

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)
def 		SCREAMING_SNAKE_CASE__					(				_UpperCAmelCase					)       ->			Optional[Any]:



    '''simple docstring'''






    lowerCAmelCase					:      Union[str, Any]           =   torch.exp(_UpperCAmelCase					)
    lowerCAmelCase					:      List[str]           =   torch.sum(_UpperCAmelCase,   dim=1					)  # sum of exp(x_i)
    lowerCAmelCase					:      Optional[int]           =   torch.sum(x * exp_x,   dim=1					)  # sum of x_i * exp(x_i)
    return torch.log(_UpperCAmelCase					) - B / A






class  __A					(				nn.Module			):






    def __init__(						self      :     List[str]     ,					UpperCAmelCase_      :     Union[str, Any]     ):
        super().__init__()
        lowerCAmelCase					:      Dict           =   config.output_attentions
        lowerCAmelCase					:      List[Any]           =   config.output_hidden_states
        lowerCAmelCase					:      Any           =   nn.ModuleList([BertLayer(a_     ) for _ in range(config.num_hidden_layers     )]     )
        lowerCAmelCase					:      Union[str, Any]           =   nn.ModuleList([BertHighway(a_     ) for _ in range(config.num_hidden_layers     )]     )

        lowerCAmelCase					:      Union[str, Any]           =   [-1 for _ in range(config.num_hidden_layers     )]






    def 						lowercase__			(						self      :     List[str]     ,					UpperCAmelCase_      :     List[Any]     ):
        if (type(a_     ) is float) or (type(a_     ) is int):
            for i in range(len(self.early_exit_entropy     )     ):
                lowerCAmelCase					:      List[str]           =   x
        else:
            lowerCAmelCase					:      List[Any]           =   x






    def 						lowercase__			(						self      :     Any     ,					UpperCAmelCase_      :     Dict     ):
        lowerCAmelCase					:      str           =   pooler.state_dict()
        for highway in self.highway:
            for name, param in highway.pooler.state_dict().items():
                param.copy_(loaded_model[name]     )






    def 						lowercase__			(						self      :     List[str]     ,					UpperCAmelCase_      :     int     ,					UpperCAmelCase_      :     Any=None     ,					UpperCAmelCase_      :     Optional[Any]=None     ,					UpperCAmelCase_      :     Optional[int]=None     ,					UpperCAmelCase_      :     Optional[int]=None     ,					):
        lowerCAmelCase					:      Optional[int]           =   ()
        lowerCAmelCase					:      Union[str, Any]           =   ()
        lowerCAmelCase					:      Tuple           =   ()
        for i, layer_module in enumerate(self.layer     ):
            if self.output_hidden_states:
                lowerCAmelCase					:      List[str]           =   all_hidden_states + (hidden_states,)

            lowerCAmelCase					:      List[Any]           =   layer_module(
                a_     ,					a_     ,					head_mask[i]     ,					a_     ,					a_     )
            lowerCAmelCase					:      List[Any]           =   layer_outputs[0]

            if self.output_attentions:
                lowerCAmelCase					:      Any           =   all_attentions + (layer_outputs[1],)

            lowerCAmelCase					:      Tuple           =   (hidden_states,)
            if self.output_hidden_states:
                lowerCAmelCase					:      Any           =   current_outputs + (all_hidden_states,)
            if self.output_attentions:
                lowerCAmelCase					:      Optional[Any]           =   current_outputs + (all_attentions,)

            lowerCAmelCase					:      int           =   self.highway[i](a_     )
            # logits, pooled_output

            if not self.training:
                lowerCAmelCase					:      str           =   highway_exit[0]
                lowerCAmelCase					:      List[str]           =   entropy(a_     )
                lowerCAmelCase					:      List[str]           =   highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                lowerCAmelCase					:      List[str]           =   all_highway_exits + (highway_exit,)

                if highway_entropy < self.early_exit_entropy[i]:
                    lowerCAmelCase					:      List[Any]           =   (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                    raise HighwayException(a_     ,					i + 1     )
            else:
                lowerCAmelCase					:      Optional[int]           =   all_highway_exits + (highway_exit,)

