



"""simple docstring"""




import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

_UpperCamelCase:				str    							=  logging.get_logger(__name__)


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase = True				)     ->			int:






      '''simple docstring'''


      lowercase  :      Optional[int]					=   {}
      for name in model.attn_processors.keys():
            lowercase  :      Tuple					=   name.endswith('attn2.processor'				) and not (
                name.startswith('transformer_in'				) or 'temp_attentions' in name.split('.'				)
            )
            lowercase  :      str					=   model.config.cross_attention_dim if has_cross_attention else None
            if name.startswith('mid_block'				):
                  lowercase  :      Union[str, Any]					=   model.config.block_out_channels[-1]
            elif name.startswith('up_blocks'				):
                  lowercase  :      Union[str, Any]					=   int(name[len('up_blocks.'				)]				)
                  lowercase  :      Optional[int]					=   list(reversed(model.config.block_out_channels				)				)[block_id]
            elif name.startswith('down_blocks'				):
                  lowercase  :      str					=   int(name[len('down_blocks.'				)]				)
                  lowercase  :      Union[str, Any]					=   model.config.block_out_channels[block_id]
            elif name.startswith('transformer_in'				):
                  # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                  lowercase  :      Tuple					=   8 * model.config.attention_head_dim

            lowercase  :      Dict					=   LoRAAttnProcessor(hidden_size=__lowerCamelCase	,      cross_attention_dim=__lowerCamelCase				)
            lowercase  :      Dict					=   lora_attn_procs[name].to(model.device				)

            if mock_weights:
                  # add 1 to weights to mock trained weights
                  with torch.no_grad():
                        lora_attn_procs[name].to_q_lora.up.weight += 1
                        lora_attn_procs[name].to_k_lora.up.weight += 1
                        lora_attn_procs[name].to_v_lora.up.weight += 1
                        lora_attn_procs[name].to_out_lora.up.weight += 1

      return lora_attn_procs





@skip_mps
class   a__		(						snake_case_,  snake_case_,  unittest.TestCase     ):
   _lowerCamelCase												=				UNetaDConditionModel
   _lowerCamelCase												=				'sample'


   @property
   def        lowercase				(							self      :							Dict			)    ->       Dict:
         lowercase  :      Optional[int]					=   4
         lowercase  :      Optional[Any]					=   4
         lowercase  :      Any					=   4
         lowercase  :      Union[str, Any]					=   (32, 32)

         lowercase  :      Tuple					=   floats_tensor((batch_size, num_channels, num_frames) + sizes			).to(lowerCAmelCase			)
         lowercase  :      Union[str, Any]					=   torch.tensor([10]			).to(lowerCAmelCase			)
         lowercase  :      Union[str, Any]					=   floats_tensor((batch_size, 4, 32)			).to(lowerCAmelCase			)

         return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}


   @property
   def        lowercase				(							self      :							Optional[int]			)    ->       Optional[Any]:
         return (4, 4, 32, 32)


   @property
   def        lowercase				(							self      :							List[str]			)    ->       int:
         return (4, 4, 32, 32)


   def        lowercase				(							self      :							int			)    ->       Any:
         lowercase  :      List[Any]					=   {
             'block_out_channels': (32, 64),
             'down_block_types': (
                 'CrossAttnDownBlock3D',
                 'DownBlock3D',
             ),
             'up_block_types': ('UpBlock3D', 'CrossAttnUpBlock3D'),
             'cross_attention_dim': 32,
             'attention_head_dim': 8,
             'out_channels': 4,
             'in_channels': 4,
             'layers_per_block': 1,
             'sample_size': 32,
         }
         lowercase  :      str					=   self.dummy_input
         return init_dict, inputs_dict


