

'''simple docstring'''





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

from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ....modeling_utils import ModuleUtilsMixin
from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings


__a						:str            = logging.get_logger(__name__)

__a						:Union[str, Any]            = 'MMBTConfig'

class      _a				( nn.Module							):




      """simple docstring"""

      def __init__(      self       :							List[Any]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							List[Any]  ):
            super().__init__()
            A_	     =							config
            A_	     =							encoder
            A_	     =							nn.Linear(config.modal_hidden_size	, config.hidden_size  )
            A_	     =							embeddings.position_embeddings
            A_	     =							embeddings.token_type_embeddings
            A_	     =							embeddings.word_embeddings
            A_	     =							embeddings.LayerNorm
            A_	     =							nn.Dropout(p=config.hidden_dropout_prob  )







      def 		__A  (      self       :							Union[str, Any]	, UpperCAmelCase       :							int	, UpperCAmelCase       :							str=None	, UpperCAmelCase       :							Tuple=None	, UpperCAmelCase       :							str=None	, UpperCAmelCase       :							Union[str, Any]=None  ):
            A_	     =							self.proj_embeddings(self.encoder(lowercase_  )  )
            A_	     =							token_embeddings.size(1  )

            if start_token is not None:
                  A_	     =							self.word_embeddings(lowercase_  )
                  seq_length += 1
                  A_	     =							torch.cat([start_token_embeds.unsqueeze(1  ), token_embeddings]	, dim=1  )

            if end_token is not None:
                  A_	     =							self.word_embeddings(lowercase_  )
                  seq_length += 1
                  A_	     =							torch.cat([token_embeddings, end_token_embeds.unsqueeze(1  )]	, dim=1  )

            if position_ids is None:
                  A_	     =							torch.arange(lowercase_	, dtype=torch.long	, device=input_modal.device  )
                  A_	     =							position_ids.unsqueeze(0  ).expand(input_modal.size(0  )	, lowercase_  )

            if token_type_ids is None:
                  A_	     =							torch.zeros(
                      (input_modal.size(0  ), seq_length)	, dtype=torch.long	, device=input_modal.device  )

            A_	     =							self.position_embeddings(lowercase_  )
            A_	     =							self.token_type_embeddings(lowercase_  )
            A_	     =							token_embeddings + position_embeddings + token_type_embeddings
            A_	     =							self.LayerNorm(lowercase_  )
            A_	     =							self.dropout(lowercase_  )
            return embeddings




__a						:List[Any]            = R'\n    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and\n    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.\n    It\'s a supervised multimodal bitransformer model that fuses information from text and other image encoders, and\n    obtain state-of-the-art performance on various multimodal classification benchmark tasks.\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration.\n        transformer (`nn.Module`): A text transformer that is used by MMBT.\n            It should have embeddings, encoder, and pooler attributes.\n        encoder (`nn.Module`): Encoder for the second modality.\n            It should take in a batch of modal inputs and return k, n dimension embeddings.\n'

__a						:List[Any]            = R'\n    Args:\n        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):\n            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image\n            Encoder, the shape would be (batch_size, channels, height, width)\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it\'s\n            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See\n            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification\n            tasks.\n        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.\n        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:\n            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n            [What are attention masks?](../glossary#attention-mask)\n        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:\n            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n            1]`:\n\n            - 0 corresponds to a *sentence A* token,\n            - 1 corresponds to a *sentence B* token.\n\n            [What are token type IDs?](../glossary#token-type-ids)\n        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:\n            Segment token indices to indicate different portions of the non-text modality. The embeddings from these\n            tokens will be summed with the respective token embeddings for the non-text modality.\n        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n            config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.\n            Selected in the range `[0, config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n\n            - 1 indicates the head is **not masked**,\n            - 0 indicates the head is **masked**.\n\n        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):\n            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n            model\'s internal embedding lookup matrix.\n        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n            tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'

@add_start_docstrings(
    'The bare MMBT Model outputting raw hidden-states without any specific head on top.'    ,							lowerCamelCase__    ,							)
class      _a				( nn.Module    ,							lowerCamelCase__							):




      """simple docstring"""

      def __init__(      self       :							Tuple	, UpperCAmelCase       :							Dict	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							List[str]  ):
            super().__init__()
            A_	     =							config
            A_	     =							transformer
            A_	     =							ModalEmbeddings(lowercase_	, lowercase_	, transformer.embeddings  )





