from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TFSharedEmbeddings,
      )

      if _tf_gpu_memory_limit is not None:
            UpperCamelCase													=		tf.config.list_physical_devices('''GPU''')
            for gpu in gpus:
                  # Restrict TensorFlow to only allocate x GB of memory on the GPUs
                  try:
                        tf.config.set_logical_device_configuration(
                            gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                        )
                        UpperCamelCase													=		tf.config.list_logical_devices('''GPU''')
                        print('''Logical GPUs''', logical_gpus)
                  except RuntimeError as e:
                        # Virtual devices must be set before GPUs have been initialized
                        print(e)



@require_tf
class 						snake_case_ :
   __A			:	Any        =       None
   __A			:	str        =       ()
   __A			:	List[Any]        =       ()
   __A			:	List[Any]        =       True
   __A			:	Optional[int]        =       True
   __A			:	Any        =       True
   __A			:	List[str]        =       False






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  Union[str, Any]			,		lowercase_       :  Union[str, Any]			,		lowercase_       :  Dict=False						)      ->		Union[str, Any]:
       lowercase__				:     str					      =       copy.deepcopy(a_						)

       if model_class in get_values(a_						):
           lowercase__				:     Any					      =       {
               k: tf.tile(tf.expand_dims(a_			,		1						)			,		(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)						)
               if isinstance(a_			,		tf.Tensor						) and v.ndim > 0
               else v
               for k, v in inputs_dict.items()
           }

       if return_labels:
           if model_class in get_values(a_						):
               lowercase__				:     List[str]					      =       tf.ones(self.model_tester.batch_size			,		dtype=tf.intaa						)
           elif model_class in get_values(a_						):
               lowercase__				:     Dict					      =       tf.zeros(self.model_tester.batch_size			,		dtype=tf.intaa						)
               lowercase__				:     Any					      =       tf.zeros(self.model_tester.batch_size			,		dtype=tf.intaa						)
           elif model_class in [
               *get_values(a_						),
               *get_values(a_						),
           ]:
               lowercase__				:     List[Any]					      =       tf.zeros(self.model_tester.batch_size			,		dtype=tf.intaa						)
           elif model_class in get_values(a_						):
               lowercase__				:     List[str]					      =       tf.zeros(self.model_tester.batch_size			,		dtype=tf.intaa						)
           elif model_class in [
               *get_values(a_						),
               *get_values(a_						),
               *get_values(a_						),
               *get_values(a_						),
               *get_values(a_						),
           ]:
               lowercase__				:     Any					      =       tf.zeros(
                   (self.model_tester.batch_size, self.model_tester.seq_length)			,		dtype=tf.intaa						)
       return inputs_dict






   @slow
   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		Union[str, Any]:
       lowercase__      ,     lowercase__				:     Optional[Any]					      =       self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
           lowercase__				:     List[str]					      =       self._prepare_for_class(a_			,		a_						)
           lowercase__				:     str					      =       model_class(a_						)

           @tf.function
           def run_in_graph_mode():
               return model(a_						)

           lowercase__				:     List[Any]					      =       run_in_graph_mode()
           self.assertIsNotNone(a_						)






   @slow
   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		str:
       lowercase__      ,     lowercase__				:     int					      =       self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
           lowercase__				:     Optional[Any]					      =       self._prepare_for_class(a_			,		a_						)
           lowercase__				:     Optional[Any]					      =       model_class(a_						)

           @tf.function(experimental_compile=a_						)
           def run_in_graph_mode():
               return model(a_						)

           lowercase__				:     str					      =       run_in_graph_mode()
           self.assertIsNotNone(a_						)






   @slow
   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		List[Any]:
       lowercase__      ,     lowercase__				:     Optional[Any]					      =       self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
           lowercase__				:     Tuple					      =       model_class(a_						)
           if getattr(a_			,		"hf_compute_loss"			,		a_						):
               # Test that model correctly compute the loss with kwargs
               lowercase__				:     Tuple					      =       self._prepare_for_class(inputs_dict.copy()			,		a_			,		return_labels=a_						)
               # Is there a better way to remove these decoder inputs?
               lowercase__				:     Dict					      =       {
                   key: val
                   for key, val in prepared_for_class.items()
                   if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
               }

               lowercase__				:     Tuple					      =       {
                   "labels",
                   "label",
                   "label_ids",
                   "start_positions",
                   "start_position",
                   "end_positions",
                   "end_position",
                   "next_sentence_label",
               }
               lowercase__				:     Any					      =       possible_label_cols.intersection(set(a_						)						)
               self.assertGreater(len(a_						)			,		0			,		msg="No matching label names found!"						)
               lowercase__				:     Any					      =       {key: val for key, val in prepared_for_class.items() if key in label_names}
               lowercase__				:     List[str]					      =       {key: val for key, val in prepared_for_class.items() if key not in label_names}
               self.assertGreater(len(a_						)			,		0						)

