


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_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: Dict      =					RetriBertConfig
 _UpperCamelCase				: str      =					None
 _UpperCamelCase				: Any      =					'''retribert'''
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: Optional[Any]				)    ->			Optional[Any]:
      if isinstance(_a       ,    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(_a       ,    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(_a       ,    nn.LayerNorm				):
           module.bias.data.zero_()
           module.weight.data.fill_(1.0				)
_snake_case   = r"\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 ([`RetriBertConfig`]): 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. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"




@add_start_docstrings(
    '''Bert Based model to embed queries or document for document retrieval.'''      ,						a_      ,						)
class    A_   ( a_     ):







 """simple docstring"""





 def __init__(			self					: Optional[int]       ,    _a					: RetriBertConfig				)    ->			None:
      super().__init__(_a				)
      UpperCAmelCase						:				List[str]		  =			config.projection_dim

      UpperCAmelCase						:				Any		  =			BertModel(_a				)
      UpperCAmelCase						:				int		  =			None if config.share_encoders else BertModel(_a				)
      UpperCAmelCase						:				Dict		  =			nn.Dropout(config.hidden_dropout_prob				)
      UpperCAmelCase						:				Any		  =			nn.Linear(config.hidden_size       ,    config.projection_dim       ,    bias=_a				)
      UpperCAmelCase						:				Tuple		  =			nn.Linear(config.hidden_size       ,    config.projection_dim       ,    bias=_a				)

      UpperCAmelCase						:				Optional[int]		  =			nn.CrossEntropyLoss(reduction="mean"				)

      # Initialize weights and apply final processing
      self.post_init()
 def   _UpperCamelCase    (			self					: Any       ,    _a					: Dict       ,    _a					: List[Any]       ,    _a					: Optional[Any]       ,    _a					: str=-1       ,    )    ->			str:
      # reproduces BERT forward pass with checkpointing
      if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
           return sent_encoder(_a       ,    attention_mask=_a				)[1]
      else:
           # prepare implicit variables
           UpperCAmelCase						:				Union[str, Any]		  =			input_ids.device
           UpperCAmelCase						:				Any		  =			input_ids.size()
           UpperCAmelCase						:				Optional[int]		  =			torch.zeros(_a       ,    dtype=torch.long       ,    device=_a				)
           UpperCAmelCase						:				Optional[Any]		  =			[None] * sent_encoder.config.num_hidden_layers
           UpperCAmelCase						:				torch.Tensor		  =			sent_encoder.get_extended_attention_mask(
               _a       ,    _a				)

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

           # run embedding layer on everything at once
           UpperCAmelCase						:				int		  =			sent_encoder.embeddings(
               input_ids=_a       ,    position_ids=_a       ,    token_type_ids=_a       ,    inputs_embeds=_a				)
           # run encoding and pooling on one mini-batch at a time
           UpperCAmelCase						:				Union[str, Any]		  =			[]
           for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size				)				):
                UpperCAmelCase						:				List[str]		  =			embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                UpperCAmelCase						:				Union[str, Any]		  =			extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                UpperCAmelCase						:				List[str]		  =			checkpoint.checkpoint(_a       ,    _a       ,    _a				)
                pooled_output_list.append(_a				)
           return torch.cat(_a       ,    dim=0				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: int       ,    _a					: Tuple=None       ,    _a					: Union[str, Any]=-1       ,    )    ->			List[str]:
      UpperCAmelCase						:				Tuple		  =			self.embed_sentences_checkpointed(
          _a       ,    _a       ,    self.bert_query       ,    _a       ,    )
      return self.project_query(_a				)
 def   _UpperCamelCase    (			self					: str       ,    _a					: Optional[int]       ,    _a					: Union[str, Any]=None       ,    _a					: Dict=-1       ,    )    ->			int:
      UpperCAmelCase						:				List[str]		  =			self.embed_sentences_checkpointed(
          _a       ,    _a       ,    self.bert_query if self.bert_doc is None else self.bert_doc       ,    _a       ,    )
      return self.project_doc(_a				)

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







from collections.abc import Callable

_snake_case   = Callable[[float], float]  # type alias for a real -> real function







def   A   (		A__		,		A__		,		A__		,		):
     UpperCAmelCase						:				Any		  =			float(A__ )
     while True:
          try:
               UpperCAmelCase						:				Optional[Any]		  =			prev_guess - function(A__ ) / derivative(A__ )
          except ZeroDivisionError:
               raise ZeroDivisionError("Could not find root" ) from None
          if abs(prev_guess - next_guess ) < 1_0**-5:
               return next_guess
          UpperCAmelCase						:				List[str]		  =			next_guess







def   A   (		A__ ):
     return (x**3) - (2 * x) - 5







def   A   (		A__ ):
     return 3 * (x**2) - 2


if __name__ == "__main__":
      print(newton(f, fa, 3))