        # Add last layer
        if self.output_hidden_states:
            lowerCAmelCase					:      Optional[int]           =   all_hidden_states + (hidden_states,)

        lowerCAmelCase					:      Any           =   (hidden_states,)
        if self.output_hidden_states:
            lowerCAmelCase					:      Optional[Any]           =   outputs + (all_hidden_states,)
        if self.output_attentions:
            lowerCAmelCase					:      Dict           =   outputs + (all_attentions,)

        lowerCAmelCase					:      Union[str, Any]           =   outputs + (all_highway_exits,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "    ,    lowerCAmelCase    ,    )
class  __A					(				lowerCAmelCase			):






    def __init__(						self      :     Optional[int]     ,					UpperCAmelCase_      :     Union[str, Any]     ):
        super().__init__(a_     )
        lowerCAmelCase					:      List[Any]           =   config

        lowerCAmelCase					:      Tuple           =   BertEmbeddings(a_     )
        lowerCAmelCase					:      Optional[Any]           =   DeeBertEncoder(a_     )
        lowerCAmelCase					:      int           =   BertPooler(a_     )

        self.init_weights()






    def 						lowercase__			(						self      :     List[str]     ):
        self.encoder.init_highway_pooler(self.pooler     )






    def 						lowercase__			(						self      :     Optional[Any]     ):
        return self.embeddings.word_embeddings






    def 						lowercase__			(						self      :     int     ,					UpperCAmelCase_      :     Union[str, Any]     ):
        lowerCAmelCase					:      Optional[Any]           =   value






    def 						lowercase__			(						self      :     Optional[int]     ,					UpperCAmelCase_      :     Union[str, Any]     ):
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(a_     )






    @add_start_docstrings_to_model_forward(a_     )
    def 						lowercase__			(						self      :     Any     ,					UpperCAmelCase_      :     str=None     ,					UpperCAmelCase_      :     Dict=None     ,					UpperCAmelCase_      :     Tuple=None     ,					UpperCAmelCase_      :     Dict=None     ,					UpperCAmelCase_      :     Any=None     ,					UpperCAmelCase_      :     Optional[int]=None     ,					UpperCAmelCase_      :     str=None     ,					UpperCAmelCase_      :     List[Any]=None     ,					):
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'     )
        elif input_ids is not None:
            lowerCAmelCase					:      List[str]           =   input_ids.size()
        elif inputs_embeds is not None:
            lowerCAmelCase					:      str           =   inputs_embeds.size()[:-1]
        else:
            raise ValueError('You have to specify either input_ids or inputs_embeds'     )

        lowerCAmelCase					:      str           =   input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            lowerCAmelCase					:      Any           =   torch.ones(a_     ,					device=a_     )
        if encoder_attention_mask is None:
            lowerCAmelCase					:      Any           =   torch.ones(a_     ,					device=a_     )
        if token_type_ids is None:
            lowerCAmelCase					:      Optional[Any]           =   torch.zeros(a_     ,					dtype=torch.long     ,					device=a_     )

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        lowerCAmelCase					:      torch.Tensor           =   self.get_extended_attention_mask(a_     ,					a_     ,					a_     )

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if encoder_attention_mask.dim() == 3:
            lowerCAmelCase					:      Any           =   encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
            lowerCAmelCase					:      Tuple           =   encoder_attention_mask[:, None, None, :]

        lowerCAmelCase					:      Optional[Any]           =   encoder_extended_attention_mask.to(
            dtype=next(self.parameters()     ).dtype     )  # fp16 compatibility
        lowerCAmelCase					:      List[str]           =   (1.0 - encoder_extended_attention_mask) * -10000.0

