






import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCamelCase		=	logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowerCamelCase		=	{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase		=	{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase		=	sorted(arg_to_scheduler.keys())
lowerCamelCase		=	'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''





class       _a							(       pl.LightningModule):




	def __init__(      self		:	Any	,      _SCREAMING_SNAKE_CASE		:	argparse.Namespace	,      _SCREAMING_SNAKE_CASE		:	Tuple=None	,      _SCREAMING_SNAKE_CASE		:	int="base"	,      _SCREAMING_SNAKE_CASE		:	List[str]=None	,      _SCREAMING_SNAKE_CASE		:	Tuple=None	,      _SCREAMING_SNAKE_CASE		:	int=None	,      **_SCREAMING_SNAKE_CASE		:	List[Any]	,      )-> List[Any]:
			super().__init__()
			# TODO: move to self.save_hyperparameters()
			# self.save_hyperparameters()
			# can also expand arguments into trainer signature for easier reading

			self.save_hyperparameters(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Union[str, Any]					  =      0
			lowerCAmelCase__     :    Union[str, Any]					  =      Path(self.hparams.output_dir	)
			lowerCAmelCase__     :    str					  =      self.hparams.cache_dir if self.hparams.cache_dir else None
			if config is None:
					lowerCAmelCase__     :    str					  =      AutoConfig.from_pretrained(
					    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path	,      **({'''num_labels''': num_labels} if num_labels is not None else {})	,      cache_dir=_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE	,      )
			else:
					lowerCAmelCase__     :    PretrainedConfig					  =      config

			lowerCAmelCase__     :    int					  =      ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
			for p in extra_model_params:
					if getattr(self.hparams	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	):
							assert hasattr(self.config	,      _SCREAMING_SNAKE_CASE	), F'model config doesn\'t have a `{p}` attribute'
							setattr(self.config	,      _SCREAMING_SNAKE_CASE	,      getattr(self.hparams	,      _SCREAMING_SNAKE_CASE	)	)

			if tokenizer is None:
					lowerCAmelCase__     :    Any					  =      AutoTokenizer.from_pretrained(
					    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,      cache_dir=_SCREAMING_SNAKE_CASE	,      )
			else:
					lowerCAmelCase__     :    PreTrainedTokenizer					  =      tokenizer
			lowerCAmelCase__     :    List[str]					  =      MODEL_MODES[mode]
			if model is None:
					lowerCAmelCase__     :    Dict					  =      self.model_type.from_pretrained(
					    self.hparams.model_name_or_path	,      from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path	)	,      config=self.config	,      cache_dir=_SCREAMING_SNAKE_CASE	,      )
			else:
					lowerCAmelCase__     :    Optional[Any]					  =      model




	def 							UpperCAmelCase__(      self		:	Dict	,      *_SCREAMING_SNAKE_CASE		:	Any	,      **_SCREAMING_SNAKE_CASE		:	Tuple	)-> Union[str, Any]:
			lowerCAmelCase__     :    Tuple					  =      self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE	)




	def 							UpperCAmelCase__(      self		:	Optional[Any]	)-> Any:
			lowerCAmelCase__     :    List[str]					  =      arg_to_scheduler[self.hparams.lr_scheduler]
			lowerCAmelCase__     :    int					  =      get_schedule_func(
			    self.opt	,      num_warmup_steps=self.hparams.warmup_steps	,      num_training_steps=self.total_steps()	)
			lowerCAmelCase__     :    Any					  =      {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
			return scheduler




	def 							UpperCAmelCase__(      self		:	Dict	)-> Union[str, Any]:
			lowerCAmelCase__     :    List[Any]					  =      self.model
			lowerCAmelCase__     :    Union[str, Any]					  =      ['''bias''', '''LayerNorm.weight''']
			lowerCAmelCase__     :    List[Any]					  =      [
			    {
			        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
			        '''weight_decay''': self.hparams.weight_decay,
			    },
			    {
			        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
			        '''weight_decay''': 0.0,
			    },
			]
			if self.hparams.adafactor:
					lowerCAmelCase__     :    List[str]					  =      Adafactor(
					    _SCREAMING_SNAKE_CASE	,      lr=self.hparams.learning_rate	,      scale_parameter=_SCREAMING_SNAKE_CASE	,      relative_step=_SCREAMING_SNAKE_CASE	)

			else:
					lowerCAmelCase__     :    List[Any]					  =      AdamW(
					    _SCREAMING_SNAKE_CASE	,      lr=self.hparams.learning_rate	,      eps=self.hparams.adam_epsilon	)
			lowerCAmelCase__     :    Tuple					  =      optimizer