   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available(),     reason='XFormers attention is only available with CUDA and `xformers` installed',     )
   def        lowercase				(							self      :							Any			)    ->       Tuple:
         lowercase	,    lowercase  :      Dict					=   self.prepare_init_args_and_inputs_for_common()
         lowercase  :      List[Any]					=   self.model_class(**lowerCAmelCase			)

         model.enable_xformers_memory_efficient_attention()

         assert (
             model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
             == "XFormersAttnProcessor"
         ), "xformers is not enabled"


   def        lowercase				(							self      :							Any			)    ->       Optional[int]:
         lowercase	,    lowercase  :      Optional[Any]					=   self.prepare_init_args_and_inputs_for_common()

         lowercase  :      Optional[int]					=   32

         lowercase  :      Optional[Any]					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()

         with torch.no_grad():
               lowercase  :      Dict					=   model(**lowerCAmelCase			)

               if isinstance(lowerCAmelCase,     lowerCAmelCase			):
                     lowercase  :      Dict					=   output.sample

         self.assertIsNotNone(lowerCAmelCase			)
         lowercase  :      Union[str, Any]					=   inputs_dict['sample'].shape
         self.assertEqual(output.shape,     lowerCAmelCase,     'Input and output shapes do not match'			)


   def        lowercase				(							self      :							List[str]			)    ->       Optional[Any]:
         lowercase	,    lowercase  :      str					=   self.prepare_init_args_and_inputs_for_common()
         lowercase  :      int					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()

         with torch.no_grad():
               # Warmup pass when using mps (see #372)
               if torch_device == "mps" and isinstance(lowerCAmelCase,     lowerCAmelCase			):
                     model(**self.dummy_input			)

               lowercase  :      Tuple					=   model(**lowerCAmelCase			)
               if isinstance(lowerCAmelCase,     lowerCAmelCase			):
                     lowercase  :      Tuple					=   first.sample

               lowercase  :      Any					=   model(**lowerCAmelCase			)
               if isinstance(lowerCAmelCase,     lowerCAmelCase			):
                     lowercase  :      Optional[Any]					=   second.sample

         lowercase  :      Tuple					=   first.cpu().numpy()
         lowercase  :      List[Any]					=   second.cpu().numpy()
         lowercase  :      Optional[Any]					=   out_a[~np.isnan(lowerCAmelCase			)]
         lowercase  :      Union[str, Any]					=   out_a[~np.isnan(lowerCAmelCase			)]
         lowercase  :      Optional[int]					=   np.amax(np.abs(out_a - out_a			)			)
         self.assertLessEqual(lowerCAmelCase,     1e-5			)


   def        lowercase				(							self      :							str			)    ->       Union[str, Any]:
         lowercase	,    lowercase  :      List[Any]					=   self.prepare_init_args_and_inputs_for_common()

         lowercase  :      List[Any]					=   8

         lowercase  :      Union[str, Any]					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()

         model.set_attention_slice('auto'			)
         with torch.no_grad():
               lowercase  :      Tuple					=   model(**lowerCAmelCase			)
         assert output is not None

         model.set_attention_slice('max'			)
         with torch.no_grad():
               lowercase  :      Optional[int]					=   model(**lowerCAmelCase			)
         assert output is not None

         model.set_attention_slice(2			)
         with torch.no_grad():
               lowercase  :      int					=   model(**lowerCAmelCase			)
         assert output is not None


   def        lowercase				(							self      :							str			)    ->       Optional[Any]:
         lowercase	,    lowercase  :      Optional[int]					=   self.prepare_init_args_and_inputs_for_common()

         lowercase  :      str					=   8

         lowercase  :      List[str]					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)

         with torch.no_grad():
               lowercase  :      Any					=   model(**lowerCAmelCase			).sample

         lowercase  :      Dict					=   create_lora_layers(lowerCAmelCase			)

         # make sure we can set a list of attention processors
         model.set_attn_processor(lowerCAmelCase			)
         model.to(lowerCAmelCase			)

         # test that attn processors can be set to itself
         model.set_attn_processor(model.attn_processors			)

         with torch.no_grad():
               lowercase  :      str					=   model(**lowerCAmelCase,     cross_attention_kwargs={'scale': 0.0}			).sample
               lowercase  :      Union[str, Any]					=   model(**lowerCAmelCase,     cross_attention_kwargs={'scale': 0.5}			).sample
               lowercase  :      str					=   model(**lowerCAmelCase,     cross_attention_kwargs={'scale': 0.5}			).sample

         assert (samplea - samplea).abs().max() < 3e-3
         assert (samplea - samplea).abs().max() < 3e-3