               # Make sure it works with XLA!
               model.compile(optimizer=tf.keras.optimizers.SGD(0.0						)			,		jit_compile=a_						)
               # Make sure the model fits without crashing regardless of where we pass the labels
               lowercase__				:     int					      =       model.fit(
                   a_			,		validation_data=a_			,		steps_per_epoch=1			,		validation_steps=1			,		shuffle=a_			,		verbose=0			,		)
               lowercase__				:     Union[str, Any]					      =       history.history["loss"][0]
               self.assertTrue(not isnan(a_						)						)
               lowercase__				:     int					      =       history.history["val_loss"][0]
               self.assertTrue(not isnan(a_						)						)

               # Now test it with separate labels, to make sure that path works in XLA too.
               lowercase__				:     Union[str, Any]					      =       model_class(a_						)
               model.compile(optimizer=tf.keras.optimizers.SGD(0.0						)			,		jit_compile=a_						)
               lowercase__				:     str					      =       model.fit(
                   a_			,		a_			,		validation_data=(inputs_minus_labels, labels)			,		steps_per_epoch=1			,		validation_steps=1			,		shuffle=a_			,		verbose=0			,		)

               lowercase__				:     Any					      =       history.history["loss"][0]
               self.assertTrue(not isnan(a_						)						)
               lowercase__				:     Dict					      =       history.history["val_loss"][0]
               self.assertTrue(not isnan(a_						)						)






   @slow
   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Optional[Any]:
       lowercase__      ,     lowercase__				:     int					      =       self.model_tester.prepare_config_and_inputs_for_common()
       lowercase__				:     Any					      =       True
       lowercase__				:     Union[str, Any]					      =       True

       if hasattr(a_			,		"use_cache"						):
           lowercase__				:     Any					      =       True

       lowercase__				:     Union[str, Any]					      =       getattr(self.model_tester			,		"encoder_seq_length"			,		self.model_tester.seq_length						)
       lowercase__				:     Dict					      =       getattr(self.model_tester			,		"key_length"			,		a_						)

       for model_class in self.all_model_classes[:2]:
           lowercase__				:     Tuple					      =       self._prepare_for_class(a_			,		a_						)
           lowercase__				:     Dict					      =       model_class(a_						)
           model.build()
           lowercase__				:     int					      =       len(model(a_						)						)

           for key in list(class_inputs_dict.keys()						):
               # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
               if key not in model.input_signature:
                   del class_inputs_dict[key]
               # Check it's a tensor, in case the inputs dict has some bools in it too
               elif isinstance(class_inputs_dict[key]			,		tf.Tensor						) and class_inputs_dict[key].dtype.is_integer:
                   lowercase__				:     Optional[int]					      =       tf.cast(class_inputs_dict[key]			,		tf.intaa						)

           if set(class_inputs_dict.keys()						) != set(model.input_signature.keys()						):
               continue  # Some models have inputs that the preparation functions don't create, we skip those

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(a_			,		saved_model=a_						)
               lowercase__				:     Any					      =       os.path.join(a_			,		"saved_model"			,		"1"						)
               lowercase__				:     str					      =       tf.keras.models.load_model(a_						)
               lowercase__				:     List[str]					      =       model(a_						)

               if self.is_encoder_decoder:
                   lowercase__				:     Optional[int]					      =       outputs["encoder_hidden_states"]
                   lowercase__				:     int					      =       outputs["encoder_attentions"]
               else:
                   lowercase__				:     Tuple					      =       outputs["hidden_states"]
                   lowercase__				:     Tuple					      =       outputs["attentions"]

               self.assertEqual(len(a_						)			,		a_						)

               lowercase__				:     int					      =       getattr(
                   self.model_tester			,		"expected_num_hidden_layers"			,		self.model_tester.num_hidden_layers + 1						)

               self.assertEqual(len(a_						)			,		a_						)
               self.assertListEqual(
                   list(output_hidden_states[0].shape[-2:]						)			,		[self.model_tester.seq_length, self.model_tester.hidden_size]			,		)

               self.assertEqual(len(a_						)			,		self.model_tester.num_hidden_layers						)
               self.assertListEqual(
                   list(output_attentions[0].shape[-3:]						)			,		[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]			,		)






