code: 
  
  
  """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
  
  
  __magic_name__	:   Optional[Any]          =							logging.get_logger(__name__)
  
  __magic_name__	:   List[str]          =							[
      'yjernite/retribert-base-uncased',
      # See all RetriBert models at https://huggingface.co/models?filter=retribert
  ]
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  							__a				   =					RetriBertConfig
  							__a				   =					None
  							__a				   =					'''retribert'''
  
  
  
  
  							def      __a      (    self:						Dict    ,       A_:						Optional[Any]      ):
  										if isinstance(A_    ,       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(A_    ,       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(A_    ,       nn.LayerNorm      ):
  													module.bias.data.zero_()
  													module.weight.data.fill_(1.0      )
  
  __magic_name__	:   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.''' ,		lowerCamelCase ,		)
  class  __snake_case      (lowerCamelCase     ):
  
  
  
  
  							def __init__(    self:						int    ,       A_:						RetriBertConfig      ):
  										super().__init__(A_      )
  										__lowerCamelCase        =			config.projection_dim
  
  										__lowerCamelCase        =			BertModel(A_      )
  										__lowerCamelCase        =			None if config.share_encoders else BertModel(A_      )
  										__lowerCamelCase        =			nn.Dropout(config.hidden_dropout_prob      )
  										__lowerCamelCase        =			nn.Linear(config.hidden_size    ,       config.projection_dim    ,       bias=A_      )
  										__lowerCamelCase        =			nn.Linear(config.hidden_size    ,       config.projection_dim    ,       bias=A_      )
  
  										__lowerCamelCase        =			nn.CrossEntropyLoss(reduction="""mean"""      )
  
  										# Initialize weights and apply final processing
  										self.post_init()
  
  
  
  
  							def      __a      (    self:						Tuple    ,       A_:						Union[str, Any]    ,       A_:						Any    ,       A_:						List[str]    ,       A_:						Any=-1    ,       ):
  										# reproduces BERT forward pass with checkpointing
  										if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
  													return sent_encoder(A_    ,       attention_mask=A_      )[1]
  										else:
  													# prepare implicit variables
  													__lowerCamelCase        =			input_ids.device
  													__lowerCamelCase        =			input_ids.size()
  													__lowerCamelCase        =			torch.zeros(A_    ,       dtype=torch.long    ,       device=A_      )
  													__lowerCamelCase        =			[None] * sent_encoder.config.num_hidden_layers
  													__lowerCamelCase        =			sent_encoder.get_extended_attention_mask(
  													    A_    ,       A_      )
  
  													# define function for checkpointing
  													def partial_encode(*A_:						Optional[int]      ):
  																__lowerCamelCase        =			sent_encoder.encoder(
  																    inputs[0]    ,       attention_mask=inputs[1]    ,       head_mask=A_    ,       )
  																__lowerCamelCase        =			encoder_outputs[0]
  																__lowerCamelCase        =			sent_encoder.pooler(A_      )
  																return pooled_output
  
  													# run embedding layer on everything at once
  													__lowerCamelCase        =			sent_encoder.embeddings(
  													    input_ids=A_    ,       position_ids=A_    ,       token_type_ids=A_    ,       inputs_embeds=A_      )
  													# 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(A_    ,       A_    ,       A_      )
  																pooled_output_list.append(A_      )
  													return torch.cat(A_    ,       dim=0      )
  
  
  
  
  							def      __a      (    self:						str    ,       A_:						List[Any]    ,       A_:						Dict=None    ,       A_:						Optional[Any]=-1    ,       ):
  										__lowerCamelCase        =			self.embed_sentences_checkpointed(
  										    A_    ,       A_    ,       self.bert_query    ,       A_    ,       )
  										return self.project_query(A_      )
  
  
  
  
  							def      __a      (    self:						Dict    ,       A_:						int    ,       A_:						List[str]=None    ,       A_:						int=-1    ,       ):
  										__lowerCamelCase        =			self.embed_sentences_checkpointed(
  										    A_    ,       A_    ,       self.bert_query if self.bert_doc is None else self.bert_doc    ,       A_    ,       )
  										return self.project_doc(A_      )
  
  
  
  
  							def      __a      (    self:						List[Any]    ,       A_:						torch.LongTensor    ,       A_:						Optional[torch.FloatTensor]    ,       A_:						torch.LongTensor    ,       A_:						Optional[torch.FloatTensor]    ,       A_:						int = -1    ,       ):
  										__lowerCamelCase        =			input_ids_query.device
  										__lowerCamelCase        =			self.embed_questions(A_    ,       A_    ,       A_      )
  										__lowerCamelCase        =			self.embed_answers(A_    ,       A_    ,       A_      )
  										__lowerCamelCase        =			torch.mm(A_    ,       a_reps.t()      )
  										__lowerCamelCase        =			self.ce_loss(A_    ,       torch.arange(compare_scores.shape[1]      ).to(A_      )      )
  										__lowerCamelCase        =			self.ce_loss(compare_scores.t()    ,       torch.arange(compare_scores.shape[0]      ).to(A_      )      )
  										__lowerCamelCase        =			(loss_qa + loss_aq) / 2
  										return loss
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  from transformers import load_tool
  from transformers.tools.agent_types import AGENT_TYPE_MAPPING
  
  from .test_tools_common import ToolTesterMixin, output_types
  
  
  
  
  class  __snake_case      (unittest.TestCase ,		lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										__lowerCamelCase        =			load_tool("""translation"""      )
  										self.tool.setup()
  										__lowerCamelCase        =			load_tool("""translation"""    ,       remote=A_      )
  
  
  
  
  							def      __a      (    self:						int      ):
  										__lowerCamelCase        =			self.tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						int      ):
  										__lowerCamelCase        =			self.remote_tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										__lowerCamelCase        =			self.tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						Any      ):
  										__lowerCamelCase        =			self.remote_tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						List[Any]      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										# There is a single output
  										if len(self.tool.outputs      ) == 1:
  													__lowerCamelCase        =			[outputs]
  
  										self.assertListEqual(output_types(A_      )    ,       self.tool.outputs      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										if not isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[outputs]
  
  										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )
  
  										for output, output_type in zip(A_    ,       self.tool.outputs      ):
  													__lowerCamelCase        =			AGENT_TYPE_MAPPING[output_type]
  													self.assertTrue(isinstance(A_    ,       A_      )      )
  
  
  
  
  							def      __a      (    self:						Dict      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  
  										__lowerCamelCase        =			[]
  
  										for _input, input_type in zip(A_    ,       self.tool.inputs      ):
  													if isinstance(A_    ,       A_      ):
  																_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input      ) for _input_type in input_type]      )
  													else:
  																_inputs.append(AGENT_TYPE_MAPPING[input_type](_input      )      )
  
          # Should not raise an error
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										if not isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[outputs]
  
  										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )
  
  
  
  
  