




"""simple docstring"""

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__lowerCamelCase								=						16
__lowerCamelCase								=						32



def 				a (						__snake_case     :				Accelerator,    __snake_case     :				int = 16  ):



				'''simple docstring'''





				UpperCAmelCase_					:List[str]      			=      AutoTokenizer.from_pretrained('''bert-base-cased'''  )
				UpperCAmelCase_					:Any      			=      load_dataset('''glue''',    '''mrpc'''  )

				def tokenize_function(__snake_case     :				Dict  ):
								# max_length=None => use the model max length (it's actually the default)
								UpperCAmelCase_					:Dict      			=      tokenizer(examples['''sentence1'''],    examples['''sentence2'''],    truncation=__snake_case,    max_length=__snake_case  )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								UpperCAmelCase_					:int      			=      datasets.map(
								    __snake_case,    batched=__snake_case,    remove_columns=['''idx''', '''sentence1''', '''sentence2'''],    )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				UpperCAmelCase_					:Union[str, Any]      			=      tokenized_datasets.rename_column('''label''',    '''labels'''  )

				def collate_fn(__snake_case     :				Union[str, Any]  ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								UpperCAmelCase_					:List[str]      			=      128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												UpperCAmelCase_					:Union[str, Any]      			=      16
								elif accelerator.mixed_precision != "no":
												UpperCAmelCase_					:Optional[int]      			=      8
								else:
												UpperCAmelCase_					:Optional[int]      			=      None

								return tokenizer.pad(
								    __snake_case,    padding='''longest''',    max_length=__snake_case,    pad_to_multiple_of=__snake_case,    return_tensors='''pt''',    )

				# Instantiate dataloaders.
				UpperCAmelCase_					:Any      			=      DataLoader(
				    tokenized_datasets['''train'''],    shuffle=__snake_case,    collate_fn=__snake_case,    batch_size=__snake_case  )
				UpperCAmelCase_					:List[str]      			=      DataLoader(
				    tokenized_datasets['''validation'''],    shuffle=__snake_case,    collate_fn=__snake_case,    batch_size=__snake_case  )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						__lowerCamelCase								=						mocked_dataloaders  # noqa: F811



def 				a (						__snake_case     :				Optional[int],    __snake_case     :				Dict  ):



				'''simple docstring'''





				if os.environ.get('''TESTING_MOCKED_DATALOADERS''',    __snake_case  ) == "1":
								UpperCAmelCase_					:List[Any]      			=      2
				# Initialize accelerator
				UpperCAmelCase_					:Dict      			=      Accelerator(cpu=args.cpu,    mixed_precision=args.mixed_precision  )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCAmelCase_					:Tuple      			=      config['''lr''']
				UpperCAmelCase_					:List[str]      			=      int(config['''num_epochs''']  )
				UpperCAmelCase_					:str      			=      int(config['''seed''']  )
				UpperCAmelCase_					:Union[str, Any]      			=      int(config['''batch_size''']  )

				# New Code #
				# Parse out whether we are saving every epoch or after a certain number of batches
				if hasattr(args.checkpointing_steps,    '''isdigit'''  ):
								if args.checkpointing_steps == "epoch":
												UpperCAmelCase_					:int      			=      args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												UpperCAmelCase_					:Union[str, Any]      			=      int(args.checkpointing_steps  )
								else:
												raise ValueError(
												    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'  )
				else:
								UpperCAmelCase_					:Optional[int]      			=      None

				set_seed(__snake_case  )

				UpperCAmelCase_			,UpperCAmelCase_					:int      			=      get_dataloaders(__snake_case,    __snake_case  )
				UpperCAmelCase_					:str      			=      evaluate.load('''glue''',    '''mrpc'''  )

				# If the batch size is too big we use gradient accumulation
				UpperCAmelCase_					:str      			=      1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								UpperCAmelCase_					:List[str]      			=      batch_size // MAX_GPU_BATCH_SIZE
								UpperCAmelCase_					:Tuple      			=      MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCAmelCase_					:Tuple      			=      AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''',    return_dict=__snake_case  )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				UpperCAmelCase_					:int      			=      model.to(accelerator.device  )

				# Instantiate optimizer
				UpperCAmelCase_					:int      			=      AdamW(params=model.parameters(),    lr=__snake_case  )

				# Instantiate scheduler
				UpperCAmelCase_					:Union[str, Any]      			=      get_linear_schedule_with_warmup(
				    optimizer=__snake_case,    num_warmup_steps=100,    num_training_steps=(len(__snake_case  ) * num_epochs) // gradient_accumulation_steps,    )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				UpperCAmelCase_			,UpperCAmelCase_			,UpperCAmelCase_			,UpperCAmelCase_			,UpperCAmelCase_					:str      			=      accelerator.prepare(
				    __snake_case,    __snake_case,    __snake_case,    __snake_case,    __snake_case  )

