



"""simple docstring"""




import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


_UpperCamelCase:				Dict    							=  get_logger(__name__)


def   lowercase__     (			_UpperCAmelCase				)     ->			Optional[Any]:






      '''simple docstring'''


      return TestCommand(
          args.dataset	,      args.name	,      args.cache_dir	,      args.data_dir	,      args.all_configs	,      args.save_info or args.save_infos	,      args.ignore_verifications	,      args.force_redownload	,      args.clear_cache	,      )





class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				False  # to tell pytest it's not a test class


   @staticmethod
   def        lowercase				(							lowerCAmelCase      :							ArgumentParser			)    ->       int:
         lowercase  :      Optional[Any]					=   parser.add_parser('test',     help='Test dataset implementation.'			)
         test_parser.add_argument('--name',     type=lowerCAmelCase,     default=lowerCAmelCase,     help='Dataset processing name'			)
         test_parser.add_argument(
             '--cache_dir',     type=lowerCAmelCase,     default=lowerCAmelCase,     help='Cache directory where the datasets are stored.',     )
         test_parser.add_argument(
             '--data_dir',     type=lowerCAmelCase,     default=lowerCAmelCase,     help='Can be used to specify a manual directory to get the files from.',     )
         test_parser.add_argument('--all_configs',     action='store_true',     help='Test all dataset configurations'			)
         test_parser.add_argument(
             '--save_info',     action='store_true',     help='Save the dataset infos in the dataset card (README.md)'			)
         test_parser.add_argument(
             '--ignore_verifications',     action='store_true',     help='Run the test without checksums and splits checks.',     )
         test_parser.add_argument('--force_redownload',     action='store_true',     help='Force dataset redownload'			)
         test_parser.add_argument(
             '--clear_cache',     action='store_true',     help='Remove downloaded files and cached datasets after each config test',     )
         # aliases
         test_parser.add_argument('--save_infos',     action='store_true',     help='alias to save_info'			)
         test_parser.add_argument('dataset',     type=lowerCAmelCase,     help='Name of the dataset to download'			)
         test_parser.set_defaults(func=_test_command_factory			)


   def __init__(							self      :							Any,     lowerCAmelCase      :							str,     lowerCAmelCase      :							str,     lowerCAmelCase      :							str,     lowerCAmelCase      :							str,     lowerCAmelCase      :							bool,     lowerCAmelCase      :							bool,     lowerCAmelCase      :							bool,     lowerCAmelCase      :							bool,     lowerCAmelCase      :							bool,     )    ->       int:
         lowercase  :      Tuple					=   dataset
         lowercase  :      str					=   name
         lowercase  :      List[Any]					=   cache_dir
         lowercase  :      Optional[int]					=   data_dir
         lowercase  :      List[Any]					=   all_configs
         lowercase  :      List[str]					=   save_infos
         lowercase  :      str					=   ignore_verifications
         lowercase  :      List[str]					=   force_redownload
         lowercase  :      str					=   clear_cache
         if clear_cache and not cache_dir:
               print(
                   'When --clear_cache is used, specifying a cache directory is mandatory.\n'
                   'The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'
                   'Please provide a --cache_dir that will be used to test the dataset script.'			)
               exit(1			)
         if save_infos:
               lowercase  :      str					=   True






   def        lowercase				(							self      :							List[str]			)    ->       str:
         fl_logger().setLevel(lowerCAmelCase			)
         if self._name is not None and self._all_configs:
               print('Both parameters `config` and `all_configs` can\'t be used at once.'			)
               exit(1			)
         lowercase	,    lowercase  :      List[Any]					=   self._dataset, self._name
         lowercase  :      Any					=   dataset_module_factory(lowerCAmelCase			)
         lowercase  :      Any					=   import_main_class(module.module_path			)
         lowercase  :      List[Any]					=   len(builder_cls.BUILDER_CONFIGS			) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

         def get_builders() -> Generator[DatasetBuilder, None, None]:
               if self._all_configs and builder_cls.BUILDER_CONFIGS:
                     for i, config in enumerate(builder_cls.BUILDER_CONFIGS			):
                           if "config_name" in module.builder_kwargs:
                                 yield builder_cls(
                                     cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )
                           else:
                                 yield builder_cls(
                                     config_name=config.name,     cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )
               else:
                     if "config_name" in module.builder_kwargs:
                           yield builder_cls(cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs			)
                     else:
                           yield builder_cls(
                               config_name=lowerCAmelCase,     cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )

