



'''simple docstring'''







from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						):
       super().__init__()

       self.register_modules(vae=__UpperCamelCase     ,						text_encoder=__UpperCamelCase     ,						tokenizer=__UpperCamelCase     ,						unet=__UpperCamelCase     ,						scheduler=__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):






       with torch.no_grad():
           UpperCamelCase__      =			self.vae.encode(tfms.ToTensor()(__UpperCamelCase ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
           UpperCamelCase__      =			0.18_215 * latent.latent_dist.sample()
       return latent


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			(1 / 0.18_215) * latent
       with torch.no_grad():
           UpperCamelCase__      =			self.vae.decode(__UpperCamelCase ).sample
       UpperCamelCase__      =			(img / 2 + 0.5).clamp(0     ,						1 )
       UpperCamelCase__      =			img.detach().cpu().permute(0     ,						2     ,						3     ,						1 ).numpy()
       UpperCamelCase__      =			(img * 2_55).round().astype("uint8" )
       return Image.fromarray(img[0] )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.tokenizer(
           __UpperCamelCase     ,						padding="max_length"     ,						max_length=self.tokenizer.model_max_length     ,						truncation=__UpperCamelCase     ,						return_tensors="pt"     ,						)

       UpperCamelCase__      =			self.text_encoder(text_input.input_ids.to(self.device ) )[0]

       UpperCamelCase__      =			self.tokenizer(
           ""     ,						padding="max_length"     ,						max_length=self.tokenizer.model_max_length     ,						truncation=__UpperCamelCase     ,						return_tensors="pt"     ,						)

       UpperCamelCase__      =			self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

       return torch.cat([uncond_embedding, text_embedding] )







   def __call__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = 0.3     ,						__UpperCamelCase = 0.6     ,						__UpperCamelCase = 0.5     ,						__UpperCamelCase = 42     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = 7.5     ,						):
       UpperCamelCase__      =			steps - int(kmin * steps )
       UpperCamelCase__      =			steps - int(kmax * steps )

       UpperCamelCase__      =			self.prep_text(__UpperCamelCase )

       self.scheduler.set_timesteps(__UpperCamelCase )

       UpperCamelCase__      =			img.size
       UpperCamelCase__      =			self.encode(__UpperCamelCase )

       torch.manual_seed(__UpperCamelCase )
       UpperCamelCase__      =			torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)     ,						).to(self.device )

       UpperCamelCase__      =			self.scheduler.add_noise(
           __UpperCamelCase     ,						__UpperCamelCase     ,						timesteps=self.scheduler.timesteps[tmax]     ,						)

       UpperCamelCase__      =			torch.cat([latents] * 2 )

       UpperCamelCase__      =			self.scheduler.scale_model_input(__UpperCamelCase     ,						self.scheduler.timesteps[tmax] )

       with torch.no_grad():
           UpperCamelCase__      =			self.unet(
               __UpperCamelCase     ,						self.scheduler.timesteps[tmax]     ,						encoder_hidden_states=__UpperCamelCase     ,						).sample

       UpperCamelCase__      =			pred.chunk(2 )
       UpperCamelCase__      =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

       UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						self.scheduler.timesteps[tmax]     ,						__UpperCamelCase ).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps ) ):
           if i > tmax:
               if i < tmin:  # layout generation phase
                   UpperCamelCase__      =			self.scheduler.add_noise(
                       __UpperCamelCase     ,						__UpperCamelCase     ,						timesteps=__UpperCamelCase     ,						)

                   UpperCamelCase__      =			(mix_factor * latents) + (
                       1 - mix_factor
                   ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                   UpperCamelCase__      =			torch.cat([input] * 2 )

               else:  # content generation phase
                   UpperCamelCase__      =			torch.cat([latents] * 2 )

               UpperCamelCase__      =			self.scheduler.scale_model_input(__UpperCamelCase     ,						__UpperCamelCase )

               with torch.no_grad():
                   UpperCamelCase__      =			self.unet(
                       __UpperCamelCase     ,						__UpperCamelCase     ,						encoder_hidden_states=__UpperCamelCase     ,						).sample

               UpperCamelCase__      =			pred.chunk(2 )
               UpperCamelCase__      =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

               UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

       return self.decode(__UpperCamelCase )









