





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


lowerCAmelCase_        =			logging.get_logger(__name__)

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



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				Dict					    =RetriBertConfig
  a_							:				List[str]					    =None
  a_							:				int					    ="""retribert"""







  def UpperCamelCase_       (					self					:    Tuple					, UpperCamelCase					:    Optional[int]							):


    '''simple docstring'''





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




lowerCAmelCase_        =			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."""							,			UpperCAmelCase_							,			)
class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  def __init__(					self					:    Any					, UpperCamelCase					:    RetriBertConfig							):


    '''simple docstring'''





    super().__init__(UpperCamelCase							)
    _snake_case      : int														=		config.projection_dim

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

    _snake_case      : Any														=		nn.CrossEntropyLoss(reduction='mean'							)

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







  def UpperCamelCase_       (					self					:    Optional[Any]					, UpperCamelCase					:    Tuple					, UpperCamelCase					:    Optional[Any]					, UpperCamelCase					:    Optional[Any]					, UpperCamelCase					:    Any=-1					, ):


    '''simple docstring'''





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

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

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







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    int					, UpperCamelCase					:    Any=None					, UpperCamelCase					:    str=-1					, ):


    '''simple docstring'''





    _snake_case      : Optional[int]														=		self.embed_sentences_checkpointed(
        UpperCamelCase					, UpperCamelCase					, self.bert_query					, UpperCamelCase					, )
    return self.project_query(UpperCamelCase							)







  def UpperCamelCase_       (					self					:    int					, UpperCamelCase					:    Union[str, Any]					, UpperCamelCase					:    int=None					, UpperCamelCase					:    Tuple=-1					, ):


    '''simple docstring'''





    _snake_case      : Union[str, Any]														=		self.embed_sentences_checkpointed(
        UpperCamelCase					, UpperCamelCase					, self.bert_query if self.bert_doc is None else self.bert_doc					, UpperCamelCase					, )
    return self.project_doc(UpperCamelCase							)







  def UpperCamelCase_       (					self					:    str					, UpperCamelCase					:    torch.LongTensor					, UpperCamelCase					:    Optional[torch.FloatTensor]					, UpperCamelCase					:    torch.LongTensor					, UpperCamelCase					:    Optional[torch.FloatTensor]					, UpperCamelCase					:    int = -1					, ):


    '''simple docstring'''





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











'''simple docstring'''







from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					list:
			seed(1	)
			return [gauss(lowercase					,	lowercase	) for _ in range(lowercase	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					list:
			return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return sum(lowercase	) / instance_count





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return instance_count / total_count





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				[]  # An empty list to store all squared differences
			# iterate over number of elements in items
			for i in range(len(lowercase	)	):
						# for loop iterates over number of elements in inner layer of items
						for j in range(len(items[i]	)	):
									# appending squared differences to 'squared_diff' list
									squared_diff.append((items[i][j] - means[i]) ** 2	)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
			__lowerCAmelCase											=				len(lowercase	)  # Number of classes in dataset
			return 1 / (total_count - n_classes) * sum(lowercase	)





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase	)					->					list:
			__lowerCAmelCase											=				[]
			# for loop iterates over number of elements in list
			for i in range(len(lowercase	)	):
						# for loop iterates over number of inner items of each element
						for j in range(len(x_items[i]	)	):
									__lowerCAmelCase											=				[]  # to store all discriminant values of each item as a list
									# for loop iterates over number of classes we have in our dataset
									for k in range(len(lowercase	)	):
												# appending values of discriminants for each class to 'temp' list
												temp.append(
												    x_items[i][j] * (means[k] / variance)
												    - (means[k] ** 2 / (2 * variance))
												    + log(probabilities[k]	)	)
									# appending discriminant values of each item to 'results' list
									results.append(lowercase	)

			return [result.index(max(lowercase	)	) for result in results]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				sum(1 for i, j in zip(lowercase					,	lowercase	) if i == j	)
			# percentage of accuracy equals to number of correct predictions divided by number
			# of all data and multiplied by 100
			return (correct / len(lowercase	)) * 100


_a					: Optional[int]      =    TypeVar("""num""")





