




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					:     str					  =  logging.get_logger(__name__)

_lowerCamelCase					:     str					  =  "MMBTConfig"
class 				__snake_case     (nn.Module						):



				def __init__(					self					:						List[Any]						,		_UpperCAmelCase					:						Union[str, Any]						,		_UpperCAmelCase					:						Union[str, Any]						,		_UpperCAmelCase					:						Dict					)     ->			Optional[int]:





								'''simple docstring'''




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



				def 					SCREAMING_SNAKE_CASE				(					self					:						str						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						Tuple=None						,		_UpperCAmelCase					:						Dict=None						,		_UpperCAmelCase					:						List[Any]=None						,		_UpperCAmelCase					:						int=None					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					self.proj_embeddings(self.encoder(_UpperCAmelCase					)					)
								_lowerCAmelCase			:			Tuple        =					token_embeddings.size(1					)

								if start_token is not None:
												_lowerCAmelCase			:			List[Any]        =					self.word_embeddings(_UpperCAmelCase					)
												seq_length += 1
												_lowerCAmelCase			:			int        =					torch.cat([start_token_embeds.unsqueeze(1					), token_embeddings]						,		dim=1					)

								if end_token is not None:
												_lowerCAmelCase			:			Optional[int]        =					self.word_embeddings(_UpperCAmelCase					)
												seq_length += 1
												_lowerCAmelCase			:			Dict        =					torch.cat([token_embeddings, end_token_embeds.unsqueeze(1					)]						,		dim=1					)

								if position_ids is None:
												_lowerCAmelCase			:			List[Any]        =					torch.arange(_UpperCAmelCase						,		dtype=torch.long						,		device=input_modal.device					)
												_lowerCAmelCase			:			str        =					position_ids.unsqueeze(0					).expand(input_modal.size(0					)						,		_UpperCAmelCase					)

								if token_type_ids is None:
												_lowerCAmelCase			:			Optional[Any]        =					torch.zeros(
												    (input_modal.size(0					), seq_length)						,		dtype=torch.long						,		device=input_modal.device					)

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






_lowerCamelCase					:     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"

_lowerCamelCase					:     int					  =  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						):



				def __init__(					self					:						Tuple						,		_UpperCAmelCase					:						Optional[int]						,		_UpperCAmelCase					:						Any						,		_UpperCAmelCase					:						Dict					)     ->			str:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase			:			int        =					config
								_lowerCAmelCase			:			List[str]        =					transformer
								_lowerCAmelCase			:			Any        =					ModalEmbeddings(_UpperCAmelCase						,		_UpperCAmelCase						,		transformer.embeddings					)



				@add_start_docstrings_to_model_forward(_UpperCAmelCase					)
				@replace_return_docstrings(output_type=_UpperCAmelCase						,		config_class=_CONFIG_FOR_DOC					)
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]						,		_UpperCAmelCase					:						Union[str, Any]						,		_UpperCAmelCase					:						List[str]=None						,		_UpperCAmelCase					:						int=None						,		_UpperCAmelCase					:						Union[str, Any]=None						,		_UpperCAmelCase					:						str=None						,		_UpperCAmelCase					:						Dict=None						,		_UpperCAmelCase					:						Union[str, Any]=None						,		_UpperCAmelCase					:						List[str]=None						,		_UpperCAmelCase					:						List[Any]=None						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						List[str]=None						,		_UpperCAmelCase					:						Dict=None						,		_UpperCAmelCase					:						List[str]=None						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						List[str]=None						,		_UpperCAmelCase					:						str=None						,		)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					output_attentions if output_attentions is not None else self.config.output_attentions
								_lowerCAmelCase			:			List[str]        =					(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								_lowerCAmelCase			:			Dict        =					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:
												_lowerCAmelCase			:			int        =					input_ids.size()
								elif inputs_embeds is not None:
												_lowerCAmelCase			:			List[Any]        =					inputs_embeds.size()[:-1]
								else:
												raise ValueError("""You have to specify either input_ids or inputs_embeds"""					)