      @add_start_docstrings_to_model_forward(lowercase_  )
      @replace_return_docstrings(output_type=lowercase_	, config_class=_CONFIG_FOR_DOC  )
      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Dict	, UpperCAmelCase       :							Any=None	, UpperCAmelCase       :							Union[str, Any]=None	, UpperCAmelCase       :							Union[str, Any]=None	, UpperCAmelCase       :							Optional[Any]=None	, UpperCAmelCase       :							Tuple=None	, UpperCAmelCase       :							Union[str, Any]=None	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							Any=None	, UpperCAmelCase       :							Optional[Any]=None	, UpperCAmelCase       :							Dict=None	, UpperCAmelCase       :							Optional[Any]=None	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							List[Any]=None	, UpperCAmelCase       :							Union[str, Any]=None	, UpperCAmelCase       :							List[str]=None	, ):
            A_	     =							output_attentions if output_attentions is not None else self.config.output_attentions
            A_	     =							(
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            A_	     =							return_dict if return_dict is not None else self.config.use_return_dict

            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:
                  A_	     =							input_ids.size()
            elif inputs_embeds is not None:
                  A_	     =							inputs_embeds.size()[:-1]
            else:
                  raise ValueError("You have to specify either input_ids or inputs_embeds"  )

            A_	     =							input_ids.device if input_ids is not None else inputs_embeds.device

            A_	     =							self.modal_encoder(
                lowercase_	, start_token=lowercase_	, end_token=lowercase_	, position_ids=lowercase_	, token_type_ids=lowercase_	, )

            A_	     =							modal_embeddings.size()[:-1]

            if token_type_ids is None:
                  A_	     =							torch.ones(lowercase_	, dtype=torch.long	, device=lowercase_  )

            A_	     =							self.transformer.embeddings(
                input_ids=lowercase_	, position_ids=lowercase_	, token_type_ids=lowercase_	, inputs_embeds=lowercase_  )

            A_	     =							torch.cat([modal_embeddings, txt_embeddings]	, 1  )

            A_	     =							embedding_output.size()[:-1]

            if attention_mask is None:
                  A_	     =							torch.ones(lowercase_	, device=lowercase_  )
            else:
                  A_	     =							torch.cat(
                      [torch.ones(lowercase_	, device=lowercase_	, dtype=torch.long  ), attention_mask]	, dim=1  )
            if encoder_attention_mask is None:
                  A_	     =							torch.ones(lowercase_	, device=lowercase_  )
            else:
                  A_	     =							torch.cat(
                      [torch.ones(lowercase_	, device=lowercase_  ), encoder_attention_mask]	, dim=1  )

            A_	     =							self.get_extended_attention_mask(lowercase_	, lowercase_  )
            A_	     =							self.invert_attention_mask(lowercase_  )
            A_	     =							self.get_head_mask(lowercase_	, self.config.num_hidden_layers  )

            A_	     =							self.transformer.encoder(
                lowercase_	, attention_mask=lowercase_	, head_mask=lowercase_	, encoder_hidden_states=lowercase_	, encoder_attention_mask=lowercase_	, output_attentions=lowercase_	, output_hidden_states=lowercase_	, return_dict=lowercase_	, )

            A_	     =							encoder_outputs[0]
            A_	     =							self.transformer.pooler(lowercase_  )

            if not return_dict:
                  return (sequence_output, pooled_output) + encoder_outputs[1:]

            return BaseModelOutputWithPooling(
                last_hidden_state=lowercase_	, pooler_output=lowercase_	, hidden_states=encoder_outputs.hidden_states	, attentions=encoder_outputs.attentions	, )





      def 		__A  (      self       :							int  ):
            return self.embeddings.word_embeddings







      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							int  ):
            A_	     =							value




@add_start_docstrings(
    '\n    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)\n    '    ,							lowerCamelCase__    ,							lowerCamelCase__    ,							)
class      _a				( nn.Module							):




      """simple docstring"""

      def __init__(      self       :							Optional[Any]	, UpperCAmelCase       :							int	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							Any  ):
            super().__init__()
            A_	     =							config.num_labels

            A_	     =							MMBTModel(lowercase_	, lowercase_	, lowercase_  )
            A_	     =							nn.Dropout(config.hidden_dropout_prob  )
            A_	     =							nn.Linear(config.hidden_size	, config.num_labels  )







      def 		__A  (      self       :							Tuple	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Tuple=None	, UpperCAmelCase       :							Union[str, Any]=None	, UpperCAmelCase       :							Any=None	, UpperCAmelCase       :							Tuple=None	, UpperCAmelCase       :							Union[str, Any]=None	, UpperCAmelCase       :							Dict=None	, UpperCAmelCase       :							str=None	, UpperCAmelCase       :							List[Any]=None	, UpperCAmelCase       :							Any=None	, UpperCAmelCase       :							List[Any]=None	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							int=None	, ):
            A_	     =							return_dict if return_dict is not None else self.config.use_return_dict