   @slow
   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		str:
       tf.keras.mixed_precision.set_global_policy("mixed_float16"						)

       # try/finally block to ensure subsequent tests run in float32
       try:
           lowercase__      ,     lowercase__				:     List[Any]					      =       self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes[:2]:
               lowercase__				:     List[str]					      =       self._prepare_for_class(a_			,		a_						)
               lowercase__				:     str					      =       model_class(a_						)
               lowercase__				:     Optional[int]					      =       model(a_						)

               self.assertIsNotNone(a_						)
       finally:
           tf.keras.mixed_precision.set_global_policy("float32"						)






   @slow
   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		Optional[int]:
       lowercase__      ,     lowercase__				:     Union[str, Any]					      =       self.model_tester.prepare_config_and_inputs_for_common()
       # head_mask and decoder_head_mask has different shapes than other input args
       if "head_mask" in inputs_dict:
           del inputs_dict["head_mask"]
       if "decoder_head_mask" in inputs_dict:
           del inputs_dict["decoder_head_mask"]
       if "cross_attn_head_mask" in inputs_dict:
           del inputs_dict["cross_attn_head_mask"]
       lowercase__				:     List[Any]					      =       {
           module_member
           for model_class in self.all_model_classes
           for module in (import_module(model_class.__module__						),)
           for module_member_name in dir(a_						)
           if module_member_name.endswith("MainLayer"						)
           for module_member in (getattr(a_			,		a_						),)
           if isinstance(a_			,		a_						)
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(a_			,		"_keras_serializable"			,		a_						)
       }

       for main_layer_class in tf_main_layer_classes:
           # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
           if "T5" in main_layer_class.__name__:
               # Take the same values than in TFT5ModelTester for this shared layer
               lowercase__				:     List[Any]					      =       TFSharedEmbeddings(self.model_tester.vocab_size			,		self.model_tester.hidden_size			,		name="shared"						)
               lowercase__				:     List[str]					      =       False
               lowercase__				:     List[str]					      =       main_layer_class(a_			,		embed_tokens=a_						)
           else:
               lowercase__				:     int					      =       main_layer_class(a_						)

           lowercase__				:     int					      =       {
               name: tf.keras.Input(tensor.shape[1:]			,		dtype=tensor.dtype						) for name, tensor in inputs_dict.items()
           }

           if hasattr(self.model_tester			,		"num_labels"						):
               lowercase__				:     Dict					      =       self.model_tester.num_labels
           else:
               lowercase__				:     Tuple					      =       2

           lowercase__				:     List[str]					      =       tf.data.Dataset.from_tensor_slices(
               (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)						))						).batch(1						)

           lowercase__				:     Tuple					      =       main_layer(a_						)[0]
           lowercase__				:     Tuple					      =       tf.keras.layers.Dense(a_			,		activation="softmax"			,		name="outputs"						)(a_						)
           lowercase__				:     List[Any]					      =       tf.keras.models.Model(inputs=a_			,		outputs=[outputs]						)

           model.compile(loss="binary_crossentropy"			,		optimizer="adam"			,		metrics=["binary_accuracy"]						)
           model.fit(a_			,		epochs=1						)

           with tempfile.TemporaryDirectory() as tmpdirname:
               lowercase__				:     str					      =       os.path.join(a_			,		"keras_model.h5"						)
               model.save(a_						)
               if "T5" in main_layer_class.__name__:
                   lowercase__				:     Union[str, Any]					      =       tf.keras.models.load_model(
                       a_			,		custom_objects={
                           main_layer_class.__name__: main_layer_class,
                           "TFSharedEmbeddings": TFSharedEmbeddings,
                       }			,		)
               else:
                   lowercase__				:     Union[str, Any]					      =       tf.keras.models.load_model(
                       a_			,		custom_objects={main_layer_class.__name__: main_layer_class}						)
               assert isinstance(a_			,		tf.keras.Model						)
               model(a_						)






