	:						Optional[int]						=				self.text_encoder(text_input_ids.to(self.device					)					)[0]

			# duplicate text embeddings for each generation per prompt, using mps friendly method
			__snake_case	,							__snake_case	,							__snake_case	:						Union[str, Any]						=				text_embeddings.shape
			__snake_case	:						Optional[int]						=				text_embeddings.repeat(1				,       a_				,       1					)
			__snake_case	:						Dict						=				text_embeddings.view(bs_embed * num_images_per_prompt				,       a_				,       -1					)

			# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
			# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
			# corresponds to doing no classifier free guidance.
			__snake_case	:						Dict						=				guidance_scale > 1.0
			# get unconditional embeddings for classifier free guidance
			if do_classifier_free_guidance:
				__snake_case	:						List[str]
				if negative_prompt is None:
					__snake_case	:						List[Any]						=				['''''']
				elif type(a_					) is not type(a_					):
					raise TypeError(
					    f"""`negative_prompt` should be the same type to `prompt`, but got {type(a_					)} !="""
					    f""" {type(a_					)}."""					)
				elif isinstance(a_				,       a_					):
					__snake_case	:						List[str]						=				[negative_prompt]
				elif batch_size != len(a_					):
					raise ValueError(
					    f"""`negative_prompt`: {negative_prompt} has batch size {len(a_					)}, but `prompt`:"""
					    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
					    ''' the batch size of `prompt`.'''					)
				else:
					__snake_case	:						Optional[int]						=				negative_prompt

				__snake_case	:						Optional[int]						=				text_input_ids.shape[-1]
				__snake_case	:						List[Any]						=				self.tokenizer(
				    a_				,       padding='''max_length'''				,       max_length=a_				,       truncation=a_				,       return_tensors='''pt'''				,       )
				__snake_case	:						str						=				self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

				# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
				__snake_case	:						str						=				uncond_embeddings.shape[1]
				__snake_case	:						int						=				uncond_embeddings.repeat(a_				,       a_				,       1					)
				__snake_case	:						int						=				uncond_embeddings.view(batch_size * num_images_per_prompt				,       a_				,       -1					)

				# For classifier free guidance, we need to do two forward passes.
				# Here we concatenate the unconditional and text embeddings into a single batch
				# to avoid doing two forward passes
				__snake_case	:						Any						=				torch.cat([uncond_embeddings, text_embeddings]					)

			# get the initial random noise unless the user supplied it

			# Unlike in other pipelines, latents need to be generated in the target device
			# for 1-to-1 results reproducibility with the CompVis implementation.
			# However this currently doesn't work in `mps`.
			__snake_case	:						Any						=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
			__snake_case	:						Dict						=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
			__snake_case	:						Dict						=				text_embeddings.dtype
			if latents is None:
				if self.device.type == "mps":
					# randn does not exist on mps
					__snake_case	:						Union[str, Any]						=				torch.randn(
					    a_				,       generator=a_				,       device='''cpu'''				,       dtype=a_					).to(self.device					)
					__snake_case	:						Tuple						=				torch.randn(a_				,       generator=a_				,       device='''cpu'''				,       dtype=a_					).to(
					    self.device					)
				else:
					__snake_case	:						Dict						=				torch.randn(
					    a_				,       generator=a_				,       device=self.device				,       dtype=a_					)
					__snake_case	:						Dict						=				torch.randn(a_				,       generator=a_				,       device=self.device				,       dtype=a_					)
			else:
				if latents_reference.shape != latents_shape:
					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""					)
				__snake_case	:						Union[str, Any]						=				latents_reference.to(self.device					)
				__snake_case	:						Dict						=				latents.to(self.device					)

			# This is the key part of the pipeline where we
			# try to ensure that the generated images w/ the same seed
			# but different sizes actually result in similar images
			__snake_case	:						int						=				(latents_shape[3] - latents_shape_reference[3]) // 2
			__snake_case	:						Tuple						=				(latents_shape[2] - latents_shape_reference[2]) // 2
			__snake_case	:						Optional[int]						=				latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
			__snake_case	:						Union[str, Any]						=				latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
			__snake_case	:						int						=				0 if dx < 0 else dx
			__snake_case	:						Union[str, Any]						=				0 if dy < 0 else dy
			__snake_case	:						str						=				max(-dx				,       0					)
			__snake_case	:						Tuple						=				max(-dy				,       0					)
			# import pdb
			# pdb.set_trace()
			__snake_case	:						Any						=				latents_reference[:, :, dy : dy + h, dx : dx + w]

			# set timesteps
			self.scheduler.set_timesteps(a_					)

			# Some schedulers like PNDM have timesteps as arrays
			# It's more optimized to move all timesteps to correct device beforehand
			__snake_case	:						Optional[Any]						=				self.scheduler.timesteps.to(self.device					)

			# scale the initial noise by the standard deviation required by the scheduler
			__snake_case	:						List[Any]						=				latents * self.scheduler.init_noise_sigma

			# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
			# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
			# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
			# and should be between [0, 1]
			__snake_case	:						Any						=				'''eta''' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
			__snake_case	:						Tuple						=				{}
			if accepts_eta:
				__snake_case	:						List[str]						=				eta

			for i, t in enumerate(self.progress_bar(a_					)					):
				# expand the latents if we are doing classifier free guidance
				__snake_case	:						str						=				torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
				__snake_case	:						Tuple						=				self.scheduler.scale_model_input(a_				,       a_					)

				# predict the noise residual
				__snake_case	:						int						=				self.unet(a_				,       a_				,       encoder_hidden_states=a_					).sample

				# perform guidance
				if do_classifier_free_guidance:
					__snake_case	,							__snake_case	:						Tuple						=				noise_pred.chunk(2					)
					__snake_case	:						str						=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

				# compute the previous noisy sample x_t -> x_t-1
				__snake_case	:						Optional[Any]						=				self.scheduler.step(a_				,       a_				,       a_				,       **a_					).prev_sample

				# call the callback, if provided
				if callback is not None and i % callback_steps == 0:
					callback(a_				,       a_				,       a_					)

			__snake_case	:						Union[str, Any]						=				1 / 0.1_8215 * latents
			__snake_case	:						Optional[Any]						=				self.vae.decode(a_					).sample

			__snake_case	:						Union[str, Any]						=				(image / 2 + 0.5).clamp(0				,       1					)

			# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
			__snake_case	:						Union[str, Any]						=				image.cpu().permute(0				,       2				,       3				,       1					).float().numpy()

			if self.safety_checker is not None:
				__snake_case	:						Optional[int]						=				self.feature_extractor(self.numpy_to_pil(a_					)				,       return_tensors='''pt'''					).to(
				    self.device					)
				__snake_case	,							__snake_case	:						List[Any]						=				self.safety_checker(
				    images=a_				,       clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype					)					)
			else:
				__snake_case	:						Union[str, Any]						=				None

			if output_type == "pil":
				__snake_case	:						Union[str, Any]						=				self.numpy_to_pil(a_					)

			if not return_dict:
				return (image, has_nsfw_concept)

			return StableDiffusionPipelineOutput(images=a_				,       nsfw_content_detected=a_					)