'''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


lowercase__     :  int    					=			logging.get_logger(__name__)

lowercase__     :  Any    					=			'MMBTConfig'

class       lowerCamelCase_							(	nn.Module  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()
       UpperCamelCase__      =			config
       UpperCamelCase__      =			encoder
       UpperCamelCase__      =			nn.Linear(config.modal_hidden_size     ,						config.hidden_size )
       UpperCamelCase__      =			embeddings.position_embeddings
       UpperCamelCase__      =			embeddings.token_type_embeddings
       UpperCamelCase__      =			embeddings.word_embeddings
       UpperCamelCase__      =			embeddings.LayerNorm
       UpperCamelCase__      =			nn.Dropout(p=config.hidden_dropout_prob )







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None ):
       UpperCamelCase__      =			self.proj_embeddings(self.encoder(__UpperCamelCase ) )
       UpperCamelCase__      =			token_embeddings.size(1 )

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

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

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

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

       UpperCamelCase__      =			self.position_embeddings(__UpperCamelCase )
       UpperCamelCase__      =			self.token_type_embeddings(__UpperCamelCase )
       UpperCamelCase__      =			token_embeddings + position_embeddings + token_type_embeddings
       UpperCamelCase__      =			self.LayerNorm(__UpperCamelCase )
       UpperCamelCase__      =			self.dropout(__UpperCamelCase )
       return embeddings

lowercase__     :  Union[str, 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'

lowercase__     :  List[str]    					=			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       lowerCamelCase_							(	nn.Module					,    lowerCAmelCase_  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()
       UpperCamelCase__      =			config
       UpperCamelCase__      =			transformer
       UpperCamelCase__      =			ModalEmbeddings(__UpperCamelCase     ,						__UpperCamelCase     ,						transformer.embeddings )


   @add_start_docstrings_to_model_forward(__UpperCamelCase )
   @replace_return_docstrings(output_type=__UpperCamelCase     ,						config_class=_CONFIG_FOR_DOC )
   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						):
       UpperCamelCase__      =			output_attentions if output_attentions is not None else self.config.output_attentions
       UpperCamelCase__      =			(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       UpperCamelCase__      =			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:
           UpperCamelCase__      =			input_ids.size()
       elif inputs_embeds is not None:
           UpperCamelCase__      =			inputs_embeds.size()[:-1]
       else:
           raise ValueError("You have to specify either input_ids or inputs_embeds" )

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

       UpperCamelCase__      =			self.modal_encoder(
           __UpperCamelCase     ,						start_token=__UpperCamelCase     ,						end_token=__UpperCamelCase     ,						position_ids=__UpperCamelCase     ,						token_type_ids=__UpperCamelCase     ,						)

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

       if token_type_ids is None:
           UpperCamelCase__      =			torch.ones(__UpperCamelCase     ,						dtype=torch.long     ,						device=__UpperCamelCase )

       UpperCamelCase__      =			self.transformer.embeddings(
           input_ids=__UpperCamelCase     ,						position_ids=__UpperCamelCase     ,						token_type_ids=__UpperCamelCase     ,						inputs_embeds=__UpperCamelCase )

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

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

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

       UpperCamelCase__      =			self.get_extended_attention_mask(__UpperCamelCase     ,						__UpperCamelCase )
       UpperCamelCase__      =			self.invert_attention_mask(__UpperCamelCase )
       UpperCamelCase__      =			self.get_head_mask(__UpperCamelCase     ,						self.config.num_hidden_layers )

       UpperCamelCase__      =			self.transformer.encoder(
           __UpperCamelCase     ,						attention_mask=__UpperCamelCase     ,						head_mask=__UpperCamelCase     ,						encoder_hidden_states=__UpperCamelCase     ,						encoder_attention_mask=__UpperCamelCase     ,						output_attentions=__UpperCamelCase     ,						output_hidden_states=__UpperCamelCase     ,						return_dict=__UpperCamelCase     ,						)

       UpperCamelCase__      =			encoder_outputs[0]
       UpperCamelCase__      =			self.transformer.pooler(__UpperCamelCase )

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

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


   def 	_UpperCamelCase       (  self ):
       return self.embeddings.word_embeddings







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			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       lowerCamelCase_							(	nn.Module  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()
       UpperCamelCase__      =			config.num_labels

       UpperCamelCase__      =			MMBTModel(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase )
       UpperCamelCase__      =			nn.Dropout(config.hidden_dropout_prob )
       UpperCamelCase__      =			nn.Linear(config.hidden_size     ,						config.num_labels )







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						):
       UpperCamelCase__      =			return_dict if return_dict is not None else self.config.use_return_dict

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

       UpperCamelCase__      =			outputs[1]

       UpperCamelCase__      =			self.dropout(__UpperCamelCase )
       UpperCamelCase__      =			self.classifier(__UpperCamelCase )

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

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

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




