




import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate



class  _snake_case				(							snake_case__       ):

				'''simple docstring'''




				UpperCamelCase__		     =CONFIG_NAME





				@register_to_config
				def __init__(							self      :  str					,							snake_case      :  bool = True					,							snake_case      :  int = 8					,							snake_case      :  str = "lanczos"					,							snake_case      :  bool = True					,							snake_case      :  bool = False					,							):
								super().__init__()





				@staticmethod
				def 				snake_case_ (							snake_case      :  np.ndarray     ):
								if images.ndim == 3:
												UpperCAmelCase_					:List[str]      			=      images[None, ...]
								UpperCAmelCase_					:str      			=      (images * 255).round().astype('''uint8'''     )
								if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												UpperCAmelCase_					:int      			=      [Image.fromarray(image.squeeze()					,							mode='''L'''     ) for image in images]
								else:
												UpperCAmelCase_					:str      			=      [Image.fromarray(snake_case     ) for image in images]

								return pil_images





				@staticmethod
				def 				snake_case_ (							snake_case      :  Union[List[PIL.Image.Image], PIL.Image.Image]     ):
								if not isinstance(snake_case					,							snake_case     ):
												UpperCAmelCase_					:List[Any]      			=      [images]
								UpperCAmelCase_					:List[Any]      			=      [np.array(snake_case     ).astype(np.floataa     ) / 255.0 for image in images]
								UpperCAmelCase_					:str      			=      np.stack(snake_case					,							axis=0     )

								return images





				@staticmethod
				def 				snake_case_ (							snake_case      :  np.ndarray     ):
								if images.ndim == 3:
												UpperCAmelCase_					:Tuple      			=      images[..., None]

								UpperCAmelCase_					:Optional[Any]      			=      torch.from_numpy(images.transpose(0					,							3					,							1					,							2     )     )
								return images





				@staticmethod
				def 				snake_case_ (							snake_case      :  torch.FloatTensor     ):
								UpperCAmelCase_					:Dict      			=      images.cpu().permute(0					,							2					,							3					,							1     ).float().numpy()
								return images





				@staticmethod
				def 				snake_case_ (							snake_case      :  Union[str, Any]     ):
								return 2.0 * images - 1.0





				@staticmethod
				def 				snake_case_ (							snake_case      :  List[str]     ):
								return (images / 2 + 0.5).clamp(0					,							1     )





				@staticmethod
				def 				snake_case_ (							snake_case      :  PIL.Image.Image     ):
								UpperCAmelCase_					:Any      			=      image.convert('''RGB'''     )
								return image





				def 				snake_case_ (							self      :  List[str]					,							snake_case      :  PIL.Image.Image					,							snake_case      :  Optional[int] = None					,							snake_case      :  Optional[int] = None					,							):
								if height is None:
												UpperCAmelCase_					:Any      			=      image.height
								if width is None:
												UpperCAmelCase_					:Any      			=      image.width

								UpperCAmelCase_					:Optional[Any]      			=      (
								    x - x % self.config.vae_scale_factor for x in (width, height)
								)  # resize to integer multiple of vae_scale_factor
								UpperCAmelCase_					:str      			=      image.resize((width, height)					,							resample=PIL_INTERPOLATION[self.config.resample]     )
								return image





				def 				snake_case_ (							self      :  Any					,							snake_case      :  Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]					,							snake_case      :  Optional[int] = None					,							snake_case      :  Optional[int] = None					,							):
								UpperCAmelCase_					:int      			=      (PIL.Image.Image, np.ndarray, torch.Tensor)
								if isinstance(snake_case					,							snake_case     ):
												UpperCAmelCase_					:Union[str, Any]      			=      [image]
								elif not (isinstance(snake_case					,							snake_case     ) and all(isinstance(snake_case					,							snake_case     ) for i in image     )):
												raise ValueError(
												    f'Input is in incorrect format: {[type(snake_case     ) for i in image]}. Currently, we only support {", ".join(snake_case     )}'     )

								if isinstance(image[0]					,							PIL.Image.Image     ):
												if self.config.do_convert_rgb:
																UpperCAmelCase_					:Union[str, Any]      			=      [self.convert_to_rgb(snake_case     ) for i in image]
												if self.config.do_resize:
																UpperCAmelCase_					:Dict      			=      [self.resize(snake_case					,							snake_case					,							snake_case     ) for i in image]
												UpperCAmelCase_					:str      			=      self.pil_to_numpy(snake_case     )  # to np
												UpperCAmelCase_					:Tuple      			=      self.numpy_to_pt(snake_case     )  # to pt