								_lowerCAmelCase			:			Dict        =					input_ids.device if input_ids is not None else inputs_embeds.device

								_lowerCAmelCase			:			Union[str, Any]        =					self.modal_encoder(
								    _UpperCAmelCase						,		start_token=_UpperCAmelCase						,		end_token=_UpperCAmelCase						,		position_ids=_UpperCAmelCase						,		token_type_ids=_UpperCAmelCase						,		)

								_lowerCAmelCase			:			List[str]        =					modal_embeddings.size()[:-1]

								if token_type_ids is None:
												_lowerCAmelCase			:			List[Any]        =					torch.ones(_UpperCAmelCase						,		dtype=torch.long						,		device=_UpperCAmelCase					)

								_lowerCAmelCase			:			List[str]        =					self.transformer.embeddings(
								    input_ids=_UpperCAmelCase						,		position_ids=_UpperCAmelCase						,		token_type_ids=_UpperCAmelCase						,		inputs_embeds=_UpperCAmelCase					)

								_lowerCAmelCase			:			List[Any]        =					torch.cat([modal_embeddings, txt_embeddings]						,		1					)

								_lowerCAmelCase			:			Optional[int]        =					embedding_output.size()[:-1]

								if attention_mask is None:
												_lowerCAmelCase			:			Dict        =					torch.ones(_UpperCAmelCase						,		device=_UpperCAmelCase					)
								else:
												_lowerCAmelCase			:			List[str]        =					torch.cat(
												    [torch.ones(_UpperCAmelCase						,		device=_UpperCAmelCase						,		dtype=torch.long					), attention_mask]						,		dim=1					)
								if encoder_attention_mask is None:
												_lowerCAmelCase			:			List[Any]        =					torch.ones(_UpperCAmelCase						,		device=_UpperCAmelCase					)
								else:
												_lowerCAmelCase			:			str        =					torch.cat(
												    [torch.ones(_UpperCAmelCase						,		device=_UpperCAmelCase					), encoder_attention_mask]						,		dim=1					)

								_lowerCAmelCase			:			Tuple        =					self.get_extended_attention_mask(_UpperCAmelCase						,		_UpperCAmelCase					)
								_lowerCAmelCase			:			Union[str, Any]        =					self.invert_attention_mask(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					self.get_head_mask(_UpperCAmelCase						,		self.config.num_hidden_layers					)

								_lowerCAmelCase			:			Dict        =					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						,		)

								_lowerCAmelCase			:			List[str]        =					encoder_outputs[0]
								_lowerCAmelCase			:			Tuple        =					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 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Union[str, Any]:





								'''simple docstring'''




								return self.embeddings.word_embeddings



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						Tuple					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					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    "  ,		_a  ,		_a  ,		)
class 				__snake_case     (nn.Module						):



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						List[str]						,		_UpperCAmelCase					:						List[str]						,		_UpperCAmelCase					:						List[str]					)     ->			Any:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase			:			Union[str, Any]        =					config.num_labels

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



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						Dict						,		_UpperCAmelCase					:						Tuple=None						,		_UpperCAmelCase					:						Union[str, Any]=None						,		_UpperCAmelCase					:						int=None						,		_UpperCAmelCase					:						int=None						,		_UpperCAmelCase					:						Tuple=None						,		_UpperCAmelCase					:						Union[str, Any]=None						,		_UpperCAmelCase					:						Optional[Any]=None						,		_UpperCAmelCase					:						Dict=None						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						Union[str, Any]=None						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						Optional[int]=None						,		)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					return_dict if return_dict is not None else self.config.use_return_dict

								_lowerCAmelCase			:			Optional[int]        =					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						,		)

								_lowerCAmelCase			:			Optional[Any]        =					outputs[1]

