






import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode






def   lowerCamelCase_					( _a			,     **_a ):




		"""simple docstring"""






		return RunBeamCommand(
		    args.dataset			,     args.name			,     args.cache_dir			,     args.beam_pipeline_options			,     args.data_dir			,     args.all_configs			,     args.save_info or args.save_infos			,     args.ignore_verifications			,     args.force_redownload			,     **_a			,     )





class       _a							(       _lowercase):




	@staticmethod
	def 							UpperCAmelCase__(      _SCREAMING_SNAKE_CASE		:	ArgumentParser	)-> str:
			lowerCAmelCase__     :    Optional[Any]					  =      parser.add_parser('''run_beam'''	,      help='''Run a Beam dataset processing pipeline'''	)
			run_beam_parser.add_argument('''dataset'''	,      type=_SCREAMING_SNAKE_CASE	,      help='''Name of the dataset to download'''	)
			run_beam_parser.add_argument('''--name'''	,      type=_SCREAMING_SNAKE_CASE	,      default=_SCREAMING_SNAKE_CASE	,      help='''Dataset config name'''	)
			run_beam_parser.add_argument(
			    '''--cache_dir'''	,      type=_SCREAMING_SNAKE_CASE	,      default=_SCREAMING_SNAKE_CASE	,      help='''Cache directory where the datasets are stored'''	,      )
			run_beam_parser.add_argument(
			    '''--beam_pipeline_options'''	,      type=_SCREAMING_SNAKE_CASE	,      default=''''''	,      help='''Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'''	,      )
			run_beam_parser.add_argument(
			    '''--data_dir'''	,      type=_SCREAMING_SNAKE_CASE	,      default=_SCREAMING_SNAKE_CASE	,      help='''Can be used to specify a manual directory to get the files from'''	,      )
			run_beam_parser.add_argument('''--all_configs'''	,      action='''store_true'''	,      help='''Test all dataset configurations'''	)
			run_beam_parser.add_argument('''--save_info'''	,      action='''store_true'''	,      help='''Save the dataset infos file'''	)
			run_beam_parser.add_argument(
			    '''--ignore_verifications'''	,      action='''store_true'''	,      help='''Run the test without checksums and splits checks'''	)
			run_beam_parser.add_argument('''--force_redownload'''	,      action='''store_true'''	,      help='''Force dataset redownload'''	)
			# aliases
			run_beam_parser.add_argument('''--save_infos'''	,      action='''store_true'''	,      help='''alias for save_info'''	)
			run_beam_parser.set_defaults(func=_SCREAMING_SNAKE_CASE	)




	def __init__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	bool	,      _SCREAMING_SNAKE_CASE		:	bool	,      _SCREAMING_SNAKE_CASE		:	bool	,      _SCREAMING_SNAKE_CASE		:	bool	,      **_SCREAMING_SNAKE_CASE		:	Optional[int]	,      )-> Union[str, Any]:
			lowerCAmelCase__     :    str					  =      dataset
			lowerCAmelCase__     :    List[str]					  =      name
			lowerCAmelCase__     :    Union[str, Any]					  =      cache_dir
			lowerCAmelCase__     :    int					  =      beam_pipeline_options
			lowerCAmelCase__     :    str					  =      data_dir
			lowerCAmelCase__     :    Any					  =      all_configs
			lowerCAmelCase__     :    str					  =      save_infos
			lowerCAmelCase__     :    Union[str, Any]					  =      ignore_verifications
			lowerCAmelCase__     :    Union[str, Any]					  =      force_redownload
			lowerCAmelCase__     :    List[Any]					  =      config_kwargs




	def 							UpperCAmelCase__(      self		:	Optional[int]	)-> List[str]:
			import apache_beam as beam

			if self._name is not None and self._all_configs:
					print('''Both parameters `name` and `all_configs` can\'t be used at once.'''	)
					exit(1	)
			lowerCAmelCase__     :    Dict					  =      self._dataset, self._name
			lowerCAmelCase__     :    Optional[int]					  =      dataset_module_factory(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Any					  =      import_main_class(dataset_module.module_path	)
			lowerCAmelCase__     :    List[DatasetBuilder]					  =      []
			if self._beam_pipeline_options:
					lowerCAmelCase__     :    List[Any]					  =      beam.options.pipeline_options.PipelineOptions(
					    flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(''','''	) if opt]	)
			else:
					lowerCAmelCase__     :    Union[str, Any]					  =      None
			if self._all_configs and len(builder_cls.BUILDER_CONFIGS	) > 0:
					for builder_config in builder_cls.BUILDER_CONFIGS:
							builders.append(
							    builder_cls(
							        config_name=builder_config.name	,      data_dir=self._data_dir	,      hash=dataset_module.hash	,      beam_options=_SCREAMING_SNAKE_CASE	,      cache_dir=self._cache_dir	,      base_path=dataset_module.builder_kwargs.get('''base_path'''	)	,      )	)
			else:
					builders.append(
					    builder_cls(
					        config_name=_SCREAMING_SNAKE_CASE	,      data_dir=self._data_dir	,      beam_options=_SCREAMING_SNAKE_CASE	,      cache_dir=self._cache_dir	,      base_path=dataset_module.builder_kwargs.get('''base_path'''	)	,      **self._config_kwargs	,      )	)

			for builder in builders:
					builder.download_and_prepare(
					    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
					    if not self._force_redownload
					    else DownloadMode.FORCE_REDOWNLOAD	,      download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH	)	,      verification_mode=VerificationMode.NO_CHECKS
					    if self._ignore_verifications
					    else VerificationMode.ALL_CHECKS	,      try_from_hf_gcs=_SCREAMING_SNAKE_CASE	,      )
					if self._save_infos:
							builder._save_infos()

			print('''Apache beam run successful.'''	)

			# If save_infos=True, the dataset infos file is created next to the loaded module file.
			# Let's move it to the original directory of the dataset script, to allow the user to
			# upload them on S3 at the same time afterwards.
			if self._save_infos:
					lowerCAmelCase__     :    str					  =      os.path.join(builder_cls.get_imported_module_dir()	,      config.DATASETDICT_INFOS_FILENAME	)

					lowerCAmelCase__     :    Dict					  =      Path(_SCREAMING_SNAKE_CASE	).name + '''.py'''

					lowerCAmelCase__     :    Optional[Any]					  =      os.path.join(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
					if os.path.isfile(_SCREAMING_SNAKE_CASE	):
							lowerCAmelCase__     :    List[Any]					  =      os.path.dirname(_SCREAMING_SNAKE_CASE	)
					elif os.path.isfile(_SCREAMING_SNAKE_CASE	):
							lowerCAmelCase__     :    List[str]					  =      path
					else:  # in case of a remote dataset
							print(F'Dataset Infos file saved at {dataset_infos_path}'	)
							exit(1	)

					# Move datasetinfo back to the user
					lowerCAmelCase__     :    int					  =      os.path.join(_SCREAMING_SNAKE_CASE	,      config.DATASETDICT_INFOS_FILENAME	)
					copyfile(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
					print(F'Dataset Infos file saved at {user_dataset_infos_path}'	)








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	,      )