            A_	     =							self.mmbt(
                input_modal=lowercase_	, input_ids=lowercase_	, modal_start_tokens=lowercase_	, modal_end_tokens=lowercase_	, attention_mask=lowercase_	, token_type_ids=lowercase_	, modal_token_type_ids=lowercase_	, position_ids=lowercase_	, modal_position_ids=lowercase_	, head_mask=lowercase_	, inputs_embeds=lowercase_	, return_dict=lowercase_	, )

            A_	     =							outputs[1]

            A_	     =							self.dropout(lowercase_  )
            A_	     =							self.classifier(lowercase_  )

            A_	     =							None
            if labels is not None:
                  if self.num_labels == 1:
                        #  We are doing regression
                        A_	     =							MSELoss()
                        A_	     =							loss_fct(logits.view(-1  )	, labels.view(-1  )  )
                  else:
                        A_	     =							CrossEntropyLoss()
                        A_	     =							loss_fct(logits.view(-1	, self.num_labels  )	, labels.view(-1  )  )

            if not return_dict:
                  A_	     =							(logits,) + outputs[2:]
                  return ((loss,) + output) if loss is not None else output

            return SequenceClassifierOutput(
                loss=lowercase_	, logits=lowercase_	, hidden_states=outputs.hidden_states	, attentions=outputs.attentions	, )


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput

class      _a				( nn.Module							):




      """simple docstring"""

      def __init__(      self       :							int	, UpperCAmelCase       :							str	, UpperCAmelCase       :							str=1.0  ):
            super().__init__()

            A_	     =							cut_size
            A_	     =							cut_power







      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[Any]  ):
            A_     ,    A_	     =							pixel_values.shape[2:4]
            A_	     =							min(UpperCAmelCase	, UpperCAmelCase  )
            A_	     =							min(UpperCAmelCase	, UpperCAmelCase	, self.cut_size  )
            A_	     =							[]
            for _ in range(UpperCAmelCase  ):
                  A_	     =							int(torch.rand([]  ) ** self.cut_power * (max_size - min_size) + min_size  )
                  A_	     =							torch.randint(0	, sideX - size + 1	, ()  )
                  A_	     =							torch.randint(0	, sideY - size + 1	, ()  )
                  A_	     =							pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
                  cutouts.append(F.adaptive_avg_poolad(UpperCAmelCase	, self.cut_size  )  )
            return torch.cat(UpperCAmelCase  )




def 			__snake_case	(   __UpperCamelCase  :   Dict							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      A_	     =							F.normalize(__UpperCamelCase							,dim=-1						)
      A_	     =							F.normalize(__UpperCamelCase							,dim=-1						)
      return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)

def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      for param in model.parameters():
            A_	     =							value



class      _a				( snake_case_							):




      """simple docstring"""

      def __init__(      self       :							int	, UpperCAmelCase       :							AutoencoderKL	, UpperCAmelCase       :							CLIPTextModel	, UpperCAmelCase       :							CLIPModel	, UpperCAmelCase       :							CLIPTokenizer	, UpperCAmelCase       :							UNetaDConditionModel	, UpperCAmelCase       :							Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]	, UpperCAmelCase       :							CLIPImageProcessor	, ):
            super().__init__()
            self.register_modules(
                vae=UpperCAmelCase	, text_encoder=UpperCAmelCase	, clip_model=UpperCAmelCase	, tokenizer=UpperCAmelCase	, unet=UpperCAmelCase	, scheduler=UpperCAmelCase	, feature_extractor=UpperCAmelCase	, )

            A_	     =							transforms.Normalize(mean=feature_extractor.image_mean	, std=feature_extractor.image_std  )
            A_	     =							(
                feature_extractor.size
                if isinstance(feature_extractor.size	, UpperCAmelCase  )
                else feature_extractor.size["shortest_edge"]
            )
            A_	     =							MakeCutouts(self.cut_out_size  )

            set_requires_grad(self.text_encoder	, UpperCAmelCase  )
            set_requires_grad(self.clip_model	, UpperCAmelCase  )