   @slow
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Optional[Any]:
       lowercase__      ,     lowercase__				:     Optional[int]					      =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes[:2]:
           lowercase__				:     Optional[int]					      =       model_class(a_						)

           lowercase__				:     List[str]					      =       copy.deepcopy(a_						)

           if not self.is_encoder_decoder:
               lowercase__				:     Optional[Any]					      =       inputs["input_ids"]
               del inputs["input_ids"]
           else:
               lowercase__				:     List[Any]					      =       inputs["input_ids"]
               lowercase__				:     Any					      =       inputs.get("decoder_input_ids"			,		a_						)
               del inputs["input_ids"]
               inputs.pop("decoder_input_ids"			,		a_						)

           if not self.is_encoder_decoder:
               lowercase__				:     Optional[int]					      =       model.get_input_embeddings()(a_						)
           else:
               lowercase__				:     str					      =       model.get_input_embeddings()(a_						)
               lowercase__				:     Optional[int]					      =       model.get_input_embeddings()(a_						)

           lowercase__				:     List[Any]					      =       self._prepare_for_class(a_			,		a_						)

           @tf.function
           def run_in_graph_mode():
               return model(a_						)

           lowercase__				:     List[Any]					      =       run_in_graph_mode()
           self.assertIsNotNone(a_						)






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  str			,		lowercase_       :  Optional[Any]						)      ->		Optional[Any]:
       lowercase__				:     Dict					      =       []
       if model.config.bos_token_id is not None:
           special_tokens.append(model.config.bos_token_id						)
       if model.config.pad_token_id is not None:
           special_tokens.append(model.config.pad_token_id						)
       if model.config.eos_token_id is not None:
           special_tokens.append(model.config.eos_token_id						)

       # create random bad tokens that are not special tokens
       lowercase__				:     List[Any]					      =       []
       while len(a_						) < num_bad_tokens:
           lowercase__				:     Union[str, Any]					      =       tf.squeeze(ids_tensor((1, 1)			,		self.model_tester.vocab_size						)			,		0						).numpy()[0]
           if token not in special_tokens:
               bad_tokens.append(a_						)
       return bad_tokens






   def    __UpperCamelCase  (   self       :  List[Any]			,		lowercase_       :  List[Any]						)      ->		List[str]:



       for token_id in output_ids[0].numpy().tolist():
           self.assertGreaterEqual(a_			,		0						)
           self.assertLess(a_			,		self.model_tester.vocab_size						)







   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  List[Any]			,		lowercase_       :  int						)      ->		Tuple:



       for bad_word_ids in bad_words_ids:
           # for all slices in batch
           for generated_ids_slice in generated_ids:
               # for all word idx
               for i in range(len(a_						)			,		len(a_						)						):
                   # if tokens match
                   if generated_ids_slice[i - len(a_						) : i] == bad_word_ids:
                       return True
       return False




import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		[
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]



class 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       RetriBertConfig
   __A			:	str        =       None
   __A			:	Any        =       "retribert"






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  List[Any]						)      ->		List[str]:
       if isinstance(lowercase_			,		nn.Linear						):
           module.weight.data.normal_(mean=0.0			,		std=self.config.initializer_range						)
           if module.bias is not None:
               module.bias.data.zero_()
       elif isinstance(lowercase_			,		nn.Embedding						):
           module.weight.data.normal_(mean=0.0			,		std=self.config.initializer_range						)
           if module.padding_idx is not None:
               module.weight.data[module.padding_idx].zero_()
       elif isinstance(lowercase_			,		nn.LayerNorm						):
           module.bias.data.zero_()
           module.weight.data.fill_(1.0						)



UpperCamelCase													=		R'''

    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 ([`RetriBertConfig`]): 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. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''



@add_start_docstrings(
    "Bert Based model to embed queries or document for document retrieval."						,__A						,)
class 						snake_case_ (	__A ):






   def __init__(   self       :  Dict			,		lowercase_       :  RetriBertConfig						)      ->		None:
       super().__init__(lowercase_						)
       lowercase__				:     str					      =       config.projection_dim

       lowercase__				:     Optional[int]					      =       BertModel(lowercase_						)
       lowercase__				:     Union[str, Any]					      =       None if config.share_encoders else BertModel(lowercase_						)
       lowercase__				:     Tuple					      =       nn.Dropout(config.hidden_dropout_prob						)
       lowercase__				:     Any					      =       nn.Linear(config.hidden_size			,		config.projection_dim			,		bias=lowercase_						)
       lowercase__				:     str					      =       nn.Linear(config.hidden_size			,		config.projection_dim			,		bias=lowercase_						)

       lowercase__				:     Optional[Any]					      =       nn.CrossEntropyLoss(reduction="mean"						)