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        lowerCAmelCase					:      int           =   self.get_head_mask(a_     ,					self.config.num_hidden_layers     )

        lowerCAmelCase					:      Union[str, Any]           =   self.embeddings(
            input_ids=a_     ,					position_ids=a_     ,					token_type_ids=a_     ,					inputs_embeds=a_     )
        lowerCAmelCase					:      Optional[Any]           =   self.encoder(
            a_     ,					attention_mask=a_     ,					head_mask=a_     ,					encoder_hidden_states=a_     ,					encoder_attention_mask=a_     ,					)
        lowerCAmelCase					:      Optional[Any]           =   encoder_outputs[0]
        lowerCAmelCase					:      List[Any]           =   self.pooler(a_     )

        lowerCAmelCase					:      List[Any]           =   (
            sequence_output,
            pooled_output,
        ) + encoder_outputs[
            1:
        ]  # add hidden_states and attentions if they are here
        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class  __A					(				lowerCAmelCase			):






    def __init__(						self      :     Optional[int]     ,					UpperCAmelCase_      :     Union[str, Any]     ,					UpperCAmelCase_      :     Optional[int]     ):
        lowerCAmelCase					:      Union[str, Any]           =   message
        lowerCAmelCase					:      List[Any]           =   exit_layer  # start from 1!






class  __A					(				nn.Module			):






    def __init__(						self      :     Any     ,					UpperCAmelCase_      :     List[str]     ):
        super().__init__()
        lowerCAmelCase					:      str           =   BertPooler(a_     )
        lowerCAmelCase					:      Dict           =   nn.Dropout(config.hidden_dropout_prob     )
        lowerCAmelCase					:      Optional[Any]           =   nn.Linear(config.hidden_size     ,					config.num_labels     )






    def 						lowercase__			(						self      :     List[str]     ,					UpperCAmelCase_      :     List[Any]     ):
        # Pooler
        lowerCAmelCase					:      Union[str, Any]           =   encoder_outputs[0]
        lowerCAmelCase					:      Tuple           =   self.pooler(a_     )
        # "return" pooler_output

        # BertModel
        lowerCAmelCase					:      Tuple           =   (pooler_input, pooler_output) + encoder_outputs[1:]
        # "return" bmodel_output

        # Dropout and classification
        lowerCAmelCase					:      List[str]           =   bmodel_output[1]

        lowerCAmelCase					:      Union[str, Any]           =   self.dropout(a_     )
        lowerCAmelCase					:      Any           =   self.classifier(a_     )

        return logits, pooled_output






@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "    ,    lowerCAmelCase    ,    )
class  __A					(				lowerCAmelCase			):






    def __init__(						self      :     List[str]     ,					UpperCAmelCase_      :     Optional[Any]     ):
        super().__init__(a_     )
        lowerCAmelCase					:      Dict           =   config.num_labels
        lowerCAmelCase					:      Dict           =   config.num_hidden_layers

        lowerCAmelCase					:      Any           =   DeeBertModel(a_     )
        lowerCAmelCase					:      List[Any]           =   nn.Dropout(config.hidden_dropout_prob     )
        lowerCAmelCase					:      Optional[Any]           =   nn.Linear(config.hidden_size     ,					self.config.num_labels     )

        self.init_weights()






    @add_start_docstrings_to_model_forward(a_     )
    def 						lowercase__			(						self      :     List[str]     ,					UpperCAmelCase_      :     Dict=None     ,					UpperCAmelCase_      :     Dict=None     ,					UpperCAmelCase_      :     Any=None     ,					UpperCAmelCase_      :     Optional[int]=None     ,					UpperCAmelCase_      :     Tuple=None     ,					UpperCAmelCase_      :     Tuple=None     ,					UpperCAmelCase_      :     Dict=None     ,					UpperCAmelCase_      :     Optional[int]=-1     ,					UpperCAmelCase_      :     str=False     ,					):
        lowerCAmelCase					:      Optional[Any]           =   self.num_layers
        try:
            lowerCAmelCase					:      List[Any]           =   self.bert(
                a_     ,					attention_mask=a_     ,					token_type_ids=a_     ,					position_ids=a_     ,					head_mask=a_     ,					inputs_embeds=a_     ,					)
            # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

            lowerCAmelCase					:      Any           =   outputs[1]

