


"""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_											=			logging.get_logger(__name__)

a_											=			[
    "yjernite/retribert-base-uncased",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	RetriBertConfig
		__UpperCamelCase							=	None
		__UpperCamelCase							=	'retribert'







		def        a_       (    self					:				Union[str, Any]			,      a__					:				int						)				->  int:





						'''simple docstring'''







						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						)





a_											=			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.'   , _UpperCamelCase   , )
class snake_case					(     _UpperCamelCase):







		def __init__(    self					:				str			,      a__					:				RetriBertConfig						)				->  None:





						'''simple docstring'''







						super().__init__(a__						)
						_A         =	config.projection_dim

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

						_A         =	nn.CrossEntropyLoss(reduction="mean"						)

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







		def        a_       (    self					:				Union[str, Any]			,      a__					:				List[str]			,      a__					:				Union[str, Any]			,      a__					:				int			,      a__					:				Dict=-1			,      )				->  Union[str, Any]:





						'''simple docstring'''







						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
										_A         =	input_ids.device
										_A         =	input_ids.size()
										_A         =	torch.zeros(a__			,      dtype=torch.long			,      device=a__						)
										_A         =	[None] * sent_encoder.config.num_hidden_layers
										_A         =	sent_encoder.get_extended_attention_mask(
										    a__			,      a__						)

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

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







		def        a_       (    self					:				Optional[int]			,      a__					:				Dict			,      a__					:				List[str]=None			,      a__					:				int=-1			,      )				->  str:





						'''simple docstring'''







						_A         =	self.embed_sentences_checkpointed(
						    a__			,      a__			,      self.bert_query			,      a__			,      )
						return self.project_query(a__						)







		def        a_       (    self					:				List[str]			,      a__					:				List[Any]			,      a__					:				Tuple=None			,      a__					:				List[Any]=-1			,      )				->  Tuple:





						'''simple docstring'''







						_A         =	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        a_       (    self					:				int			,      a__					:				torch.LongTensor			,      a__					:				Optional[torch.FloatTensor]			,      a__					:				torch.LongTensor			,      a__					:				Optional[torch.FloatTensor]			,      a__					:				int = -1			,      )				->  torch.FloatTensor:





						'''simple docstring'''







						_A         =	input_ids_query.device
						_A         =	self.embed_questions(a__			,      a__			,      a__						)
						_A         =	self.embed_answers(a__			,      a__			,      a__						)
						_A         =	torch.mm(a__			,      a_reps.t()						)
						_A         =	self.ce_loss(a__			,      torch.arange(compare_scores.shape[1]						).to(a__						)						)
						_A         =	self.ce_loss(compare_scores.t()			,      torch.arange(compare_scores.shape[0]						).to(a__						)						)
						_A         =	(loss_qa + loss_aq) / 2
						return loss
from itertools import combinations, product


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__      )	->bool:




  '''simple docstring'''

  if xa == ya == 0 or xa == ya == 0:
    return False
  _UpperCamelCase				=  xa * xa + ya * ya
  _UpperCamelCase				=  xa * xa + ya * ya
  _UpperCamelCase				=  (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
  return (
      a_square + b_square == c_square
      or a_square + c_square == b_square
      or b_square + c_square == a_square
  )


def 							lowerCAmelCase__    (       a__ = 50      )	->int:




  '''simple docstring'''

  return sum(
      1
      for pta, pta in combinations(product(range(limit + 1      )  ,			repeat=2      )  ,			2      )
      if is_right(*a__  ,			*a__      )      )


if __name__ == "__main__":
   print(F"{solution() = }")




