



"""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


_UpperCamelCase:					Tuple     				=	logging.get_logger(__name__)

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


class 	a__		(    lowercase_  ):
   _lowerCamelCase       				=					RetriBertConfig
   _lowerCamelCase       				=					None
   _lowerCamelCase       				=					'retribert'


   def 							lowercase					(				self   :				Optional[int],  lowerCAmelCase   :				int      )     ->							Optional[int]:
         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      )




_UpperCamelCase:					Dict     				=	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.',	lowercase_,	)
class 	a__		(    lowercase_  ):


   def __init__(				self   :				Optional[int],  lowerCAmelCase   :				Tuple      )     ->							Any:
         super().__init__(_lowerCAmelCase      )
         lowercase	:   Tuple											= config.projection_dim

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

         lowercase	:   str											= nn.CrossEntropyLoss(reduction='mean'      )

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


   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				str,  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				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(_lowerCAmelCase,  attention_mask=_lowerCAmelCase      )[1]
         else:
               # prepare implicit variables
               lowercase	:   str											= input_ids.device
               lowercase	:   int											= input_ids.size()
               lowercase	:   int											= torch.zeros(_lowerCAmelCase,  dtype=torch.long,  device=_lowerCAmelCase      )
               lowercase	:   Optional[Any]											= [None] * sent_encoder.config.num_hidden_layers
               lowercase	:   int											= sent_encoder.get_extended_attention_mask(
                   _lowerCAmelCase,  _lowerCAmelCase      )

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

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


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				int,  lowerCAmelCase   :				List[str]=None,  lowerCAmelCase   :				Optional[Any]=-1,  )     ->							Dict:
         lowercase	:   Any											= self.embed_sentences_checkpointed(
             _lowerCAmelCase,  _lowerCAmelCase,  self.bert_query,  _lowerCAmelCase,  )
         return self.project_query(_lowerCAmelCase      )


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







def  __UpperCamelCase   (     a)			->str:
					if len(a) == 0 or not all(len(a) == 2 for pair in coordinates):
										raise ValueError("The program cannot work out a fitting polynomial.")

					if len({tuple(a) for pair in coordinates}) != len(a):
										raise ValueError("The program cannot work out a fitting polynomial.")

					lowerCamelCase__      =							{x for x, _ in coordinates}
					if len(a) == 1:
										return f"x={coordinates[0][0]}"

					if len(a) != len(a):
										raise ValueError("The program cannot work out a fitting polynomial.")

					lowerCamelCase__      =							len(a)

					lowerCamelCase__      =							0
					lowerCamelCase__      =							[]
					# put the x and x to the power values in a matrix
					while count_of_line < x:
										lowerCamelCase__      =							0
										lowerCamelCase__      =							coordinates[count_of_line][0]
										lowerCamelCase__      =							[]
										while count_in_line < x:
															count_line.append(a ** (x - (count_in_line + 1)))
															count_in_line += 1
										matrix.append(a)
										count_of_line += 1

					lowerCamelCase__      =							0
					# put the y values into a vector
					lowerCamelCase__      =							[]
					while count_of_line < x:
										vector.append(coordinates[count_of_line][1])
										count_of_line += 1

					lowerCamelCase__      =							0

					while count < x:
										lowerCamelCase__      =							0
										while zahlen < x:
															if count == zahlen:
																				zahlen += 1
															if zahlen == x:
																				break
															lowerCamelCase__      =							matrix[zahlen][count] / matrix[count][count]
															for counting_columns, item in enumerate(matrix[count]):
																				# manipulating all the values in the matrix
																				matrix[zahlen][counting_columns] -= item * bruch
															# manipulating the values in the vector
															vector[zahlen] -= vector[count] * bruch
															zahlen += 1
										count += 1

					lowerCamelCase__      =							0
					# make solutions
					lowerCamelCase__      =							[]
					while count < x:
										solution.append(str(vector[count] / matrix[count][count]))
										count += 1

					lowerCamelCase__      =							0
					lowerCamelCase__      =							"f(x)="

					while count < x:
										lowerCamelCase__      =							solution[count].split("E")
										if len(a) > 1:
															lowerCamelCase__      =							f"{remove_e[0]}*10^{remove_e[1]}"
										solved += f"x^{x - (count + 1)}*{solution[count]}"
										if count + 1 != x:
															solved += "+"
										count += 1

					return solved


if __name__ == "__main__":
						print(points_to_polynomial([]))
						print(points_to_polynomial([[]]))
						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
						print(points_to_polynomial([[1, 3], [2, 6], [3, 1_1]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -1_1]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))