            lowerCAmelCase					:      Union[str, Any]           =   self.dropout(a_     )
            lowerCAmelCase					:      List[Any]           =   self.classifier(a_     )
            lowerCAmelCase					:      Tuple           =   (logits,) + outputs[2:]  # add hidden states and attention if they are here
        except HighwayException as e:
            lowerCAmelCase					:      Tuple           =   e.message
            lowerCAmelCase					:      Dict           =   e.exit_layer
            lowerCAmelCase					:      List[Any]           =   outputs[0]

        if not self.training:
            lowerCAmelCase					:      Any           =   entropy(a_     )
            lowerCAmelCase					:      Tuple           =   []
            lowerCAmelCase					:      Dict           =   []
        if labels is not None:
            if self.num_labels == 1:
                #  We are doing regression
                lowerCAmelCase					:      str           =   MSELoss()
                lowerCAmelCase					:      List[str]           =   loss_fct(logits.view(-1     )     ,					labels.view(-1     )     )
            else:
                lowerCAmelCase					:      int           =   CrossEntropyLoss()
                lowerCAmelCase					:      List[str]           =   loss_fct(logits.view(-1     ,					self.num_labels     )     ,					labels.view(-1     )     )

            # work with highway exits
            lowerCAmelCase					:      Dict           =   []
            for highway_exit in outputs[-1]:
                lowerCAmelCase					:      str           =   highway_exit[0]
                if not self.training:
                    highway_logits_all.append(a_     )
                    highway_entropy.append(highway_exit[2]     )
                if self.num_labels == 1:
                    #  We are doing regression
                    lowerCAmelCase					:      int           =   MSELoss()
                    lowerCAmelCase					:      int           =   loss_fct(highway_logits.view(-1     )     ,					labels.view(-1     )     )
                else:
                    lowerCAmelCase					:      Optional[int]           =   CrossEntropyLoss()
                    lowerCAmelCase					:      Optional[int]           =   loss_fct(highway_logits.view(-1     ,					self.num_labels     )     ,					labels.view(-1     )     )
                highway_losses.append(a_     )

            if train_highway:
                lowerCAmelCase					:      Any           =   (sum(highway_losses[:-1]     ),) + outputs
                # exclude the final highway, of course
            else:
                lowerCAmelCase					:      Union[str, Any]           =   (loss,) + outputs
        if not self.training:
            lowerCAmelCase					:      Dict           =   outputs + ((original_entropy, highway_entropy), exit_layer)
            if output_layer >= 0:
                lowerCAmelCase					:      str           =   (
                    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                )  # use the highway of the last layer

        return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)









'''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


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




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      )    ->  Union[str, Any]:
					super().__init__()
					self.register_modules(
					    vae=a_							,      text_encoder=a_							,      tokenizer=a_							,      unet=a_							,      scheduler=a_							,      safety_checker=a_							,      feature_extractor=a_							,      )

				def 	a__		(     self							,      a_ = "auto"							)    ->  Dict:
					if slice_size == "auto":
						# half the attention head size is usually a good trade-off between
						# speed and memory
						lowercase						:		Union[str, Any]   					=		self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(a_							)

				def 	a__		(     self							)    ->  Dict:
					self.enable_attention_slicing(a_							)


				@torch.no_grad()
				def __call__(     self							,      a_							,      a_ = 5_1_2							,      a_ = 5_1_2							,      a_ = 5_0							,      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_							,      )    ->  Union[str, Any]:

					if isinstance(a_							,      a_							):
						lowercase						:		Tuple   					=		1
					elif isinstance(a_							,      a_							):
						lowercase						:		int   					=		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
					lowercase						:		int   					=		self.tokenizer(
					    a_							,      padding="max_length"							,      max_length=self.tokenizer.model_max_length							,      return_tensors="pt"							,      )
					lowercase						:		List[Any]   					=		text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						lowercase						:		str   					=		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}'''							)
						lowercase						:		Dict   					=		text_input_ids[:, : self.tokenizer.model_max_length]

					if text_embeddings is None:
						lowercase						:		List[Any]   					=		self.text_encoder(text_input_ids.to(self.device							)							)[0]