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase = lambda lowercase	: True					,	lowercase = None					,	)					->					num:
			while True:
						try:
									__lowerCAmelCase											=				input_type(input(lowercase	).strip() or default	)
									if condition(lowercase	):
												return user_input
									else:
												print(f'{user_input}: {err_msg}'	)
												continue
						except ValueError:
									print(
									    f'{user_input}: Incorrect input type, expected {input_type.__name__!r}'	)





def    _lowerCAmelCase						(						)					->					List[str]:
			while True:
						print(""" Linear Discriminant Analysis """.center(50					,	"""*"""	)	)
						print("""*""" * 50					,	"""\n"""	)
						print("""First of all we should specify the number of classes that"""	)
						print("""we want to generate as training dataset"""	)
						# Trying to get number of classes
						__lowerCAmelCase											=				valid_input(
						    input_type=lowercase					,	condition=lambda lowercase	: x > 0					,	input_msg="""Enter the number of classes (Data Groupings): """					,	err_msg="""Number of classes should be positive!"""					,	)

						print("""-""" * 100	)

						# Trying to get the value of standard deviation
						__lowerCAmelCase											=				valid_input(
						    input_type=lowercase					,	condition=lambda lowercase	: x >= 0					,	input_msg=(
						        """Enter the value of standard deviation"""
						        """(Default value is 1.0 for all classes): """
						    )					,	err_msg="""Standard deviation should not be negative!"""					,	default="""1.0"""					,	)

						print("""-""" * 100	)

						# Trying to get number of instances in classes and theirs means to generate
						# dataset
						__lowerCAmelCase											=				[]  # An empty list to store instance counts of classes in dataset
						for i in range(lowercase	):
									__lowerCAmelCase											=				valid_input(
									    input_type=lowercase					,	condition=lambda lowercase	: x > 0					,	input_msg=(f'Enter The number of instances for class_{i+1}: ')					,	err_msg="""Number of instances should be positive!"""					,	)
									counts.append(lowercase	)
						print("""-""" * 100	)

						# An empty list to store values of user-entered means of classes
						__lowerCAmelCase											=				[]
						for a in range(lowercase	):
									__lowerCAmelCase											=				valid_input(
									    input_type=lowercase					,	input_msg=(f'Enter the value of mean for class_{a+1}: ')					,	err_msg="""This is an invalid value."""					,	)
									user_means.append(lowercase	)
						print("""-""" * 100	)

						print("""Standard deviation: """					,	lowercase	)
						# print out the number of instances in classes in separated line
						for i, count in enumerate(lowercase					,	1	):
									print(f'Number of instances in class_{i} is: {count}'	)
						print("""-""" * 100	)

						# print out mean values of classes separated line
						for i, user_mean in enumerate(lowercase					,	1	):
									print(f'Mean of class_{i} is: {user_mean}'	)
						print("""-""" * 100	)

						# Generating training dataset drawn from gaussian distribution
						__lowerCAmelCase											=				[
						    gaussian_distribution(user_means[j]					,	lowercase					,	counts[j]	)
						    for j in range(lowercase	)
						]
						print("""Generated Normal Distribution: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Generating Ys to detecting corresponding classes
						__lowerCAmelCase											=				y_generator(lowercase					,	lowercase	)
						print("""Generated Corresponding Ys: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Calculating the value of actual mean for each class
						__lowerCAmelCase											=				[calculate_mean(counts[k]					,	x[k]	) for k in range(lowercase	)]
						# for loop iterates over number of elements in 'actual_means' list and print
						# out them in separated line
						for i, actual_mean in enumerate(lowercase					,	1	):
									print(f'Actual(Real) mean of class_{i} is: {actual_mean}'	)
						print("""-""" * 100	)

						# Calculating the value of probabilities for each class
						__lowerCAmelCase											=				[
						    calculate_probabilities(counts[i]					,	sum(lowercase	)	) for i in range(lowercase	)
						]

						# for loop iterates over number of elements in 'probabilities' list and print
						# out them in separated line
						for i, probability in enumerate(lowercase					,	1	):
									print(f'Probability of class_{i} is: {probability}'	)
						print("""-""" * 100	)

						# Calculating the values of variance for each class
						__lowerCAmelCase											=				calculate_variance(lowercase					,	lowercase					,	sum(lowercase	)	)
						print("""Variance: """					,	lowercase	)
						print("""-""" * 100	)

						# Predicting Y values
						# storing predicted Y values in 'pre_indexes' variable
						__lowerCAmelCase											=				predict_y_values(lowercase					,	lowercase					,	lowercase					,	lowercase	)
						print("""-""" * 100	)

						# Calculating Accuracy of the model
						print(f'Accuracy: {accuracy(lowercase					,	lowercase	)}'	)
						print("""-""" * 100	)
						print(""" DONE """.center(100					,	"""+"""	)	)

						if input("""Press any key to restart or 'q' for quit: """	).strip().lower() == "q":
									print("""\n""" + """GoodBye!""".center(100					,	"""-"""	) + """\n"""	)
									break
						system("""cls""" if name == """nt""" else """clear"""	)  # noqa: S605


if __name__ == "__main__":
	main()

