
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


_UpperCAmelCase    			=					logging.get_logger(__name__)

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

class 		__magic_name__				( UpperCAmelCase_					):


  """simple docstring"""





  _UpperCamelCase						 = RetriBertConfig
  _UpperCamelCase						 = None
  _UpperCamelCase						 = "retribert"


  def     _UpperCAmelCase  (					self	,		a__		):
    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		)







_UpperCAmelCase    			=					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."						,UpperCAmelCase_						,)
class 		__magic_name__				( UpperCAmelCase_					):


  """simple docstring"""





  def __init__(					self	,		a__		):
    super().__init__(__UpperCamelCase		)
    _lowerCamelCase      =						config.projection_dim

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

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

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


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		a__=-1	,		):
    # reproduces BERT forward pass with checkpointing
    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
      _lowerCamelCase      =						input_ids.device
      _lowerCamelCase      =						input_ids.size()
      _lowerCamelCase      =						torch.zeros(__UpperCamelCase	,		dtype=torch.long	,		device=__UpperCamelCase		)
      _lowerCamelCase      =						[None] * sent_encoder.config.num_hidden_layers
      _lowerCamelCase      =						sent_encoder.get_extended_attention_mask(
          __UpperCamelCase	,		__UpperCamelCase		)

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

      # run embedding layer on everything at once
      _lowerCamelCase      =						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
      _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(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase		)
        pooled_output_list.append(__UpperCamelCase		)
      return torch.cat(__UpperCamelCase	,		dim=0		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__=None	,		a__=-1	,		):
    _lowerCamelCase      =						self.embed_sentences_checkpointed(
        __UpperCamelCase	,		__UpperCamelCase	,		self.bert_query	,		__UpperCamelCase	,		)
    return self.project_query(__UpperCamelCase		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__=None	,		a__=-1	,		):
    _lowerCamelCase      =						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	,		a__	,		a__	,		a__	,		a__	,		a__ = -1	,		):
    _lowerCamelCase      =						input_ids_query.device
    _lowerCamelCase      =						self.embed_questions(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase		)
    _lowerCamelCase      =						self.embed_answers(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase		)
    _lowerCamelCase      =						torch.mm(__UpperCamelCase	,		a_reps.t()		)
    _lowerCamelCase      =						self.ce_loss(__UpperCamelCase	,		torch.arange(compare_scores.shape[1]		).to(__UpperCamelCase		)		)
    _lowerCamelCase      =						self.ce_loss(compare_scores.t()	,		torch.arange(compare_scores.shape[0]		).to(__UpperCamelCase		)		)
    _lowerCamelCase      =						(loss_qa + loss_aq) / 2
    return loss










'''simple docstring'''







import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union





class 	lowerCamelCase_				(			enum.Enum				):




					'''simple docstring'''






					lowercase__       =					"""timm"""
					lowercase__       =					"""transformers"""






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if stage_names is None:
				raise ValueError("Stage_names must be set for transformers backbones"    )

		if out_features is not None:
				if not isinstance(lowercase_		,				(list,)    ):
						raise ValueError(f'''out_features must be a list {type(lowercase_    )}'''    )
				if any(feat not in stage_names for feat in out_features    ):
						raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''    )

		if out_indices is not None:
				if not isinstance(lowercase_		,				(list, tuple)    ):
						raise ValueError(f'''out_indices must be a list or tuple, got {type(lowercase_    )}'''    )
				if any(idx >= len(lowercase_    ) for idx in out_indices    ):
						raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"    )

		if out_features is not None and out_indices is not None:
				if len(lowercase_    ) != len(lowercase_    ):
						raise ValueError("out_features and out_indices should have the same length if both are set"    )
				if out_features != [stage_names[idx] for idx in out_indices]:
						raise ValueError("out_features and out_indices should correspond to the same stages if both are set"    )






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				):



		'''simple docstring'''







		if out_indices is None and out_features is None:
				_snake_case      			=			[len(lowercase_    ) - 1]
				_snake_case      			=			[stage_names[-1]]
		elif out_indices is None and out_features is not None:
				_snake_case      			=			[stage_names.index(lowercase_    ) for layer in out_features]
		elif out_features is None and out_indices is not None:
				_snake_case      			=			[stage_names[idx] for idx in out_indices]
		return out_features, out_indices






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				):



		'''simple docstring'''







		verify_out_features_out_indices(out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		_snake_case			,       _snake_case      			=			_align_output_features_output_indices(
		    out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		# Verify that the aligned out_features and out_indices are valid
		verify_out_features_out_indices(out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		return output_features, output_indices





class 	lowerCamelCase_				:




					'''simple docstring'''






					lowercase__       =					None






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							if getattr(self,    "_backbone",    __UpperCamelCase					) is None:
									raise ValueError("self._backbone must be set before calling _init_timm_backbone"					)

							# These will diagree with the defaults for the transformers models e.g. for resnet50
							# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
							# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
							_snake_case      			=			[stage["module"] for stage in self._backbone.feature_info.info]
							_snake_case      			=			[stage["num_chs"] for stage in self._backbone.feature_info.info]
							_snake_case      			=			self._backbone.feature_info.out_indices
							_snake_case      			=			self._backbone.feature_info.module_name()

							# We verify the out indices and out features are valid
							verify_out_features_out_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)
							_snake_case			,       _snake_case      			=			out_features, out_indices






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			getattr(__UpperCamelCase,    "stage_names"					)
							_snake_case      			=			getattr(__UpperCamelCase,    "out_features",    __UpperCamelCase					)
							_snake_case      			=			getattr(__UpperCamelCase,    "out_indices",    __UpperCamelCase					)

							_snake_case      			=			stage_names
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=__UpperCamelCase					)
							# Number of channels for each stage. This is set in the transformer backbone model init
							_snake_case      			=			None






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			config

							_snake_case      			=			getattr(__UpperCamelCase,    "use_timm_backbone",    __UpperCamelCase					)
							_snake_case      			=			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

							if self.backbone_type == BackboneType.TIMM:
									self._init_timm_backbone(__UpperCamelCase					)
							elif self.backbone_type == BackboneType.TRANSFORMERS:
									self._init_transformers_backbone(__UpperCamelCase					)
							else:
									raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_features






					@out_features.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_indices






					@out_indices.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# the current backbones will output the number of channels for each stage
							# even if that stage is not in the out_features list.
							return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return [self.out_feature_channels[name] for name in self.out_features]






					def 							__SCREAMING_SNAKE_CASE      (       self,    *__UpperCamelCase,    **__UpperCamelCase					):
							_snake_case      			=			dict(inspect.signature(self.forward					).parameters					)
							_snake_case      			=			{k: v for k, v in kwargs.items() if k in signature}
							return self(*__UpperCamelCase,    **__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase = None,    __UpperCamelCase = None,    __UpperCamelCase = None,    ):
							raise NotImplementedError("This method should be implemented by the derived class."					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			super().to_dict()
							_snake_case      			=			output.pop("_out_features"					)
							_snake_case      			=			output.pop("_out_indices"					)
							return output





class 	lowerCamelCase_				:




					'''simple docstring'''






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_features






					@out_features.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_indices






					@out_indices.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			super().to_dict()
							_snake_case      			=			output.pop("_out_features"					)
							_snake_case      			=			output.pop("_out_indices"					)
							return output