				# New Code #
				# We need to keep track of how many total steps we have iterated over
				UpperCAmelCase_					:List[Any]      			=      0
				# We also need to keep track of the stating epoch so files are named properly
				UpperCAmelCase_					:List[Any]      			=      0

				# We need to load the checkpoint back in before training here with `load_state`
				# The total number of epochs is adjusted based on where the state is being loaded from,
				# as we assume continuation of the same training script
				if args.resume_from_checkpoint:
								if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'  )
												accelerator.load_state(args.resume_from_checkpoint  )
												UpperCAmelCase_					:Union[str, Any]      			=      os.path.basename(args.resume_from_checkpoint  )
								else:
												# Get the most recent checkpoint
												UpperCAmelCase_					:Optional[Any]      			=      [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
												dirs.sort(key=os.path.getctime  )
												UpperCAmelCase_					:Tuple      			=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								UpperCAmelCase_					:List[Any]      			=      os.path.splitext(__snake_case  )[0]

								if "epoch" in training_difference:
												UpperCAmelCase_					:Any      			=      int(training_difference.replace('''epoch_''',    ''''''  )  ) + 1
												UpperCAmelCase_					:List[Any]      			=      None
								else:
												UpperCAmelCase_					:Union[str, Any]      			=      int(training_difference.replace('''step_''',    ''''''  )  )
												UpperCAmelCase_					:Optional[Any]      			=      resume_step // len(__snake_case  )
												resume_step -= starting_epoch * len(__snake_case  )

    # Now we train the model
				for epoch in range(__snake_case,    __snake_case  ):
								model.train()
								# New Code #
								if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
												# We need to skip steps until we reach the resumed step
												UpperCAmelCase_					:Optional[int]      			=      accelerator.skip_first_batches(__snake_case,    __snake_case  )
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												UpperCAmelCase_					:str      			=      train_dataloader
								for step, batch in enumerate(__snake_case  ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device  )
												UpperCAmelCase_					:List[Any]      			=      model(**__snake_case  )
												UpperCAmelCase_					:Tuple      			=      outputs.loss
												UpperCAmelCase_					:int      			=      loss / gradient_accumulation_steps
												accelerator.backward(__snake_case  )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()
												# New Code #
												overall_step += 1

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `step_{overall_step}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if isinstance(__snake_case,    __snake_case  ):
																UpperCAmelCase_					:Any      			=      f'step_{overall_step}'
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								UpperCAmelCase_					:List[str]      			=      os.path.join(args.output_dir,    __snake_case  )
																				accelerator.save_state(__snake_case  )

								model.eval()
								for step, batch in enumerate(__snake_case  ):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device  )
												with torch.no_grad():
																UpperCAmelCase_					:int      			=      model(**__snake_case  )
												UpperCAmelCase_					:Any      			=      outputs.logits.argmax(dim=-1  )
												UpperCAmelCase_			,UpperCAmelCase_					:str      			=      accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
												metric.add_batch(
												    predictions=__snake_case,    references=__snake_case,    )

								UpperCAmelCase_					:List[Any]      			=      metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'epoch {epoch}:',    __snake_case  )

								# New Code #
								# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
								# These are saved to folders named `epoch_{epoch}`
								# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
								# If mixed precision was used, will also save a "scalar.bin" file
								if checkpointing_steps == "epoch":
												UpperCAmelCase_					:Tuple      			=      f'epoch_{epoch}'
												if args.output_dir is not None:
																UpperCAmelCase_					:List[Any]      			=      os.path.join(args.output_dir,    __snake_case  )
												accelerator.save_state(__snake_case  )



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[Any]      			=      argparse.ArgumentParser(description='''Simple example of training script.'''  )
				parser.add_argument(
				    '''--mixed_precision''',    type=__snake_case,    default=__snake_case,    choices=['''no''', '''fp16''', '''bf16''', '''fp8'''],    help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.''',    )
				parser.add_argument('''--cpu''',    action='''store_true''',    help='''If passed, will train on the CPU.'''  )
				parser.add_argument(
				    '''--checkpointing_steps''',    type=__snake_case,    default=__snake_case,    help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.''',    )
				parser.add_argument(
				    '''--output_dir''',    type=__snake_case,    default='''.''',    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''',    )
				parser.add_argument(
				    '''--resume_from_checkpoint''',    type=__snake_case,    default=__snake_case,    help='''If the training should continue from a checkpoint folder.''',    )
				UpperCAmelCase_					:Optional[Any]      			=      parser.parse_args()
				UpperCAmelCase_					:List[str]      			=      {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__snake_case,    __snake_case  )


if __name__ == "__main__":
						main()








"""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					,							)