         for j, builder in enumerate(get_builders()			):
               print(f'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'''			)
               lowercase  :      Tuple					=   os.path.exists(
                   os.path.join(builder.get_imported_module_dir(),     datasets.config.DATASETDICT_INFOS_FILENAME			)			)  # record checksums only if we need to update a (deprecated) dataset_infos.json
               builder.download_and_prepare(
                   download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                   if not self._force_redownload
                   else DownloadMode.FORCE_REDOWNLOAD,     verification_mode=VerificationMode.NO_CHECKS
                   if self._ignore_verifications
                   else VerificationMode.ALL_CHECKS,     try_from_hf_gcs=lowerCAmelCase,     )
               builder.as_dataset()
               if self._save_infos:
                     builder._save_infos()

               # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
               # The dataset_infos are saved in the YAML part of the README.md

               # 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:
                     lowercase  :      Union[str, Any]					=   os.path.join(builder_cls.get_imported_module_dir(),     'README.md'			)
                     lowercase  :      Tuple					=   Path(lowerCAmelCase			).name + '.py'
                     lowercase  :      Dict					=   os.path.join(lowerCAmelCase,     lowerCAmelCase			)
                     if os.path.isfile(lowerCAmelCase			):
                           lowercase  :      Optional[Any]					=   os.path.dirname(lowerCAmelCase			)
                     elif os.path.isfile(lowerCAmelCase			):
                           lowercase  :      int					=   path
                     elif os.path.isdir(lowerCAmelCase			):  # for local directories containing only data files
                           lowercase  :      Dict					=   path
                     else:  # in case of a remote dataset
                           lowercase  :      int					=   None
                           print(f'''Dataset card saved at {dataset_readme_path}'''			)

                     # Move dataset_info back to the user
                     if dataset_dir is not None:
                           lowercase  :      Tuple					=   os.path.join(lowerCAmelCase,     'README.md'			)
                           copyfile(lowerCAmelCase,     lowerCAmelCase			)
                           print(f'''Dataset card saved at {user_dataset_readme_path}'''			)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
               if self._clear_cache:
                     if os.path.isdir(builder._cache_dir			):
                           logger.warning(f'''Clearing cache at {builder._cache_dir}'''			)
                           rmtree(builder._cache_dir			)
                     lowercase  :      Any					=   os.path.join(self._cache_dir,     datasets.config.DOWNLOADED_DATASETS_DIR			)
                     if os.path.isdir(lowerCAmelCase			):
                           logger.warning(f'''Clearing cache at {download_dir}'''			)
                           rmtree(lowerCAmelCase			)

         print('Test successful.'			)





"""simple docstring"""




import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ....modeling_utils import ModuleUtilsMixin
from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings


_UpperCamelCase:				List[Any]    							=  logging.get_logger(__name__)

_UpperCamelCase:				str    							=  'MMBTConfig'


class   a__		(						nn.Module     ):


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






   def        lowercase				(							self      :							str,     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							Any=None,     lowerCAmelCase      :							Union[str, Any]=None,     lowerCAmelCase      :							Tuple=None,     lowerCAmelCase      :							List[str]=None			)    ->       Tuple:
         lowercase  :      Tuple					=   self.proj_embeddings(self.encoder(lowerCAmelCase			)			)
         lowercase  :      Optional[Any]					=   token_embeddings.size(1			)

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

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

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

         if token_type_ids is None:
               lowercase  :      Dict					=   torch.zeros(
                   (input_modal.size(0			), seq_length),     dtype=torch.long,     device=input_modal.device			)

         lowercase  :      List[Any]					=   self.position_embeddings(lowerCAmelCase			)
         lowercase  :      Optional[int]					=   self.token_type_embeddings(lowerCAmelCase			)
         lowercase  :      List[str]					=   token_embeddings + position_embeddings + token_type_embeddings
         lowercase  :      List[Any]					=   self.LayerNorm(lowerCAmelCase			)
         lowercase  :      List[Any]					=   self.dropout(lowerCAmelCase			)
         return embeddings




