



"""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


UpperCAmelCase:  Optional[Any]			     =				logging.get_logger(__name__)

UpperCAmelCase:  int			     =				"""MMBTConfig"""





class   UpperCamelCase	(     nn.Module       ):




	"""simple docstring"""






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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_=None   ):
			_lowercase				: str     =   self.proj_embeddings(self.encoder(UpperCAmelCase_   )   )
			_lowercase				: Union[str, Any]     =   token_embeddings.size(1   )

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

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

			if position_ids is None:
					_lowercase				: int     =   torch.arange(UpperCAmelCase_				,dtype=torch.long				,device=input_modal.device   )
					_lowercase				: Tuple     =   position_ids.unsqueeze(0   ).expand(input_modal.size(0   )				,UpperCAmelCase_   )

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

			_lowercase				: str     =   self.position_embeddings(UpperCAmelCase_   )
			_lowercase				: List[str]     =   self.token_type_embeddings(UpperCAmelCase_   )
			_lowercase				: List[Any]     =   token_embeddings + position_embeddings + token_type_embeddings
			_lowercase				: Union[str, Any]     =   self.LayerNorm(UpperCAmelCase_   )
			_lowercase				: Dict     =   self.dropout(UpperCAmelCase_   )
			return embeddings







UpperCAmelCase:  List[Any]			     =				r"""
    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and
    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
    It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and
    obtain state-of-the-art performance on various multimodal classification benchmark tasks.

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration.
        transformer (`nn.Module`): A text transformer that is used by MMBT.
            It should have embeddings, encoder, and pooler attributes.
        encoder (`nn.Module`): Encoder for the second modality.
            It should take in a batch of modal inputs and return k, n dimension embeddings.
"""

UpperCAmelCase:  Tuple			     =				r"""
    Args:
        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):
            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image
            Encoder, the shape would be (batch_size, channels, height, width)
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's
            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See
            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification
            tasks.
        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.
        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:
            Segment token indices to indicate different portions of the non-text modality. The embeddings from these
            tokens will be summed with the respective token embeddings for the non-text modality.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.
            Selected in the range `[0, config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""





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




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			super().__init__()
			_lowercase				: Any     =   config
			_lowercase				: str     =   transformer
			_lowercase				: List[str]     =   ModalEmbeddings(UpperCAmelCase_				,UpperCAmelCase_				,transformer.embeddings   )






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

			_lowercase				: Optional[int]     =   input_ids.device if input_ids is not None else inputs_embeds.device

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

			_lowercase				: Optional[Any]     =   modal_embeddings.size()[:-1]

			if token_type_ids is None:
					_lowercase				: List[str]     =   torch.ones(UpperCAmelCase_				,dtype=torch.long				,device=UpperCAmelCase_   )

			_lowercase				: Tuple     =   self.transformer.embeddings(
			    input_ids=UpperCAmelCase_				,position_ids=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,inputs_embeds=UpperCAmelCase_   )

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

			_lowercase				: str     =   embedding_output.size()[:-1]

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

			_lowercase				: Optional[Any]     =   self.get_extended_attention_mask(UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: Optional[Any]     =   self.invert_attention_mask(UpperCAmelCase_   )
			_lowercase				: int     =   self.get_head_mask(UpperCAmelCase_				,self.config.num_hidden_layers   )

			_lowercase				: List[str]     =   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_				,)

			_lowercase				: List[Any]     =   encoder_outputs[0]
			_lowercase				: Optional[Any]     =   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    lowerCamelCase__					(     self   ):
			return self.embeddings.word_embeddings






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: 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    "					,   snake_case					,   snake_case					,   )
class   UpperCamelCase	(     nn.Module       ):




	"""simple docstring"""






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

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






	def    lowerCamelCase__					(     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				,):
			_lowercase				: List[str]     =   return_dict if return_dict is not None else self.config.use_return_dict

			_lowercase				: List[str]     =   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_				,)

			_lowercase				: str     =   outputs[1]

			_lowercase				: Union[str, Any]     =   self.dropout(UpperCAmelCase_   )
			_lowercase				: List[str]     =   self.classifier(UpperCAmelCase_   )

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

			if not return_dict:
					_lowercase				: List[str]     =   (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				,)








import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__lowerCamelCase								=      logging.getLogger(__name__)


@dataclass
class 	snake_case_			:




       """simple docstring"""






       _lowerCamelCase           =					field(
           metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	)
       _lowerCamelCase           =					field(
           default=lowercase__			,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
       _lowerCamelCase           =					field(
           default=lowercase__			,			metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}	)
       _lowerCamelCase           =					field(default=lowercase__			,			metadata={"""help""": """Set this flag to use fast tokenization."""}	)
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       _lowerCamelCase           =					field(
           default=lowercase__			,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,			)







def  _snake_case			(							)  ->  Union[str, Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

      if len(sys.argv     ) == 2 and sys.argv[1].endswith(".json"     ):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            UpperCAmelCase_   ,   UpperCAmelCase_   ,   UpperCAmelCase_	:						Any					   =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
      else:
            UpperCAmelCase_   ,   UpperCAmelCase_   ,   UpperCAmelCase_	:						Union[str, Any]					   =		parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir     )
          and os.listdir(training_args.output_dir     )
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                " --overwrite_output_dir to overcome."     )

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"       ,	datefmt="%m/%d/%Y %H:%M:%S"       ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,	)
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"       ,	training_args.local_rank       ,	training_args.device       ,	training_args.n_gpu       ,	bool(training_args.local_rank != -1     )       ,	training_args.fpaa       ,	)
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank     ):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s"       ,	__snake_case     )

      # Prepare Question-Answering task
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      UpperCAmelCase_	:						Any					   =		AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,	cache_dir=model_args.cache_dir       ,	)
      UpperCAmelCase_	:						List[str]					   =		AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,	cache_dir=model_args.cache_dir       ,	use_fast=__snake_case       ,	)
      UpperCAmelCase_	:						Optional[int]					   =		AutoModelForQuestionAnswering.from_pretrained(
          model_args.model_name_or_path       ,	from_tf=bool(".ckpt" in model_args.model_name_or_path     )       ,	config=__snake_case       ,	cache_dir=model_args.cache_dir       ,	)

      # Get datasets
      UpperCAmelCase_	:						str					   =		hasattr(model.config       ,	"lang2id"     )
      UpperCAmelCase_	:						Optional[Any]					   =		(
          SquadDataset(
              __snake_case       ,	tokenizer=__snake_case       ,	is_language_sensitive=__snake_case       ,	cache_dir=model_args.cache_dir     )
          if training_args.do_train
          else None
      )
      UpperCAmelCase_	:						Optional[Any]					   =		(
          SquadDataset(
              __snake_case       ,	tokenizer=__snake_case       ,	mode="dev"       ,	is_language_sensitive=__snake_case       ,	cache_dir=model_args.cache_dir       ,	)
          if training_args.do_eval
          else None
      )

      # Data collator
      UpperCAmelCase_	:						Optional[Any]					   =		DataCollatorWithPadding(__snake_case       ,	pad_to_multiple_of=8     ) if training_args.fpaa else None

      # Initialize our Trainer
      UpperCAmelCase_	:						Any					   =		Trainer(
          model=__snake_case       ,	args=__snake_case       ,	train_dataset=__snake_case       ,	eval_dataset=__snake_case       ,	data_collator=__snake_case       ,	)

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir     )







def  _snake_case			(							__snake_case     )  ->  List[str]:






      '''simple docstring'''







      main()


if __name__ == "__main__":
   main()