					# duplicate text embeddings for each generation per prompt, using mps friendly method
					lowercase					,		lowercase					,		lowercase						:		List[Any]   					=		text_embeddings.shape
					lowercase						:		Tuple   					=		text_embeddings.repeat(1							,      a_							,      1							)
					lowercase						:		Optional[int]   					=		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.
					lowercase						:		str   					=		guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
						lowercase						:		List[str]
						if negative_prompt is None:
							lowercase						:		Optional[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_							):
							lowercase						:		Tuple   					=		[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:
							lowercase						:		Dict   					=		negative_prompt

						lowercase						:		Tuple   					=		text_input_ids.shape[-1]
						lowercase						:		Any   					=		self.tokenizer(
						    a_							,      padding="max_length"							,      max_length=a_							,      truncation=a_							,      return_tensors="pt"							,      )
						lowercase						:		Any   					=		self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

						# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
						lowercase						:		Union[str, Any]   					=		uncond_embeddings.shape[1]
						lowercase						:		Tuple   					=		uncond_embeddings.repeat(a_							,      a_							,      1							)
						lowercase						:		Optional[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
						lowercase						:		Optional[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`.
					lowercase						:		List[str]   					=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					lowercase						:		Union[str, Any]   					=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
					lowercase						:		Union[str, Any]   					=		text_embeddings.dtype
					if latents is None:
						if self.device.type == "mps":
							# randn does not exist on mps
							lowercase						:		List[str]   					=		torch.randn(
							    a_							,      generator=a_							,      device="cpu"							,      dtype=a_							).to(self.device							)
							lowercase						:		Any   					=		torch.randn(a_							,      generator=a_							,      device="cpu"							,      dtype=a_							).to(
							    self.device							)
						else:
							lowercase						:		List[Any]   					=		torch.randn(
							    a_							,      generator=a_							,      device=self.device							,      dtype=a_							)
							lowercase						:		Optional[Any]   					=		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}'''							)
						lowercase						:		Dict   					=		latents_reference.to(self.device							)
						lowercase						:		int   					=		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
					lowercase						:		int   					=		(latents_shape[3] - latents_shape_reference[3]) // 2
					lowercase						:		Union[str, Any]   					=		(latents_shape[2] - latents_shape_reference[2]) // 2
					lowercase						:		int   					=		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
					lowercase						:		Dict   					=		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
					lowercase						:		Optional[int]   					=		0 if dx < 0 else dx
					lowercase						:		Tuple   					=		0 if dy < 0 else dy
					lowercase						:		Optional[int]   					=		max(-dx							,      0							)
					lowercase						:		int   					=		max(-dy							,      0							)
					# import pdb
					# pdb.set_trace()
					lowercase						:		Union[str, 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
					lowercase						:		List[Any]   					=		self.scheduler.timesteps.to(self.device							)

					# scale the initial noise by the standard deviation required by the scheduler
					lowercase						:		List[str]   					=		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]
					lowercase						:		Optional[Any]   					=		"eta" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
					lowercase						:		List[str]   					=		{}
					if accepts_eta:
						lowercase						:		List[str]   					=		eta

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

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

						# perform guidance
						if do_classifier_free_guidance:
							lowercase					,		lowercase						:		Dict   					=		noise_pred.chunk(2							)
							lowercase						:		Optional[int]   					=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

						# compute the previous noisy sample x_t -> x_t-1
						lowercase						:		int   					=		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_							)

					lowercase						:		List[Any]   					=		1 / 0.1_82_15 * latents
					lowercase						:		Tuple   					=		self.vae.decode(a_							).sample

					lowercase						:		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
					lowercase						:		Any   					=		image.cpu().permute(0							,      2							,      3							,      1							).float().numpy()

					if self.safety_checker is not None:
						lowercase						:		Tuple   					=		self.feature_extractor(self.numpy_to_pil(a_							)							,      return_tensors="pt"							).to(
						    self.device							)
						lowercase					,		lowercase						:		Tuple   					=		self.safety_checker(
						    images=a_							,      clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
					else:
						lowercase						:		Union[str, Any]   					=		None

					if output_type == "pil":
						lowercase						:		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_							)