								elif isinstance(image[0]					,							np.ndarray     ):
												UpperCAmelCase_					:Tuple      			=      np.concatenate(snake_case					,							axis=0     ) if image[0].ndim == 4 else np.stack(snake_case					,							axis=0     )
												UpperCAmelCase_					:Optional[int]      			=      self.numpy_to_pt(snake_case     )
												UpperCAmelCase_					:str      			=      image.shape
												if self.config.do_resize and (
												    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
												):
																raise ValueError(
																    f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
																    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'     )

								elif isinstance(image[0]					,							torch.Tensor     ):
												UpperCAmelCase_					:List[str]      			=      torch.cat(snake_case					,							axis=0     ) if image[0].ndim == 4 else torch.stack(snake_case					,							axis=0     )
												UpperCAmelCase_					:int      			=      image.shape

												# don't need any preprocess if the image is latents
												if channel == 4:
																return image

												if self.config.do_resize and (
												    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
												):
																raise ValueError(
																    f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
																    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'     )

        # expected range [0,1], normalize to [-1,1]
								UpperCAmelCase_					:str      			=      self.config.do_normalize
								if image.min() < 0:
												warnings.warn(
												    '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
												    f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'					,							snake_case					,							)
												UpperCAmelCase_					:Dict      			=      False

								if do_normalize:
												UpperCAmelCase_					:Union[str, Any]      			=      self.normalize(snake_case     )

								return image





				def 				snake_case_ (							self      :  Any					,							snake_case      :  torch.FloatTensor					,							snake_case      :  str = "pil"					,							snake_case      :  Optional[List[bool]] = None					,							):
								if not isinstance(snake_case					,							torch.Tensor     ):
												raise ValueError(
												    f'Input for postprocessing is in incorrect format: {type(snake_case     )}. We only support pytorch tensor'     )
								if output_type not in ["latent", "pt", "np", "pil"]:
												UpperCAmelCase_					:Any      			=      (
												    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
												    '`pil`, `np`, `pt`, `latent`'
												)
												deprecate('''Unsupported output_type'''					,							'''1.0.0'''					,							snake_case					,							standard_warn=snake_case     )
												UpperCAmelCase_					:Any      			=      'np'

								if output_type == "latent":
												return image

								if do_denormalize is None:
												UpperCAmelCase_					:Union[str, Any]      			=      [self.config.do_normalize] * image.shape[0]

								UpperCAmelCase_					:Tuple      			=      torch.stack(
								    [self.denormalize(image[i]     ) if do_denormalize[i] else image[i] for i in range(image.shape[0]     )]     )

								if output_type == "pt":
												return image

								UpperCAmelCase_					:List[Any]      			=      self.pt_to_numpy(snake_case     )

								if output_type == "np":
												return image

								if output_type == "pil":
												return self.numpy_to_pil(snake_case     )



class  _snake_case				(							snake_case__       ):

				'''simple docstring'''




				UpperCamelCase__		     =CONFIG_NAME





				@register_to_config
				def __init__(							self      :  List[Any]					,							snake_case      :  bool = True					,							snake_case      :  int = 8					,							snake_case      :  str = "lanczos"					,							snake_case      :  bool = True					,							):
								super().__init__()





				@staticmethod
				def 				snake_case_ (							snake_case      :  str     ):
								if images.ndim == 3:
												UpperCAmelCase_					:Optional[int]      			=      images[None, ...]
								UpperCAmelCase_					:str      			=      (images * 255).round().astype('''uint8'''     )
								if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												UpperCAmelCase_					:int      			=      [Image.fromarray(image.squeeze()					,							mode='''L'''     ) for image in images]
								else:
												UpperCAmelCase_					:Optional[Any]      			=      [Image.fromarray(image[:, :, :3]     ) for image in images]

								return pil_images





				@staticmethod
				def 				snake_case_ (							snake_case      :  Tuple     ):
								return image[:, :, 1] * 2**8 + image[:, :, 2]