			lowerCAmelCase__     :    List[Any]					  =      self.get_lr_scheduler()

			return [optimizer], [scheduler]




	def 							UpperCAmelCase__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]	)-> Union[str, Any]:
			return self.validation_step(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)




	def 							UpperCAmelCase__(      self		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	List[Any]	)-> Any:
			return self.validation_end(_SCREAMING_SNAKE_CASE	)




	def 							UpperCAmelCase__(      self		:	str	)-> int:
			lowerCAmelCase__     :    Union[str, Any]					  =      max(1	,      self.hparams.gpus	)  # TODO: consider num_tpu_cores
			lowerCAmelCase__     :    Optional[int]					  =      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
			return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	int	)-> List[Any]:
			if stage == "test":
					lowerCAmelCase__     :    Optional[Any]					  =      len(self.test_dataloader().dataset	)
			else:
					lowerCAmelCase__     :    List[Any]					  =      self.get_dataloader('''train'''	,      self.hparams.train_batch_size	,      shuffle=_SCREAMING_SNAKE_CASE	)
					lowerCAmelCase__     :    List[Any]					  =      len(self.train_dataloader().dataset	)




	def 							UpperCAmelCase__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	bool = False	)-> Optional[int]:
			raise NotImplementedError('''You must implement this for your task'''	)




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Optional[int]:
			return self.train_loader




	def 							UpperCAmelCase__(      self		:	List[str]	)-> Union[str, Any]:
			return self.get_dataloader('''dev'''	,      self.hparams.eval_batch_size	,      shuffle=_SCREAMING_SNAKE_CASE	)




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> List[str]:
			return self.get_dataloader('''test'''	,      self.hparams.eval_batch_size	,      shuffle=_SCREAMING_SNAKE_CASE	)




	def 							UpperCAmelCase__(      self		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	List[Any]	)-> Optional[int]:
			return os.path.join(
			    self.hparams.data_dir	,      '''cached_{}_{}_{}'''.format(
			        _SCREAMING_SNAKE_CASE	,      list(filter(_SCREAMING_SNAKE_CASE	,      self.hparams.model_name_or_path.split('''/'''	)	)	).pop()	,      str(self.hparams.max_seq_length	)	,      )	,      )




	@pl.utilities.rank_zero_only
	def 							UpperCAmelCase__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	Dict[str, Any]	)-> None:
			lowerCAmelCase__     :    List[str]					  =      self.output_dir.joinpath('''best_tfmr'''	)
			lowerCAmelCase__     :    List[Any]					  =      self.step_count
			self.model.save_pretrained(_SCREAMING_SNAKE_CASE	)
			self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE	)




	@staticmethod
	def 							UpperCAmelCase__(      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	int	)-> Optional[Any]:
			parser.add_argument(
			    '''--model_name_or_path'''	,      default=_SCREAMING_SNAKE_CASE	,      type=_SCREAMING_SNAKE_CASE	,      required=_SCREAMING_SNAKE_CASE	,      help='''Path to pretrained model or model identifier from huggingface.co/models'''	,      )
			parser.add_argument(
			    '''--config_name'''	,      default=''''''	,      type=_SCREAMING_SNAKE_CASE	,      help='''Pretrained config name or path if not the same as model_name'''	)
			parser.add_argument(
			    '''--tokenizer_name'''	,      default=_SCREAMING_SNAKE_CASE	,      type=_SCREAMING_SNAKE_CASE	,      help='''Pretrained tokenizer name or path if not the same as model_name'''	,      )
			parser.add_argument(
			    '''--cache_dir'''	,      default=''''''	,      type=_SCREAMING_SNAKE_CASE	,      help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''	,      )
			parser.add_argument(
			    '''--encoder_layerdrop'''	,      type=_SCREAMING_SNAKE_CASE	,      help='''Encoder layer dropout probability (Optional). Goes into model.config'''	,      )
			parser.add_argument(
			    '''--decoder_layerdrop'''	,      type=_SCREAMING_SNAKE_CASE	,      help='''Decoder layer dropout probability (Optional). Goes into model.config'''	,      )
			parser.add_argument(
			    '''--dropout'''	,      type=_SCREAMING_SNAKE_CASE	,      help='''Dropout probability (Optional). Goes into model.config'''	,      )
			parser.add_argument(
			    '''--attention_dropout'''	,      type=_SCREAMING_SNAKE_CASE	,      help='''Attention dropout probability (Optional). Goes into model.config'''	,      )
			parser.add_argument('''--learning_rate'''	,      default=5E-5	,      type=_SCREAMING_SNAKE_CASE	,      help='''The initial learning rate for Adam.'''	)
			parser.add_argument(
			    '''--lr_scheduler'''	,      default='''linear'''	,      choices=_SCREAMING_SNAKE_CASE	,      metavar=_SCREAMING_SNAKE_CASE	,      type=_SCREAMING_SNAKE_CASE	,      help='''Learning rate scheduler'''	,      )
			parser.add_argument('''--weight_decay'''	,      default=0.0	,      type=_SCREAMING_SNAKE_CASE	,      help='''Weight decay if we apply some.'''	)
			parser.add_argument('''--adam_epsilon'''	,      default=1E-8	,      type=_SCREAMING_SNAKE_CASE	,      help='''Epsilon for Adam optimizer.'''	)
			parser.add_argument('''--warmup_steps'''	,      default=0	,      type=_SCREAMING_SNAKE_CASE	,      help='''Linear warmup over warmup_steps.'''	)
			parser.add_argument('''--num_workers'''	,      default=4	,      type=_SCREAMING_SNAKE_CASE	,      help='''kwarg passed to DataLoader'''	)
			parser.add_argument('''--num_train_epochs'''	,      dest='''max_epochs'''	,      default=3	,      type=_SCREAMING_SNAKE_CASE	)
			parser.add_argument('''--train_batch_size'''	,      default=32	,      type=_SCREAMING_SNAKE_CASE	)
			parser.add_argument('''--eval_batch_size'''	,      default=32	,      type=_SCREAMING_SNAKE_CASE	)
			parser.add_argument('''--adafactor'''	,      action='''store_true'''	)





