




a_ 		=  [
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						bool:

    """simple docstring"""


    return 0 <= row < rows and 0 <= col < cols




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase			=    len(lowercase_   )
    __UpperCamelCase			=    len(mat[0]   )
    if is_safe(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   ) and (row, col) not in seen and mat[row][col] == 1:
        seen.add((row, col)   )
        return (
            1
            + depth_first_search(row + 1       ,  lowercase_       ,  lowercase_       ,  lowercase_   )
            + depth_first_search(row - 1       ,  lowercase_       ,  lowercase_       ,  lowercase_   )
            + depth_first_search(lowercase_       ,  col + 1       ,  lowercase_       ,  lowercase_   )
            + depth_first_search(lowercase_       ,  col - 1       ,  lowercase_       ,  lowercase_   )
        )
    else:
        return 0




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase			=    set()

    __UpperCamelCase			=    0
    for row, line in enumerate(lowercase_   ):
        for col, item in enumerate(lowercase_   ):
            if item == 1 and (row, col) not in seen:
                # Maximizing the area
                __UpperCamelCase			=    max(lowercase_       ,  depth_first_search(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )   )
    return max_area


if __name__ == "__main__":
 import doctest

 print(find_max_area(matrix))  # Output -> 6
 doctest.testmod()












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 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Optional[Any]     =       RetriBertConfig
      lowerCAmelCase__      :			Optional[Any]     =       None
      lowerCAmelCase__      :			Dict     =       "retribert"







      def    snake_case						(  self       :			Dict			,							snake_case       :			Optional[Any]					):
          if isinstance(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(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(snake_case			,							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."				,      _SCREAMING_SNAKE_CASE				,      )
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def __init__(  self       :			List[Any]			,							snake_case       :			RetriBertConfig					):
          super().__init__(snake_case					)
          __UpperCamelCase			=    config.projection_dim

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

          __UpperCamelCase			=    nn.CrossEntropyLoss(reduction='''mean'''					)

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







      def    snake_case						(  self       :			Dict			,							snake_case       :			Dict			,							snake_case       :			List[str]			,							snake_case       :			Dict			,							snake_case       :			Any=-1			,							):
          # reproduces BERT forward pass with checkpointing
          if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
              return sent_encoder(snake_case			,							attention_mask=snake_case					)[1]
          else:
              # prepare implicit variables
              __UpperCamelCase			=    input_ids.device
              __UpperCamelCase			=    input_ids.size()
              __UpperCamelCase			=    torch.zeros(snake_case			,							dtype=torch.long			,							device=snake_case					)
              __UpperCamelCase			=    [None] * sent_encoder.config.num_hidden_layers
              __UpperCamelCase			=    sent_encoder.get_extended_attention_mask(
                  snake_case			,							snake_case					)

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

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







      def    snake_case						(  self       :			Dict			,							snake_case       :			Any			,							snake_case       :			Union[str, Any]=None			,							snake_case       :			List[str]=-1			,							):
          __UpperCamelCase			=    self.embed_sentences_checkpointed(
              snake_case			,							snake_case			,							self.bert_query			,							snake_case			,							)
          return self.project_query(snake_case					)







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			Optional[Any]			,							snake_case       :			Optional[int]=None			,							snake_case       :			Optional[Any]=-1			,							):
          __UpperCamelCase			=    self.embed_sentences_checkpointed(
              snake_case			,							snake_case			,							self.bert_query if self.bert_doc is None else self.bert_doc			,							snake_case			,							)
          return self.project_doc(snake_case					)







      def    snake_case						(  self       :			List[str]			,							snake_case       :			torch.LongTensor			,							snake_case       :			Optional[torch.FloatTensor]			,							snake_case       :			torch.LongTensor			,							snake_case       :			Optional[torch.FloatTensor]			,							snake_case       :			int = -1			,							):
          __UpperCamelCase			=    input_ids_query.device
          __UpperCamelCase			=    self.embed_questions(snake_case			,							snake_case			,							snake_case					)
          __UpperCamelCase			=    self.embed_answers(snake_case			,							snake_case			,							snake_case					)
          __UpperCamelCase			=    torch.mm(snake_case			,							a_reps.t()					)
          __UpperCamelCase			=    self.ce_loss(snake_case			,							torch.arange(compare_scores.shape[1]					).to(snake_case					)					)
          __UpperCamelCase			=    self.ce_loss(compare_scores.t()			,							torch.arange(compare_scores.shape[0]					).to(snake_case					)					)
          __UpperCamelCase			=    (loss_qa + loss_aq) / 2
          return loss