         # sample 2 and sample 3 should be different
         assert (samplea - samplea).abs().max() > 3e-3


   def        lowercase				(							self      :							str			)    ->       Dict:
         lowercase	,    lowercase  :      Optional[int]					=   self.prepare_init_args_and_inputs_for_common()

         lowercase  :      Optional[Any]					=   8

         torch.manual_seed(0			)
         lowercase  :      int					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)

         with torch.no_grad():
               lowercase  :      List[str]					=   model(**lowerCAmelCase			).sample

         lowercase  :      List[Any]					=   create_lora_layers(lowerCAmelCase			)
         model.set_attn_processor(lowerCAmelCase			)

         with torch.no_grad():
               lowercase  :      Tuple					=   model(**lowerCAmelCase,     cross_attention_kwargs={'scale': 0.5}			).sample

         with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(lowerCAmelCase			)
               self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase,     'pytorch_lora_weights.bin'			)			)			)
               torch.manual_seed(0			)
               lowercase  :      Optional[Any]					=   self.model_class(**lowerCAmelCase			)
               new_model.to(lowerCAmelCase			)
               new_model.load_attn_procs(lowerCAmelCase			)

         with torch.no_grad():
               lowercase  :      str					=   new_model(**lowerCAmelCase,     cross_attention_kwargs={'scale': 0.5}			).sample

         assert (sample - new_sample).abs().max() < 1e-3

         # LoRA and no LoRA should NOT be the same
         assert (sample - old_sample).abs().max() > 1e-4


   def        lowercase				(							self      :							str			)    ->       List[str]:
         lowercase	,    lowercase  :      int					=   self.prepare_init_args_and_inputs_for_common()

         lowercase  :      List[Any]					=   8

         torch.manual_seed(0			)
         lowercase  :      Optional[Any]					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)

         with torch.no_grad():
               lowercase  :      Any					=   model(**lowerCAmelCase			).sample

         lowercase  :      List[Any]					=   create_lora_layers(lowerCAmelCase			)
         model.set_attn_processor(lowerCAmelCase			)

         with torch.no_grad():
               lowercase  :      Any					=   model(**lowerCAmelCase,     cross_attention_kwargs={'scale': 0.5}			).sample

         with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(lowerCAmelCase,     safe_serialization=lowerCAmelCase			)
               self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase,     'pytorch_lora_weights.safetensors'			)			)			)
               torch.manual_seed(0			)
               lowercase  :      int					=   self.model_class(**lowerCAmelCase			)
               new_model.to(lowerCAmelCase			)
               new_model.load_attn_procs(lowerCAmelCase			)

         with torch.no_grad():
               lowercase  :      Optional[int]					=   new_model(**lowerCAmelCase,     cross_attention_kwargs={'scale': 0.5}			).sample

         assert (sample - new_sample).abs().max() < 3e-3

         # LoRA and no LoRA should NOT be the same
         assert (sample - old_sample).abs().max() > 1e-4


   def        lowercase				(							self      :							Dict			)    ->       List[str]:
         # enable deterministic behavior for gradient checkpointing
         lowercase	,    lowercase  :      Optional[Any]					=   self.prepare_init_args_and_inputs_for_common()

         lowercase  :      Optional[Any]					=   8

         torch.manual_seed(0			)
         lowercase  :      Tuple					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)

         lowercase  :      List[str]					=   create_lora_layers(lowerCAmelCase,     mock_weights=lowerCAmelCase			)
         model.set_attn_processor(lowerCAmelCase			)
         # Saving as torch, properly reloads with directly filename
         with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(lowerCAmelCase			)
               self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase,     'pytorch_lora_weights.bin'			)			)			)
               torch.manual_seed(0			)
               lowercase  :      Dict					=   self.model_class(**lowerCAmelCase			)
               new_model.to(lowerCAmelCase			)
               new_model.load_attn_procs(lowerCAmelCase,     weight_name='pytorch_lora_weights.bin'			)