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





      def 		__A  (      self       :							Tuple  ):
            self.enable_attention_slicing(UpperCAmelCase  )





      def 		__A  (      self       :							str  ):
            set_requires_grad(self.vae	, UpperCAmelCase  )





      def 		__A  (      self       :							Dict  ):
            set_requires_grad(self.vae	, UpperCAmelCase  )





      def 		__A  (      self       :							str  ):
            set_requires_grad(self.unet	, UpperCAmelCase  )





      def 		__A  (      self       :							Tuple  ):
            set_requires_grad(self.unet	, UpperCAmelCase  )





      @torch.enable_grad()
      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Tuple=True	, ):
            A_	     =							latents.detach().requires_grad_()

            A_	     =							self.scheduler.scale_model_input(UpperCAmelCase	, UpperCAmelCase  )

            # predict the noise residual
            A_	     =							self.unet(UpperCAmelCase	, UpperCAmelCase	, encoder_hidden_states=UpperCAmelCase  ).sample

            if isinstance(self.scheduler	, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)  ):
                  A_	     =							self.scheduler.alphas_cumprod[timestep]
                  A_	     =							1 - alpha_prod_t
                  # compute predicted original sample from predicted noise also called
                  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
                  A_	     =							(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                  A_	     =							torch.sqrt(UpperCAmelCase  )
                  A_	     =							pred_original_sample * (fac) + latents * (1 - fac)
            elif isinstance(self.scheduler	, UpperCAmelCase  ):
                  A_	     =							self.scheduler.sigmas[index]
                  A_	     =							latents - sigma * noise_pred
            else:
                  raise ValueError(f'''scheduler type {type(self.scheduler  )} not supported'''  )

            A_	     =							1 / self.vae.config.scaling_factor * sample
            A_	     =							self.vae.decode(UpperCAmelCase  ).sample
            A_	     =							(image / 2 + 0.5).clamp(0	, 1  )

            if use_cutouts:
                  A_	     =							self.make_cutouts(UpperCAmelCase	, UpperCAmelCase  )
            else:
                  A_	     =							transforms.Resize(self.cut_out_size  )(UpperCAmelCase  )
            A_	     =							self.normalize(UpperCAmelCase  ).to(latents.dtype  )

            A_	     =							self.clip_model.get_image_features(UpperCAmelCase  )
            A_	     =							image_embeddings_clip / image_embeddings_clip.norm(p=2	, dim=-1	, keepdim=UpperCAmelCase  )

            if use_cutouts:
                  A_	     =							spherical_dist_loss(UpperCAmelCase	, UpperCAmelCase  )
                  A_	     =							dists.view([num_cutouts, sample.shape[0], -1]  )
                  A_	     =							dists.sum(2  ).mean(0  ).sum() * clip_guidance_scale
            else:
                  A_	     =							spherical_dist_loss(UpperCAmelCase	, UpperCAmelCase  ).mean() * clip_guidance_scale

            A_	     =							-torch.autograd.grad(UpperCAmelCase	, UpperCAmelCase  )[0]

            if isinstance(self.scheduler	, UpperCAmelCase  ):
                  A_	     =							latents.detach() + grads * (sigma**2)
                  A_	     =							noise_pred_original
            else:
                  A_	     =							noise_pred_original - torch.sqrt(UpperCAmelCase  ) * grads
            return noise_pred, latents







      @torch.no_grad()
      def __call__(      self       :							Optional[Any]	, UpperCAmelCase       :							Union[str, List[str]]	, UpperCAmelCase       :							Optional[int] = 512	, UpperCAmelCase       :							Optional[int] = 512	, UpperCAmelCase       :							Optional[int] = 50	, UpperCAmelCase       :							Optional[float] = 7.5	, UpperCAmelCase       :							Optional[int] = 1	, UpperCAmelCase       :							float = 0.0	, UpperCAmelCase       :							Optional[float] = 100	, UpperCAmelCase       :							Optional[Union[str, List[str]]] = None	, UpperCAmelCase       :							Optional[int] = 4	, UpperCAmelCase       :							Optional[bool] = True	, UpperCAmelCase       :							Optional[torch.Generator] = None	, UpperCAmelCase       :							Optional[torch.FloatTensor] = None	, UpperCAmelCase       :							Optional[str] = "pil"	, UpperCAmelCase       :							bool = True	, ):
            if isinstance(UpperCAmelCase	, UpperCAmelCase  ):
                  A_	     =							1
            elif isinstance(UpperCAmelCase	, UpperCAmelCase  ):
                  A_	     =							len(UpperCAmelCase  )
            else:
                  raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase  )}'''  )

