



"""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


snake_case_								=			logging.get_logger(__name__)

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







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				RetriBertConfig
						__UpperCamelCase													=				None
						__UpperCamelCase													=				"""retribert"""






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:int   )							->     Union[str, Any]:
												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   )


snake_case_								=			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."""      ,  SCREAMING_SNAKE_CASE_      ,  )
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:Tuple  ,    lowercase_					:RetriBertConfig   )							->     None:
												super().__init__(lowercase_   )
												UpperCAmelCase			 =					config.projection_dim

												UpperCAmelCase			 =					BertModel(lowercase_   )
												UpperCAmelCase			 =					None if config.share_encoders else BertModel(lowercase_   )
												UpperCAmelCase			 =					nn.Dropout(config.hidden_dropout_prob   )
												UpperCAmelCase			 =					nn.Linear(config.hidden_size  ,    config.projection_dim  ,    bias=lowercase_   )
												UpperCAmelCase			 =					nn.Linear(config.hidden_size  ,    config.projection_dim  ,    bias=lowercase_   )

												UpperCAmelCase			 =					nn.CrossEntropyLoss(reduction='mean'   )

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






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:Any  ,    lowercase_					:Dict  ,    lowercase_					:List[Any]  ,    lowercase_					:Dict=-1  ,    )							->     Optional[Any]:
												# 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
																		UpperCAmelCase			 =					input_ids.device
																		UpperCAmelCase			 =					input_ids.size()
																		UpperCAmelCase			 =					torch.zeros(lowercase_  ,    dtype=torch.long  ,    device=lowercase_   )
																		UpperCAmelCase			 =					[None] * sent_encoder.config.num_hidden_layers
																		UpperCAmelCase			 =					sent_encoder.get_extended_attention_mask(
																		    lowercase_  ,    lowercase_   )

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

																		# run embedding layer on everything at once
																		UpperCAmelCase			 =					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
																		UpperCAmelCase			 =					[]
																		for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size   )   ):
																								UpperCAmelCase			 =					embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																								UpperCAmelCase			 =					extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																								UpperCAmelCase			 =					checkpoint.checkpoint(lowercase_  ,    lowercase_  ,    lowercase_   )
																								pooled_output_list.append(lowercase_   )
																		return torch.cat(lowercase_  ,    dim=0   )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:Any  ,    lowercase_					:Tuple=None  ,    lowercase_					:Union[str, Any]=-1  ,    )							->     int:
												UpperCAmelCase			 =					self.embed_sentences_checkpointed(
												    lowercase_  ,    lowercase_  ,    self.bert_query  ,    lowercase_  ,    )
												return self.project_query(lowercase_   )






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Any  ,    lowercase_					:int=None  ,    lowercase_					:Any=-1  ,    )							->     Union[str, Any]:
												UpperCAmelCase			 =					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					:Optional[int]  ,    lowercase_					:torch.LongTensor  ,    lowercase_					:Optional[torch.FloatTensor]  ,    lowercase_					:torch.LongTensor  ,    lowercase_					:Optional[torch.FloatTensor]  ,    lowercase_					:int = -1  ,    )							->     torch.FloatTensor:
												UpperCAmelCase			 =					input_ids_query.device
												UpperCAmelCase			 =					self.embed_questions(lowercase_  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					self.embed_answers(lowercase_  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					torch.mm(lowercase_  ,    a_reps.t()   )
												UpperCAmelCase			 =					self.ce_loss(lowercase_  ,    torch.arange(compare_scores.shape[1]   ).to(lowercase_   )   )
												UpperCAmelCase			 =					self.ce_loss(compare_scores.t()  ,    torch.arange(compare_scores.shape[0]   ).to(lowercase_   )   )
												UpperCAmelCase			 =					(loss_qa + loss_aq) / 2
												return loss










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE     =	{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """YosoForMaskedLM""",
							    """YosoForMultipleChoice""",
							    """YosoForQuestionAnswering""",
							    """YosoForSequenceClassification""",
							    """YosoForTokenClassification""",
							    """YosoLayer""",
							    """YosoModel""",
							    """YosoPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yoso import (
														    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YosoForMaskedLM,
														    YosoForMultipleChoice,
														    YosoForQuestionAnswering,
														    YosoForSequenceClassification,
														    YosoForTokenClassification,
														    YosoLayer,
														    YosoModel,
														    YosoPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)