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:    Any     						=							logging.get_logger(__name__)

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

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   RetriBertConfig
		A_    			=   None
		A_    			=   '''retribert'''



		def 					_a					(      self     :					str	,       __SCREAMING_SNAKE_CASE     :					List[str] ):






					"""simple docstring"""
					if isinstance(__SCREAMING_SNAKE_CASE	,       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(__SCREAMING_SNAKE_CASE	,       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(__SCREAMING_SNAKE_CASE	,       nn.LayerNorm ):
								module.bias.data.zero_()
								module.weight.data.fill_(1.0 )
_SCREAMING_SNAKE_CASE:    Dict     						=							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.'''   ,  _lowercase   ,  )
class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		def __init__(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					RetriBertConfig ):






					"""simple docstring"""
					super().__init__(__SCREAMING_SNAKE_CASE )
					_snake_case														=   config.projection_dim

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

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

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



		def 					_a					(      self     :					Dict	,       __SCREAMING_SNAKE_CASE     :					str	,       __SCREAMING_SNAKE_CASE     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					Any	,       __SCREAMING_SNAKE_CASE     :					List[str]=-1	,       ):






					"""simple docstring"""
					# reproduces BERT forward pass with checkpointing
					if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
								return sent_encoder(__SCREAMING_SNAKE_CASE	,       attention_mask=__SCREAMING_SNAKE_CASE )[1]
					else:
								# prepare implicit variables
								_snake_case														=   input_ids.device
								_snake_case														=   input_ids.size()
								_snake_case														=   torch.zeros(__SCREAMING_SNAKE_CASE	,       dtype=torch.long	,       device=__SCREAMING_SNAKE_CASE )
								_snake_case														=   [None] * sent_encoder.config.num_hidden_layers
								_snake_case														=   sent_encoder.get_extended_attention_mask(
								    __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )

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

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



		def 					_a					(      self     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Dict=None	,       __SCREAMING_SNAKE_CASE     :					List[str]=-1	,       ):






					"""simple docstring"""
					_snake_case														=   self.embed_sentences_checkpointed(
					    __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       self.bert_query	,       __SCREAMING_SNAKE_CASE	,       )
					return self.project_query(__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					int	,       __SCREAMING_SNAKE_CASE     :					Dict=None	,       __SCREAMING_SNAKE_CASE     :					Dict=-1	,       ):






					"""simple docstring"""
					_snake_case														=   self.embed_sentences_checkpointed(
					    __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       self.bert_query if self.bert_doc is None else self.bert_doc	,       __SCREAMING_SNAKE_CASE	,       )
					return self.project_doc(__SCREAMING_SNAKE_CASE )







		def 					_a					(      self     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					torch.LongTensor	,       __SCREAMING_SNAKE_CASE     :					Optional[torch.FloatTensor]	,       __SCREAMING_SNAKE_CASE     :					torch.LongTensor	,       __SCREAMING_SNAKE_CASE     :					Optional[torch.FloatTensor]	,       __SCREAMING_SNAKE_CASE     :					int = -1	,       ):






					"""simple docstring"""
					_snake_case														=   input_ids_query.device
					_snake_case														=   self.embed_questions(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
					_snake_case														=   self.embed_answers(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
					_snake_case														=   torch.mm(__SCREAMING_SNAKE_CASE	,       a_reps.t() )
					_snake_case														=   self.ce_loss(__SCREAMING_SNAKE_CASE	,       torch.arange(compare_scores.shape[1] ).to(__SCREAMING_SNAKE_CASE ) )
					_snake_case														=   self.ce_loss(compare_scores.t()	,       torch.arange(compare_scores.shape[0] ).to(__SCREAMING_SNAKE_CASE ) )
					_snake_case														=   (loss_qa + loss_aq) / 2
					return loss


def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   len(lowerCamelCase_	)
			_snake_case														=   [0] * length
			_snake_case														=   [[False for i in range(lowerCamelCase_	)] for j in range(lowerCamelCase_	)]
			for i, c in enumerate(lowerCamelCase_	):
						_snake_case														=   i
						for j in range(i + 1	):
									if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
												_snake_case														=   True
												_snake_case														=   min(lowerCamelCase_				,						0 if j == 0 else (cut[j - 1] + 1)	)
						_snake_case														=   mincut
			return cut[length - 1]


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:    Dict     						=							input('''Enter the string: ''').strip()
			_SCREAMING_SNAKE_CASE:    str     						=							find_minimum_partitions(s)
			print(F"Minimum number of partitions required for the '{s}' is {ans}")