				def 				snake_case_ (							self      :  Dict					,							snake_case      :  Optional[int]     ):
								if images.ndim == 3:
												UpperCAmelCase_					:Tuple      			=      images[None, ...]
								UpperCAmelCase_					:Any      			=      images[:, :, :, 3:]
								if images.shape[-1] == 6:
												UpperCAmelCase_					:Optional[int]      			=      (images_depth * 255).round().astype('''uint8'''     )
												UpperCAmelCase_					:Optional[Any]      			=      [
												    Image.fromarray(self.rgblike_to_depthmap(snake_case     )					,							mode='''I;16'''     ) for image_depth in images_depth
												]
								elif images.shape[-1] == 4:
												UpperCAmelCase_					:List[Any]      			=      (images_depth * 65_535.0).astype(np.uintaa     )
												UpperCAmelCase_					:List[Any]      			=      [Image.fromarray(snake_case					,							mode='''I;16'''     ) for image_depth in images_depth]
								else:
												raise Exception('''Not supported'''     )

								return pil_images





				def 				snake_case_ (							self      :  List[Any]					,							snake_case      :  torch.FloatTensor					,							snake_case      :  str = "pil"					,							snake_case      :  Optional[List[bool]] = None					,							):
								if not isinstance(snake_case					,							torch.Tensor     ):
												raise ValueError(
												    f'Input for postprocessing is in incorrect format: {type(snake_case     )}. We only support pytorch tensor'     )
								if output_type not in ["latent", "pt", "np", "pil"]:
												UpperCAmelCase_					:Union[str, Any]      			=      (
												    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
												    '`pil`, `np`, `pt`, `latent`'
												)
												deprecate('''Unsupported output_type'''					,							'''1.0.0'''					,							snake_case					,							standard_warn=snake_case     )
												UpperCAmelCase_					:Dict      			=      'np'

								if do_denormalize is None:
												UpperCAmelCase_					:Union[str, Any]      			=      [self.config.do_normalize] * image.shape[0]

								UpperCAmelCase_					:str      			=      torch.stack(
								    [self.denormalize(image[i]     ) if do_denormalize[i] else image[i] for i in range(image.shape[0]     )]     )

								UpperCAmelCase_					:Tuple      			=      self.pt_to_numpy(snake_case     )

								if output_type == "np":
												if image.shape[-1] == 6:
																UpperCAmelCase_					:Union[str, Any]      			=      np.stack([self.rgblike_to_depthmap(im[:, :, 3:]     ) for im in image]					,							axis=0     )
												else:
																UpperCAmelCase_					:Optional[int]      			=      image[:, :, :, 3:]
												return image[:, :, :, :3], image_depth

								if output_type == "pil":
												return self.numpy_to_pil(snake_case     ), self.numpy_to_depth(snake_case     )
								else:
												raise Exception(f'This type {output_type} is not supported'     )








"""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


__lowerCamelCase								=						logging.get_logger(__name__)

__lowerCamelCase								=						"MMBTConfig"



class  _snake_case				(							nn.Module       ):

				'''simple docstring'''




				def __init__(							self      :  Tuple					,							snake_case      :  List[Any]					,							snake_case      :  List[str]					,							snake_case      :  str     ):
								super().__init__()
								UpperCAmelCase_					:Dict      			=      config
								UpperCAmelCase_					:List[Any]      			=      encoder
								UpperCAmelCase_					:List[str]      			=      nn.Linear(config.modal_hidden_size					,							config.hidden_size     )
								UpperCAmelCase_					:List[str]      			=      embeddings.position_embeddings
								UpperCAmelCase_					:Union[str, Any]      			=      embeddings.token_type_embeddings
								UpperCAmelCase_					:Optional[int]      			=      embeddings.word_embeddings
								UpperCAmelCase_					:str      			=      embeddings.LayerNorm
								UpperCAmelCase_					:List[Any]      			=      nn.Dropout(p=config.hidden_dropout_prob     )





				def 				snake_case_ (							self      :  List[str]					,							snake_case      :  Tuple					,							snake_case      :  Any=None					,							snake_case      :  Union[str, Any]=None					,							snake_case      :  str=None					,							snake_case      :  Optional[int]=None     ):
								UpperCAmelCase_					:Any      			=      self.proj_embeddings(self.encoder(snake_case     )     )
								UpperCAmelCase_					:str      			=      token_embeddings.size(1     )