_UpperCamelCase:				List[str]    							=  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'

_UpperCamelCase:				List[Any]    							=  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.',  SCREAMING_SNAKE_CASE__,  )
class   a__		(						nn.Module,  SCREAMING_SNAKE_CASE__     ):


   def __init__(							self      :							Dict,     lowerCAmelCase      :							Dict,     lowerCAmelCase      :							Dict,     lowerCAmelCase      :							Optional[int]			)    ->       Tuple:
         super().__init__()
         lowercase  :      Union[str, Any]					=   config
         lowercase  :      Dict					=   transformer
         lowercase  :      int					=   ModalEmbeddings(lowerCAmelCase,     lowerCAmelCase,     transformer.embeddings			)


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

         lowercase  :      Union[str, Any]					=   input_ids.device if input_ids is not None else inputs_embeds.device

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

         lowercase  :      Dict					=   modal_embeddings.size()[:-1]

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

         lowercase  :      str					=   self.transformer.embeddings(
             input_ids=lowerCAmelCase,     position_ids=lowerCAmelCase,     token_type_ids=lowerCAmelCase,     inputs_embeds=lowerCAmelCase			)

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

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

         if attention_mask is None:
               lowercase  :      Optional[Any]					=   torch.ones(lowerCAmelCase,     device=lowerCAmelCase			)
         else:
               lowercase  :      Union[str, Any]					=   torch.cat(
                   [torch.ones(lowerCAmelCase,     device=lowerCAmelCase,     dtype=torch.long			), attention_mask],     dim=1			)
         if encoder_attention_mask is None:
               lowercase  :      List[str]					=   torch.ones(lowerCAmelCase,     device=lowerCAmelCase			)
         else:
               lowercase  :      int					=   torch.cat(
                   [torch.ones(lowerCAmelCase,     device=lowerCAmelCase			), encoder_attention_mask],     dim=1			)

         lowercase  :      Optional[int]					=   self.get_extended_attention_mask(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      str					=   self.invert_attention_mask(lowerCAmelCase			)
         lowercase  :      Any					=   self.get_head_mask(lowerCAmelCase,     self.config.num_hidden_layers			)

         lowercase  :      int					=   self.transformer.encoder(
             lowerCAmelCase,     attention_mask=lowerCAmelCase,     head_mask=lowerCAmelCase,     encoder_hidden_states=lowerCAmelCase,     encoder_attention_mask=lowerCAmelCase,     output_attentions=lowerCAmelCase,     output_hidden_states=lowerCAmelCase,     return_dict=lowerCAmelCase,     )

         lowercase  :      List[Any]					=   encoder_outputs[0]
         lowercase  :      Union[str, Any]					=   self.transformer.pooler(lowerCAmelCase			)

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

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


   def        lowercase				(							self      :							str			)    ->       Optional[Any]:
         return self.embeddings.word_embeddings






   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							Union[str, Any]			)    ->       Any:
         lowercase  :      Optional[Any]					=   value




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


   def __init__(							self      :							Tuple,     lowerCAmelCase      :							Union[str, Any],     lowerCAmelCase      :							Optional[Any],     lowerCAmelCase      :							Optional[Any]			)    ->       List[str]:
         super().__init__()
         lowercase  :      Dict					=   config.num_labels

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






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

         lowercase  :      Union[str, Any]					=   self.mmbt(
             input_modal=lowerCAmelCase,     input_ids=lowerCAmelCase,     modal_start_tokens=lowerCAmelCase,     modal_end_tokens=lowerCAmelCase,     attention_mask=lowerCAmelCase,     token_type_ids=lowerCAmelCase,     modal_token_type_ids=lowerCAmelCase,     position_ids=lowerCAmelCase,     modal_position_ids=lowerCAmelCase,     head_mask=lowerCAmelCase,     inputs_embeds=lowerCAmelCase,     return_dict=lowerCAmelCase,     )

         lowercase  :      str					=   outputs[1]

         lowercase  :      Dict					=   self.dropout(lowerCAmelCase			)
         lowercase  :      Tuple					=   self.classifier(lowerCAmelCase			)

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

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

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