   def        lowercase				(							self      :							List[Any]			)    ->       Any:
         lowercase	,    lowercase  :      Tuple					=   self.prepare_init_args_and_inputs_for_common()

         lowercase  :      List[str]					=   8

         torch.manual_seed(0			)
         lowercase  :      str					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)

         lowercase  :      Tuple					=   create_lora_layers(lowerCAmelCase,     mock_weights=lowerCAmelCase			)
         model.set_attn_processor(lowerCAmelCase			)
         # Saving as torch, properly reloads with directly filename
         with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(lowerCAmelCase			)
               self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase,     'pytorch_lora_weights.bin'			)			)			)
               torch.manual_seed(0			)
               lowercase  :      Dict					=   self.model_class(**lowerCAmelCase			)
               new_model.to(lowerCAmelCase			)
               with self.assertRaises(lowerCAmelCase			) as e:
                     new_model.load_attn_procs(lowerCAmelCase,     use_safetensors=lowerCAmelCase			)
               self.assertIn('Error no file named pytorch_lora_weights.safetensors',     str(e.exception			)			)


   def        lowercase				(							self      :							Tuple			)    ->       Union[str, Any]:
         lowercase	,    lowercase  :      Optional[int]					=   self.prepare_init_args_and_inputs_for_common()

         lowercase  :      int					=   8

         torch.manual_seed(0			)
         lowercase  :      Optional[Any]					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)

         with torch.no_grad():
               lowercase  :      Any					=   model(**lowerCAmelCase			).sample

         lowercase  :      Optional[Any]					=   create_lora_layers(lowerCAmelCase			)
         model.set_attn_processor(lowerCAmelCase			)

         with torch.no_grad():
               lowercase  :      int					=   model(**lowerCAmelCase,     cross_attention_kwargs={'scale': 0.0}			).sample

         model.set_attn_processor(AttnProcessor()			)

         with torch.no_grad():
               lowercase  :      str					=   model(**lowerCAmelCase			).sample

         assert (sample - new_sample).abs().max() < 1e-4
         assert (sample - old_sample).abs().max() < 3e-3


   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available(),     reason='XFormers attention is only available with CUDA and `xformers` installed',     )
   def        lowercase				(							self      :							Dict			)    ->       Optional[Any]:
         # enable deterministic behavior for gradient checkpointing
         lowercase	,    lowercase  :      Optional[int]					=   self.prepare_init_args_and_inputs_for_common()

         lowercase  :      Optional[Any]					=   4

         torch.manual_seed(0			)
         lowercase  :      int					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         lowercase  :      Optional[int]					=   create_lora_layers(lowerCAmelCase			)
         model.set_attn_processor(lowerCAmelCase			)

         # default
         with torch.no_grad():
               lowercase  :      Tuple					=   model(**lowerCAmelCase			).sample

               model.enable_xformers_memory_efficient_attention()
               lowercase  :      int					=   model(**lowerCAmelCase			).sample

               model.disable_xformers_memory_efficient_attention()
               lowercase  :      List[str]					=   model(**lowerCAmelCase			).sample

         assert (sample - on_sample).abs().max() < 1e-4
         assert (sample - off_sample).abs().max() < 1e-4






   def        lowercase				(							self      :							str			)    ->       Dict:
         lowercase	,    lowercase  :      Optional[int]					=   self.prepare_init_args_and_inputs_for_common()
         lowercase  :      int					=   32

         lowercase  :      int					=   self.model_class(**lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()

         with torch.no_grad():
               lowercase  :      Dict					=   model(**lowerCAmelCase			)[0]

         model.enable_forward_chunking()
         with torch.no_grad():
               lowercase  :      int					=   model(**lowerCAmelCase			)[0]

         self.assertEqual(output.shape,     output_a.shape,     'Shape doesn\'t match'			)
         assert np.abs(output.cpu() - output_a.cpu()			).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.





"""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,     )