class       _a							(       pl.Callback):




	def 							UpperCAmelCase__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	Optional[int]	)-> str:
			if (
			    trainer.is_global_zero and trainer.global_rank == 0
			):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
					pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.





class       _a							(       pl.Callback):




	def 							UpperCAmelCase__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	List[Any]	)-> Union[str, Any]:
			lowerCAmelCase__     :    int					  =      trainer.lr_schedulers[0]['''scheduler''']
			lowerCAmelCase__     :    Any					  =      {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
			pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE	)




	def 							UpperCAmelCase__(      self		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	pl.Trainer	,      _SCREAMING_SNAKE_CASE		:	pl.LightningModule	)-> Any:
			rank_zero_info('''***** Validation results *****'''	)
			lowerCAmelCase__     :    Tuple					  =      trainer.callback_metrics
			# Log results
			for key in sorted(_SCREAMING_SNAKE_CASE	):
					if key not in ["log", "progress_bar"]:
							rank_zero_info('''{} = {}\n'''.format(_SCREAMING_SNAKE_CASE	,      str(metrics[key]	)	)	)




	def 							UpperCAmelCase__(      self		:	Any	,      _SCREAMING_SNAKE_CASE		:	pl.Trainer	,      _SCREAMING_SNAKE_CASE		:	pl.LightningModule	)-> Optional[Any]:
			rank_zero_info('''***** Test results *****'''	)
			lowerCAmelCase__     :    str					  =      trainer.callback_metrics
			# Log and save results to file
			lowerCAmelCase__     :    Dict					  =      os.path.join(pl_module.hparams.output_dir	,      '''test_results.txt'''	)
			with open(_SCREAMING_SNAKE_CASE	,      '''w'''	) as writer:
					for key in sorted(_SCREAMING_SNAKE_CASE	):
							if key not in ["log", "progress_bar"]:
									rank_zero_info('''{} = {}\n'''.format(_SCREAMING_SNAKE_CASE	,      str(metrics[key]	)	)	)
									writer.write('''{} = {}\n'''.format(_SCREAMING_SNAKE_CASE	,      str(metrics[key]	)	)	)






def   lowerCamelCase_					( _a			,     _a ):




