



"""simple docstring"""




import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase = False				)     ->			Dict:






      '''simple docstring'''


      if device_specific:
            seed += AcceleratorState().process_index
      random.seed(_UpperCAmelCase				)
      np.random.seed(_UpperCAmelCase				)
      torch.manual_seed(_UpperCAmelCase				)
      if is_xpu_available():
            torch.xpu.manual_seed_all(_UpperCAmelCase				)
      elif is_npu_available():
            torch.npu.manual_seed_all(_UpperCAmelCase				)
      else:
            torch.cuda.manual_seed_all(_UpperCAmelCase				)
      # ^^ safe to call this function even if cuda is not available
      if is_tpu_available():
            xm.set_rng_state(_UpperCAmelCase				)


def   lowercase__     (			_UpperCAmelCase = None	,      _UpperCAmelCase = None				)     ->			Any:






      '''simple docstring'''


      if rng_type == RNGType.TORCH:
            lowercase  :      List[str]					=   torch.get_rng_state()
      elif rng_type == RNGType.CUDA:
            lowercase  :      List[str]					=   torch.cuda.get_rng_state()
      elif rng_type == RNGType.XLA:
            assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
            lowercase  :      List[Any]					=   torch.tensor(xm.get_rng_state()				)
      elif rng_type == RNGType.NPU:
            assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
            lowercase  :      List[Any]					=   torch.npu.get_rng_state()
      elif rng_type == RNGType.XPU:
            assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
            lowercase  :      str					=   torch.xpu.get_rng_state()
      elif rng_type == RNGType.GENERATOR:
            assert generator is not None, "Need a generator to synchronize its seed."
            lowercase  :      Any					=   generator.get_state()

      # Broadcast the rng state from device 0 to other devices
      lowercase  :      Optional[Any]					=   AcceleratorState()
      if state.distributed_type == DistributedType.TPU:
            lowercase  :      Union[str, Any]					=   rng_state.to(xm.xla_device()				)
            xm.collective_broadcast([rng_state]				)
            xm.mark_step()
            lowercase  :      Optional[int]					=   rng_state.cpu()
      elif (
          state.distributed_type in CUDA_DISTRIBUTED_TYPES
          or state.distributed_type == DistributedType.MULTI_NPU
          or state.distributed_type == DistributedType.MULTI_XPU
      ):
            lowercase  :      Optional[Any]					=   rng_state.to(state.device				)
            torch.distributed.broadcast(_UpperCAmelCase	,      0				)
            lowercase  :      List[Any]					=   rng_state.cpu()
      elif state.distributed_type == DistributedType.MULTI_CPU:
            torch.distributed.broadcast(_UpperCAmelCase	,      0				)

      # Set the broadcast rng state
      if rng_type == RNGType.TORCH:
            torch.set_rng_state(_UpperCAmelCase				)
      elif rng_type == RNGType.CUDA:
            torch.cuda.set_rng_state(_UpperCAmelCase				)
      elif rng_type == RNGType.NPU:
            torch.npu.set_rng_state(_UpperCAmelCase				)
      elif rng_type == RNGType.XPU:
            torch.xpu.set_rng_state(_UpperCAmelCase				)
      elif rng_type == RNGType.XLA:
            xm.set_rng_state(rng_state.item()				)
      elif rng_type == RNGType.GENERATOR:
            generator.set_state(_UpperCAmelCase				)





def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase = None				)     ->			Optional[int]:






      '''simple docstring'''


      for rng_type in rng_types:
            synchronize_rng_state(RNGType(_UpperCAmelCase				)	,      generator=_UpperCAmelCase				)





"""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,     )