								_lowerCAmelCase			:			str        =					self.dropout(_UpperCAmelCase					)
								_lowerCAmelCase			:			Union[str, Any]        =					self.classifier(_UpperCAmelCase					)

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

								if not return_dict:
												_lowerCAmelCase			:			Tuple        =					(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						,		)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     List[str]					  =  {
    "ZinengTang/tvlt-base": "https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json",
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "tvlt"



				def __init__(					self					:						Dict						,		_UpperCAmelCase					:						Any=224						,		_UpperCAmelCase					:						Any=2048						,		_UpperCAmelCase					:						Optional[int]=128						,		_UpperCAmelCase					:						Tuple=[16, 16]						,		_UpperCAmelCase					:						Optional[int]=[16, 16]						,		_UpperCAmelCase					:						Tuple=3						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Dict=8						,		_UpperCAmelCase					:						Any=768						,		_UpperCAmelCase					:						Dict=12						,		_UpperCAmelCase					:						Any=12						,		_UpperCAmelCase					:						Optional[Any]=3072						,		_UpperCAmelCase					:						Tuple="gelu"						,		_UpperCAmelCase					:						List[str]=0.0						,		_UpperCAmelCase					:						Any=0.0						,		_UpperCAmelCase					:						Optional[int]=0.02						,		_UpperCAmelCase					:						int=1E-6						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						Dict=False						,		_UpperCAmelCase					:						str=16						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Optional[Any]=8						,		_UpperCAmelCase					:						Optional[Any]=2048						,		_UpperCAmelCase					:						Optional[int]=0.75						,		_UpperCAmelCase					:						List[Any]=0.15						,		_UpperCAmelCase					:						int="frame-level"						,		_UpperCAmelCase					:						Union[str, Any]=True						,		_UpperCAmelCase					:						Optional[int]=True						,		_UpperCAmelCase					:						Any="classification"						,		**_UpperCAmelCase					:						Tuple						,		)     ->			List[str]:





								'''simple docstring'''




								super().__init__(**_UpperCAmelCase					)

								if audio_mask_type not in ("frame-level", "patch_level"):
												raise ValueError(
												    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
												    f"got {audio_mask_type}"					)

								_lowerCAmelCase			:			Tuple        =					image_size
								_lowerCAmelCase			:			List[str]        =					spectrogram_length
								_lowerCAmelCase			:			str        =					frequency_length
								_lowerCAmelCase			:			Dict        =					image_patch_size
								_lowerCAmelCase			:			Tuple        =					audio_patch_size
								_lowerCAmelCase			:			str        =					num_image_channels
								_lowerCAmelCase			:			Dict        =					num_audio_channels
								_lowerCAmelCase			:			str        =					num_frames

								_lowerCAmelCase			:			Any        =					hidden_size
								_lowerCAmelCase			:			List[str]        =					num_hidden_layers
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			Any        =					hidden_act
								_lowerCAmelCase			:			Any        =					hidden_dropout_prob
								_lowerCAmelCase			:			Union[str, Any]        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			Tuple        =					layer_norm_eps
								_lowerCAmelCase			:			str        =					qkv_bias
								_lowerCAmelCase			:			Tuple        =					use_mean_pooling

								_lowerCAmelCase			:			Union[str, Any]        =					decoder_num_attention_heads
								_lowerCAmelCase			:			int        =					decoder_hidden_size
								_lowerCAmelCase			:			List[str]        =					decoder_num_hidden_layers
								_lowerCAmelCase			:			Optional[int]        =					decoder_intermediate_size
								_lowerCAmelCase			:			Any        =					pixel_mask_ratio
								_lowerCAmelCase			:			int        =					audio_mask_ratio
								_lowerCAmelCase			:			Optional[int]        =					audio_mask_type

								_lowerCAmelCase			:			Dict        =					task_matching
								_lowerCAmelCase			:			Any        =					task_mae
								_lowerCAmelCase			:			int        =					loss_type