		"""simple docstring"""






		parser.add_argument(
		    '''--output_dir'''			,     default=_a			,     type=_a			,     required=_a			,     help='''The output directory where the model predictions and checkpoints will be written.'''			,     )
		parser.add_argument(
		    '''--fp16'''			,     action='''store_true'''			,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''			,     )

		parser.add_argument(
		    '''--fp16_opt_level'''			,     type=_a			,     default='''O2'''			,     help=(
		        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
		        '''See details at https://nvidia.github.io/apex/amp.html'''
		    )			,     )
		parser.add_argument('''--n_tpu_cores'''			,     dest='''tpu_cores'''			,     type=_a )
		parser.add_argument('''--max_grad_norm'''			,     dest='''gradient_clip_val'''			,     default=1.0			,     type=_a			,     help='''Max gradient norm''' )
		parser.add_argument('''--do_train'''			,     action='''store_true'''			,     help='''Whether to run training.''' )
		parser.add_argument('''--do_predict'''			,     action='''store_true'''			,     help='''Whether to run predictions on the test set.''' )
		parser.add_argument(
		    '''--gradient_accumulation_steps'''			,     dest='''accumulate_grad_batches'''			,     type=_a			,     default=1			,     help='''Number of updates steps to accumulate before performing a backward/update pass.'''			,     )
		parser.add_argument('''--seed'''			,     type=_a			,     default=42			,     help='''random seed for initialization''' )
		parser.add_argument(
		    '''--data_dir'''			,     default=_a			,     type=_a			,     required=_a			,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''			,     )






def   lowerCamelCase_					( _a			,     _a			,     _a=None			,     _a=True			,     _a=None			,     _a=[]			,     _a=None			,     _a=None			,     **_a			,     ):




		"""simple docstring"""






		pl.seed_everything(args.seed )

		# init model
		lowerCAmelCase__     :    List[Any]					  =      Path(model.hparams.output_dir )
		odir.mkdir(exist_ok=_a )

		# add custom checkpoints
		if checkpoint_callback is None:
				lowerCAmelCase__     :    List[str]					  =      pl.callbacks.ModelCheckpoint(
				    filepath=args.output_dir			,     prefix='''checkpoint'''			,     monitor='''val_loss'''			,     mode='''min'''			,     save_top_k=1 )
		if early_stopping_callback:
				extra_callbacks.append(_a )
		if logging_callback is None:
				lowerCAmelCase__     :    Any					  =      LoggingCallback()

		lowerCAmelCase__     :    Union[str, Any]					  =      {}

		# TODO: remove with PyTorch 1.6 since pl uses native amp
		if args.fpaa:
				lowerCAmelCase__     :    List[Any]					  =      16
				# train_params["amp_level"] = args.fp16_opt_level

		if args.gpus > 1:
				lowerCAmelCase__     :    Optional[Any]					  =      '''auto'''  # "ddp"
				lowerCAmelCase__     :    List[str]					  =      '''ddp'''

		lowerCAmelCase__     :    Optional[Any]					  =      args.accumulate_grad_batches
		lowerCAmelCase__     :    List[Any]					  =      None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
		lowerCAmelCase__     :    Tuple					  =      '''auto'''

		lowerCAmelCase__     :    Optional[Any]					  =      pl.Trainer.from_argparse_args(
		    _a			,     weights_summary=_a			,     callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]			,     logger=_a			,     **_a			,     )

		if args.do_train:
				trainer.fit(_a )

		return trainer








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       _a							(       nn.Module):




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




	def 							UpperCAmelCase__(      self		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	List[Any]=None	,      _SCREAMING_SNAKE_CASE		:	List[str]=None	,      _SCREAMING_SNAKE_CASE		:	Dict=None	,      _SCREAMING_SNAKE_CASE		:	Tuple=None	)-> Any:
			lowerCAmelCase__     :    List[Any]					  =      self.proj_embeddings(self.encoder(_SCREAMING_SNAKE_CASE	)	)
			lowerCAmelCase__     :    Dict					  =      token_embeddings.size(1	)

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

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

			if position_ids is None:
					lowerCAmelCase__     :    Union[str, Any]					  =      torch.arange(_SCREAMING_SNAKE_CASE	,      dtype=torch.long	,      device=input_modal.device	)
					lowerCAmelCase__     :    Optional[int]					  =      position_ids.unsqueeze(0	).expand(input_modal.size(0	)	,      _SCREAMING_SNAKE_CASE	)

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

			lowerCAmelCase__     :    Union[str, Any]					  =      self.position_embeddings(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Tuple					  =      self.token_type_embeddings(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Union[str, Any]					  =      token_embeddings + position_embeddings + token_type_embeddings
			lowerCAmelCase__     :    List[Any]					  =      self.LayerNorm(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Optional[int]					  =      self.dropout(_SCREAMING_SNAKE_CASE	)
			return embeddings
lowerCamelCase		=	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.
'''

lowerCamelCase		=	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.'''  ,		_lowercase  ,		)
class       _a							(       nn.Module  ,		_lowercase):




	def __init__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	int	)-> str:
			super().__init__()
			lowerCAmelCase__     :    Dict					  =      config
			lowerCAmelCase__     :    List[Any]					  =      transformer
			lowerCAmelCase__     :    List[Any]					  =      ModalEmbeddings(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      transformer.embeddings	)