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

								if end_token is not None:
												UpperCAmelCase_					:Tuple      			=      self.word_embeddings(snake_case     )
												seq_length += 1
												UpperCAmelCase_					:Optional[Any]      			=      torch.cat([token_embeddings, end_token_embeds.unsqueeze(1     )]					,							dim=1     )

								if position_ids is None:
												UpperCAmelCase_					:Any      			=      torch.arange(snake_case					,							dtype=torch.long					,							device=input_modal.device     )
												UpperCAmelCase_					:Dict      			=      position_ids.unsqueeze(0     ).expand(input_modal.size(0     )					,							snake_case     )

								if token_type_ids is None:
												UpperCAmelCase_					:List[str]      			=      torch.zeros(
												    (input_modal.size(0     ), seq_length)					,							dtype=torch.long					,							device=input_modal.device     )

								UpperCAmelCase_					:Dict      			=      self.position_embeddings(snake_case     )
								UpperCAmelCase_					:Any      			=      self.token_type_embeddings(snake_case     )
								UpperCAmelCase_					:int      			=      token_embeddings + position_embeddings + token_type_embeddings
								UpperCAmelCase_					:List[Any]      			=      self.LayerNorm(snake_case     )
								UpperCAmelCase_					:Optional[int]      			=      self.dropout(snake_case     )
								return embeddings


__lowerCamelCase								=						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"

__lowerCamelCase								=						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."""							,						A__							,						)
class  _snake_case				(							nn.Module							,						A__       ):

				'''simple docstring'''




				def __init__(							self      :  Optional[int]					,							snake_case      :  Optional[int]					,							snake_case      :  Optional[Any]					,							snake_case      :  int     ):
								super().__init__()
								UpperCAmelCase_					:Any      			=      config
								UpperCAmelCase_					:Tuple      			=      transformer
								UpperCAmelCase_					:int      			=      ModalEmbeddings(snake_case					,							snake_case					,							transformer.embeddings     )





				@add_start_docstrings_to_model_forward(snake_case     )
				@replace_return_docstrings(output_type=snake_case					,							config_class=_CONFIG_FOR_DOC     )
				def 				snake_case_ (							self      :  Any					,							snake_case      :  Dict					,							snake_case      :  Optional[int]=None					,							snake_case      :  Any=None					,							snake_case      :  Any=None					,							snake_case      :  Optional[int]=None					,							snake_case      :  Any=None					,							snake_case      :  Optional[int]=None					,							snake_case      :  Optional[int]=None					,							snake_case      :  Tuple=None					,							snake_case      :  str=None					,							snake_case      :  Optional[Any]=None					,							snake_case      :  Tuple=None					,							snake_case      :  Optional[int]=None					,							snake_case      :  List[Any]=None					,							snake_case      :  int=None					,							snake_case      :  int=None					,							):
								UpperCAmelCase_					:Tuple      			=      output_attentions if output_attentions is not None else self.config.output_attentions
								UpperCAmelCase_					:str      			=      (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase_					:Optional[Any]      			=      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_					:Dict      			=      input_ids.size()
								elif inputs_embeds is not None:
												UpperCAmelCase_					:Dict      			=      inputs_embeds.size()[:-1]
								else:
												raise ValueError('''You have to specify either input_ids or inputs_embeds'''     )

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

								UpperCAmelCase_					:Optional[int]      			=      self.modal_encoder(
								    snake_case					,							start_token=snake_case					,							end_token=snake_case					,							position_ids=snake_case					,							token_type_ids=snake_case					,							)

								UpperCAmelCase_					:List[Any]      			=      modal_embeddings.size()[:-1]

								if token_type_ids is None:
												UpperCAmelCase_					:Dict      			=      torch.ones(snake_case					,							dtype=torch.long					,							device=snake_case     )

								UpperCAmelCase_					:Dict      			=      self.transformer.embeddings(
								    input_ids=snake_case					,							position_ids=snake_case					,							token_type_ids=snake_case					,							inputs_embeds=snake_case     )

								UpperCAmelCase_					:Optional[int]      			=      torch.cat([modal_embeddings, txt_embeddings]					,							1     )