       # Initialize weights and apply final processing
       self.post_init()






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  str			,		lowercase_       :  Optional[int]			,		lowercase_       :  Tuple			,		lowercase_       :  Optional[int]=-1			,		)      ->		List[str]:
       # reproduces BERT forward pass with checkpointing
       if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
           return sent_encoder(lowercase_			,		attention_mask=lowercase_						)[1]
       else:
           # prepare implicit variables
           lowercase__				:     Union[str, Any]					      =       input_ids.device
           lowercase__				:     Union[str, Any]					      =       input_ids.size()
           lowercase__				:     int					      =       torch.zeros(lowercase_			,		dtype=torch.long			,		device=lowercase_						)
           lowercase__				:     List[Any]					      =       [None] * sent_encoder.config.num_hidden_layers
           lowercase__				:     torch.Tensor					      =       sent_encoder.get_extended_attention_mask(
               lowercase_			,		lowercase_						)

           # define function for checkpointing
           def partial_encode(*lowercase_       :  int						):
               lowercase__				:     List[Any]					      =       sent_encoder.encoder(
                   inputs[0]			,		attention_mask=inputs[1]			,		head_mask=lowercase_			,		)
               lowercase__				:     Dict					      =       encoder_outputs[0]
               lowercase__				:     List[Any]					      =       sent_encoder.pooler(lowercase_						)
               return pooled_output

           # run embedding layer on everything at once
           lowercase__				:     Optional[int]					      =       sent_encoder.embeddings(
               input_ids=lowercase_			,		position_ids=lowercase_			,		token_type_ids=lowercase_			,		inputs_embeds=lowercase_						)
           # run encoding and pooling on one mini-batch at a time
           lowercase__				:     str					      =       []
           for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size						)						):
               lowercase__				:     int					      =       embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
               lowercase__				:     int					      =       extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
               lowercase__				:     int					      =       checkpoint.checkpoint(lowercase_			,		lowercase_			,		lowercase_						)
               pooled_output_list.append(lowercase_						)
           return torch.cat(lowercase_			,		dim=0						)






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  Any			,		lowercase_       :  Any=None			,		lowercase_       :  Union[str, Any]=-1			,		)      ->		List[str]:
       lowercase__				:     Optional[Any]					      =       self.embed_sentences_checkpointed(
           lowercase_			,		lowercase_			,		self.bert_query			,		lowercase_			,		)
       return self.project_query(lowercase_						)






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Dict=-1			,		)      ->		Optional[int]:
       lowercase__				:     str					      =       self.embed_sentences_checkpointed(
           lowercase_			,		lowercase_			,		self.bert_query if self.bert_doc is None else self.bert_doc			,		lowercase_			,		)
       return self.project_doc(lowercase_						)







   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  torch.LongTensor			,		lowercase_       :  Optional[torch.FloatTensor]			,		lowercase_       :  torch.LongTensor			,		lowercase_       :  Optional[torch.FloatTensor]			,		lowercase_       :  int = -1			,		)      ->		torch.FloatTensor:
       lowercase__				:     Tuple					      =       input_ids_query.device
       lowercase__				:     str					      =       self.embed_questions(lowercase_			,		lowercase_			,		lowercase_						)
       lowercase__				:     Any					      =       self.embed_answers(lowercase_			,		lowercase_			,		lowercase_						)
       lowercase__				:     List[str]					      =       torch.mm(lowercase_			,		a_reps.t()						)
       lowercase__				:     Optional[int]					      =       self.ce_loss(lowercase_			,		torch.arange(compare_scores.shape[1]						).to(lowercase_						)						)
       lowercase__				:     str					      =       self.ce_loss(compare_scores.t()			,		torch.arange(compare_scores.shape[0]						).to(lowercase_						)						)
       lowercase__				:     Tuple					      =       (loss_qa + loss_aq) / 2
       return loss