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

			lowerCAmelCase__     :    List[str]					  =      input_ids.device if input_ids is not None else inputs_embeds.device

			lowerCAmelCase__     :    List[Any]					  =      self.modal_encoder(
			    _SCREAMING_SNAKE_CASE	,      start_token=_SCREAMING_SNAKE_CASE	,      end_token=_SCREAMING_SNAKE_CASE	,      position_ids=_SCREAMING_SNAKE_CASE	,      token_type_ids=_SCREAMING_SNAKE_CASE	,      )

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

			if token_type_ids is None:
					lowerCAmelCase__     :    Union[str, Any]					  =      torch.ones(_SCREAMING_SNAKE_CASE	,      dtype=torch.long	,      device=_SCREAMING_SNAKE_CASE	)

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

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

			lowerCAmelCase__     :    Any					  =      embedding_output.size()[:-1]

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

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

			lowerCAmelCase__     :    Union[str, Any]					  =      self.transformer.encoder(
			    _SCREAMING_SNAKE_CASE	,      attention_mask=_SCREAMING_SNAKE_CASE	,      head_mask=_SCREAMING_SNAKE_CASE	,      encoder_hidden_states=_SCREAMING_SNAKE_CASE	,      encoder_attention_mask=_SCREAMING_SNAKE_CASE	,      output_attentions=_SCREAMING_SNAKE_CASE	,      output_hidden_states=_SCREAMING_SNAKE_CASE	,      return_dict=_SCREAMING_SNAKE_CASE	,      )

			lowerCAmelCase__     :    Any					  =      encoder_outputs[0]
			lowerCAmelCase__     :    Optional[Any]					  =      self.transformer.pooler(_SCREAMING_SNAKE_CASE	)

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

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




	def 							UpperCAmelCase__(      self		:	Dict	)-> List[str]:
			return self.embeddings.word_embeddings




	def 							UpperCAmelCase__(      self		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	)-> Optional[int]:
			lowerCAmelCase__     :    Dict					  =      value





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




	def __init__(      self		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]	)-> Union[str, Any]:
			super().__init__()
			lowerCAmelCase__     :    Union[str, Any]					  =      config.num_labels

			lowerCAmelCase__     :    str					  =      MMBTModel(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    List[Any]					  =      nn.Dropout(config.hidden_dropout_prob	)
			lowerCAmelCase__     :    Any					  =      nn.Linear(config.hidden_size	,      config.num_labels	)




	def 							UpperCAmelCase__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]=None	,      _SCREAMING_SNAKE_CASE		:	Any=None	,      _SCREAMING_SNAKE_CASE		:	List[Any]=None	,      _SCREAMING_SNAKE_CASE		:	List[str]=None	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=None	,      _SCREAMING_SNAKE_CASE		:	Any=None	,      _SCREAMING_SNAKE_CASE		:	Any=None	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=None	,      _SCREAMING_SNAKE_CASE		:	Any=None	,      _SCREAMING_SNAKE_CASE		:	List[str]=None	,      _SCREAMING_SNAKE_CASE		:	int=None	,      _SCREAMING_SNAKE_CASE		:	List[Any]=None	,      )-> Union[str, Any]:
			lowerCAmelCase__     :    Dict					  =      return_dict if return_dict is not None else self.config.use_return_dict

			lowerCAmelCase__     :    str					  =      self.mmbt(
			    input_modal=_SCREAMING_SNAKE_CASE	,      input_ids=_SCREAMING_SNAKE_CASE	,      modal_start_tokens=_SCREAMING_SNAKE_CASE	,      modal_end_tokens=_SCREAMING_SNAKE_CASE	,      attention_mask=_SCREAMING_SNAKE_CASE	,      token_type_ids=_SCREAMING_SNAKE_CASE	,      modal_token_type_ids=_SCREAMING_SNAKE_CASE	,      position_ids=_SCREAMING_SNAKE_CASE	,      modal_position_ids=_SCREAMING_SNAKE_CASE	,      head_mask=_SCREAMING_SNAKE_CASE	,      inputs_embeds=_SCREAMING_SNAKE_CASE	,      return_dict=_SCREAMING_SNAKE_CASE	,      )

			lowerCAmelCase__     :    List[str]					  =      outputs[1]

			lowerCAmelCase__     :    Dict					  =      self.dropout(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Union[str, Any]					  =      self.classifier(_SCREAMING_SNAKE_CASE	)

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

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

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




