




'''simple docstring'''





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


a		:	Union[str, Any]       =      logging.get_logger(__name__)

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




class      UpperCamelCase       (	lowerCamelCase_       ):
  a     :str          =					RetriBertConfig
  a     :Dict          =					None
  a     :Optional[Any]          =					"""retribert"""



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      if isinstance(lowerCamelCase_		,     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(lowerCamelCase_		,     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(lowerCamelCase_		,     nn.LayerNorm ):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0 )






a		:	Optional[int]       =      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.'      ,		lowerCamelCase_      ,		)
class      UpperCamelCase       (	lowerCamelCase_       ):



  def __init__(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      super().__init__(lowerCamelCase_ )
      _a				:							Optional[int]							       =      config.projection_dim

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

      _a				:							Dict							       =      nn.CrossEntropyLoss(reduction="""mean""" )

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



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase=-1		,     ):







      '''simple docstring'''


      if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
          return sent_encoder(lowerCamelCase_		,     attention_mask=lowerCamelCase_ )[1]
      else:
          # prepare implicit variables
          _a				:							str							       =      input_ids.device
          _a				:							Tuple							       =      input_ids.size()
          _a				:							Optional[Any]							       =      torch.zeros(lowerCamelCase_		,     dtype=torch.long		,     device=lowerCamelCase_ )
          _a				:							List[str]							       =      [None] * sent_encoder.config.num_hidden_layers
          _a				:							List[str]							       =      sent_encoder.get_extended_attention_mask(
              lowerCamelCase_		,     lowerCamelCase_ )

          # define function for checkpointing
          def partial_encode(*__lowerCAmelCase ):
              _a				:							Any							       =      sent_encoder.encoder(
                  inputs[0]		,     attention_mask=inputs[1]		,     head_mask=lowerCamelCase_		,     )
              _a				:							Optional[int]							       =      encoder_outputs[0]
              _a				:							str							       =      sent_encoder.pooler(lowerCamelCase_ )
              return pooled_output

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



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=-1		,     ):







      '''simple docstring'''


      _a				:							Any							       =      self.embed_sentences_checkpointed(
          lowerCamelCase_		,     lowerCamelCase_		,     self.bert_query		,     lowerCamelCase_		,     )
      return self.project_query(lowerCamelCase_ )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=-1		,     ):







      '''simple docstring'''


      _a				:							str							       =      self.embed_sentences_checkpointed(
          lowerCamelCase_		,     lowerCamelCase_		,     self.bert_query if self.bert_doc is None else self.bert_doc		,     lowerCamelCase_		,     )
      return self.project_doc(lowerCamelCase_ )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = -1		,     ):







      '''simple docstring'''


      _a				:							str							       =      input_ids_query.device
      _a				:							Optional[int]							       =      self.embed_questions(lowerCamelCase_		,     lowerCamelCase_		,     lowerCamelCase_ )
      _a				:							Optional[Any]							       =      self.embed_answers(lowerCamelCase_		,     lowerCamelCase_		,     lowerCamelCase_ )
      _a				:							str							       =      torch.mm(lowerCamelCase_		,     a_reps.t() )
      _a				:							int							       =      self.ce_loss(lowerCamelCase_		,     torch.arange(compare_scores.shape[1] ).to(lowerCamelCase_ ) )
      _a				:							int							       =      self.ce_loss(compare_scores.t()		,     torch.arange(compare_scores.shape[0] ).to(lowerCamelCase_ ) )
      _a				:							str							       =      (loss_qa + loss_aq) / 2
      return loss






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE	:							Any           =  {'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							str           =  ['''PegasusTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  ['''PegasusTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PegasusForCausalLM''',
		    '''PegasusForConditionalGeneration''',
		    '''PegasusModel''',
		    '''PegasusPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''TFPegasusForConditionalGeneration''',
		    '''TFPegasusModel''',
		    '''TFPegasusPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''FlaxPegasusForConditionalGeneration''',
		    '''FlaxPegasusModel''',
		    '''FlaxPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Dict           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