								UpperCAmelCase_					:Dict      			=      embedding_output.size()[:-1]

								if attention_mask is None:
												UpperCAmelCase_					:Tuple      			=      torch.ones(snake_case					,							device=snake_case     )
								else:
												UpperCAmelCase_					:int      			=      torch.cat(
												    [torch.ones(snake_case					,							device=snake_case					,							dtype=torch.long     ), attention_mask]					,							dim=1     )
								if encoder_attention_mask is None:
												UpperCAmelCase_					:str      			=      torch.ones(snake_case					,							device=snake_case     )
								else:
												UpperCAmelCase_					:Optional[int]      			=      torch.cat(
												    [torch.ones(snake_case					,							device=snake_case     ), encoder_attention_mask]					,							dim=1     )

								UpperCAmelCase_					:Union[str, Any]      			=      self.get_extended_attention_mask(snake_case					,							snake_case     )
								UpperCAmelCase_					:str      			=      self.invert_attention_mask(snake_case     )
								UpperCAmelCase_					:Optional[Any]      			=      self.get_head_mask(snake_case					,							self.config.num_hidden_layers     )

								UpperCAmelCase_					:str      			=      self.transformer.encoder(
								    snake_case					,							attention_mask=snake_case					,							head_mask=snake_case					,							encoder_hidden_states=snake_case					,							encoder_attention_mask=snake_case					,							output_attentions=snake_case					,							output_hidden_states=snake_case					,							return_dict=snake_case					,							)

								UpperCAmelCase_					:Dict      			=      encoder_outputs[0]
								UpperCAmelCase_					:List[str]      			=      self.transformer.pooler(snake_case     )

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

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





				def 				snake_case_ (							self      :  Optional[int]     ):
								return self.embeddings.word_embeddings





				def 				snake_case_ (							self      :  Tuple					,							snake_case      :  Optional[Any]     ):
								UpperCAmelCase_					:Optional[int]      			=      value



@add_start_docstrings(
    """
    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
    """							,						A__							,						A__							,						)
class  _snake_case				(							nn.Module       ):

				'''simple docstring'''




				def __init__(							self      :  Dict					,							snake_case      :  Any					,							snake_case      :  List[Any]					,							snake_case      :  Optional[Any]     ):
								super().__init__()
								UpperCAmelCase_					:Dict      			=      config.num_labels

								UpperCAmelCase_					:List[Any]      			=      MMBTModel(snake_case					,							snake_case					,							snake_case     )
								UpperCAmelCase_					:Optional[int]      			=      nn.Dropout(config.hidden_dropout_prob     )
								UpperCAmelCase_					:int      			=      nn.Linear(config.hidden_size					,							config.num_labels     )





				def 				snake_case_ (							self      :  int					,							snake_case      :  int					,							snake_case      :  Optional[Any]=None					,							snake_case      :  Any=None					,							snake_case      :  Any=None					,							snake_case      :  List[str]=None					,							snake_case      :  Union[str, Any]=None					,							snake_case      :  Optional[int]=None					,							snake_case      :  Optional[Any]=None					,							snake_case      :  List[Any]=None					,							snake_case      :  Optional[Any]=None					,							snake_case      :  Optional[Any]=None					,							snake_case      :  Any=None					,							snake_case      :  str=None					,							):
								UpperCAmelCase_					:Any      			=      return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_					:Union[str, Any]      			=      self.mmbt(
								    input_modal=snake_case					,							input_ids=snake_case					,							modal_start_tokens=snake_case					,							modal_end_tokens=snake_case					,							attention_mask=snake_case					,							token_type_ids=snake_case					,							modal_token_type_ids=snake_case					,							position_ids=snake_case					,							modal_position_ids=snake_case					,							head_mask=snake_case					,							inputs_embeds=snake_case					,							return_dict=snake_case					,							)

								UpperCAmelCase_					:Any      			=      outputs[1]

								UpperCAmelCase_					:Any      			=      self.dropout(snake_case     )
								UpperCAmelCase_					:int      			=      self.classifier(snake_case     )

								UpperCAmelCase_					:Dict      			=      None
								if labels is not None:
												if self.num_labels == 1:
																#  We are doing regression
																UpperCAmelCase_					:Optional[int]      			=      MSELoss()
																UpperCAmelCase_					:Union[str, Any]      			=      loss_fct(logits.view(-1     )					,							labels.view(-1     )     )
												else:
																UpperCAmelCase_					:Union[str, Any]      			=      CrossEntropyLoss()
																UpperCAmelCase_					:str      			=      loss_fct(logits.view(-1					,							self.num_labels     )					,							labels.view(-1     )     )

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

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