            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}.'''  )

            # get prompt text embeddings
            A_	     =							self.tokenizer(
                UpperCAmelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=UpperCAmelCase	, return_tensors="pt"	, )
            A_	     =							self.text_encoder(text_input.input_ids.to(self.device  )  )[0]
            # duplicate text embeddings for each generation per prompt
            A_	     =							text_embeddings.repeat_interleave(UpperCAmelCase	, dim=0  )

            if clip_guidance_scale > 0:
                  if clip_prompt is not None:
                        A_	     =							self.tokenizer(
                            UpperCAmelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=UpperCAmelCase	, return_tensors="pt"	, ).input_ids.to(self.device  )
                  else:
                        A_	     =							text_input.input_ids.to(self.device  )
                  A_	     =							self.clip_model.get_text_features(UpperCAmelCase  )
                  A_	     =							text_embeddings_clip / text_embeddings_clip.norm(p=2	, dim=-1	, keepdim=UpperCAmelCase  )
                  # duplicate text embeddings clip for each generation per prompt
                  A_	     =							text_embeddings_clip.repeat_interleave(UpperCAmelCase	, dim=0  )

            # 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.
            A_	     =							guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                  A_	     =							text_input.input_ids.shape[-1]
                  A_	     =							self.tokenizer([""]	, padding="max_length"	, max_length=UpperCAmelCase	, return_tensors="pt"  )
                  A_	     =							self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]
                  # duplicate unconditional embeddings for each generation per prompt
                  A_	     =							uncond_embeddings.repeat_interleave(UpperCAmelCase	, dim=0  )

                  # 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
                  A_	     =							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`.
            A_	     =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            A_	     =							text_embeddings.dtype
            if latents is None:
                  if self.device.type == "mps":
                        # randn does not work reproducibly on mps
                        A_	     =							torch.randn(UpperCAmelCase	, generator=UpperCAmelCase	, device="cpu"	, dtype=UpperCAmelCase  ).to(
                            self.device  )
                  else:
                        A_	     =							torch.randn(UpperCAmelCase	, generator=UpperCAmelCase	, device=self.device	, dtype=UpperCAmelCase  )
            else:
                  if latents.shape != latents_shape:
                        raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''  )
                  A_	     =							latents.to(self.device  )

            # set timesteps
            A_	     =							"offset" in set(inspect.signature(self.scheduler.set_timesteps  ).parameters.keys()  )
            A_	     =							{}
            if accepts_offset:
                  A_	     =							1

            self.scheduler.set_timesteps(UpperCAmelCase	, **UpperCAmelCase  )

            # Some schedulers like PNDM have timesteps as arrays
            # It's more optimized to move all timesteps to correct device beforehand
            A_	     =							self.scheduler.timesteps.to(self.device  )

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

            # check if the scheduler accepts generator
            A_	     =							"generator" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
            if accepts_generator:
                  A_	     =							generator

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

                  # predict the noise residual
                  A_	     =							self.unet(UpperCAmelCase	, UpperCAmelCase	, encoder_hidden_states=UpperCAmelCase  ).sample

                  # perform classifier free guidance
                  if do_classifier_free_guidance:
                        A_     ,    A_	     =							noise_pred.chunk(2  )
                        A_	     =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                  # perform clip guidance
                  if clip_guidance_scale > 0:
                        A_	     =							(
                            text_embeddings.chunk(2  )[1] if do_classifier_free_guidance else text_embeddings
                        )
                        A_     ,    A_	     =							self.cond_fn(
                            UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, )

                  # compute the previous noisy sample x_t -> x_t-1
                  A_	     =							self.scheduler.step(UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, **UpperCAmelCase  ).prev_sample

            # scale and decode the image latents with vae
            A_	     =							1 / self.vae.config.scaling_factor * latents
            A_	     =							self.vae.decode(UpperCAmelCase  ).sample

            A_	     =							(image / 2 + 0.5).clamp(0	, 1  )
            A_	     =							image.cpu().permute(0	, 2	, 3	, 1  ).numpy()

            if output_type == "pil":
                  A_	     =							self.numpy_to_pil(UpperCAmelCase  )

            if not return_dict:
                  return (image, None)

            return StableDiffusionPipelineOutput(images=UpperCAmelCase	, nsfw_content_detected=UpperCAmelCase  )