


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


__SCREAMING_SNAKE_CASE	:							Union[str, Any]           =  logging.get_logger(__name__)

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


class        __lowerCamelCase      (				lowerCamelCase_ ):


				"""simple docstring"""







				a_:		str					      =	RetriBertConfig
				a_:		Dict					      =	None
				a_:		Optional[Any]					      =	"""retribert"""






				def        lowerCAmelCase__			(				self     :    Union[str, Any]	,							lowerCamelCase_     :    str					):
								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					)
__SCREAMING_SNAKE_CASE	:							Optional[int]           =  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."""				,		lowerCamelCase_				,		)
class        __lowerCamelCase      (				lowerCamelCase_ ):


				"""simple docstring"""







				def __init__(				self     :    List[str]	,							lowerCamelCase_     :    RetriBertConfig					):
								super().__init__(lowerCamelCase_					)
								_lowerCAmelCase  						=config.projection_dim

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

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

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






				def        lowerCAmelCase__			(				self     :    Tuple	,							lowerCamelCase_     :    List[Any]	,							lowerCamelCase_     :    int	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    Optional[Any]=-1	,							):
								# 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
												_lowerCAmelCase  						=input_ids.device
												_lowerCAmelCase  						=input_ids.size()
												_lowerCAmelCase  						=torch.zeros(lowerCamelCase_	,							dtype=torch.long	,							device=lowerCamelCase_					)
												_lowerCAmelCase  						=[None] * sent_encoder.config.num_hidden_layers
												_lowerCAmelCase  						=sent_encoder.get_extended_attention_mask(
												    lowerCamelCase_	,							lowerCamelCase_					)

												# define function for checkpointing
												def partial_encode(*lowerCamelCase_     :    Dict					):
																_lowerCAmelCase  						=sent_encoder.encoder(
																    inputs[0]	,							attention_mask=inputs[1]	,							head_mask=lowerCamelCase_	,							)
																_lowerCAmelCase  						=encoder_outputs[0]
																_lowerCAmelCase  						=sent_encoder.pooler(lowerCamelCase_					)
																return pooled_output

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






				def        lowerCAmelCase__			(				self     :    Tuple	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    Union[str, Any]=None	,							lowerCamelCase_     :    Any=-1	,							):
								_lowerCAmelCase  						=self.embed_sentences_checkpointed(
								    lowerCamelCase_	,							lowerCamelCase_	,							self.bert_query	,							lowerCamelCase_	,							)
								return self.project_query(lowerCamelCase_					)






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





from __future__ import annotations

import math
from itertools import permutations

def 		snake_case_   (				lowercase__     :    int     ):






				'''simple docstring'''






				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5       ,     int(math.sqrt(lowercase__     ) + 1     )       ,     6     ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def 		snake_case_   (				lowercase__     :    int = 7     ):






				'''simple docstring'''






				_lowerCAmelCase  						="""""".join(str(lowercase__     ) for i in range(1       ,     n + 1     )     )
				_lowerCAmelCase  						=[int("""""".join(lowercase__     )     ) for i in permutations(lowercase__       ,     lowercase__     )]
				_lowerCAmelCase  						=[num for num in perm_list if is_prime(lowercase__     )]
				return max(lowercase__     ) if pandigitals else 0


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

