
"""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


_a   :      Tuple       =				logging.get_logger(__name__)

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






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       RetriBertConfig
    __lowercase				:			Union[str, Any]	=       None
    __lowercase				:			str	=       """retribert"""
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      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   :      str       =				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."""		,       _snake_case		,       )
class   _UpperCAmelCase					(       _snake_case):
    def __init__(     self    ,     snake_case_  ):
      super().__init__(snake_case_  )
      _snake_case  :    str            = config.projection_dim

      _snake_case  :    Tuple            = BertModel(snake_case_  )
      _snake_case  :    Dict            = None if config.share_encoders else BertModel(snake_case_  )
      _snake_case  :    List[Any]            = nn.Dropout(config.hidden_dropout_prob  )
      _snake_case  :    int            = nn.Linear(config.hidden_size    ,     config.projection_dim    ,     bias=snake_case_  )
      _snake_case  :    str            = nn.Linear(config.hidden_size    ,     config.projection_dim    ,     bias=snake_case_  )

      _snake_case  :    Union[str, Any]            = nn.CrossEntropyLoss(reduction="mean"  )

      # Initialize weights and apply final processing
      self.post_init()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_=-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
        _snake_case  :    str            = input_ids.device
        _snake_case  :    Optional[int]            = input_ids.size()
        _snake_case  :    List[str]            = torch.zeros(snake_case_    ,     dtype=torch.long    ,     device=snake_case_  )
        _snake_case  :    Optional[Any]            = [None] * sent_encoder.config.num_hidden_layers
        _snake_case  :    torch.Tensor            = sent_encoder.get_extended_attention_mask(
            snake_case_    ,     snake_case_  )

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

        # run embedding layer on everything at once
        _snake_case  :    str            = 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
        _snake_case  :    Union[str, Any]            = []
        for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size  )  ):
          _snake_case  :    Union[str, Any]            = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
          _snake_case  :    Any            = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
          _snake_case  :    Optional[Any]            = checkpoint.checkpoint(snake_case_    ,     snake_case_    ,     snake_case_  )
          pooled_output_list.append(snake_case_  )
        return torch.cat(snake_case_    ,     dim=0  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=None    ,     snake_case_=-1    ,     ):
      _snake_case  :    int            = self.embed_sentences_checkpointed(
          snake_case_    ,     snake_case_    ,     self.bert_query    ,     snake_case_    ,     )
      return self.project_query(snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=None    ,     snake_case_=-1    ,     ):
      _snake_case  :    Tuple            = 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        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_ = -1    ,     ):
      _snake_case  :    Optional[Any]            = input_ids_query.device
      _snake_case  :    Union[str, Any]            = self.embed_questions(snake_case_    ,     snake_case_    ,     snake_case_  )
      _snake_case  :    Dict            = self.embed_answers(snake_case_    ,     snake_case_    ,     snake_case_  )
      _snake_case  :    Tuple            = torch.mm(snake_case_    ,     a_reps.t()  )
      _snake_case  :    Union[str, Any]            = self.ce_loss(snake_case_    ,     torch.arange(compare_scores.shape[1]  ).to(snake_case_  )  )
      _snake_case  :    Union[str, Any]            = self.ce_loss(compare_scores.t()    ,     torch.arange(compare_scores.shape[0]  ).to(snake_case_  )  )
      _snake_case  :    Optional[Any]            = (loss_qa + loss_aq) / 2
      return loss






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''
						a__ =	"mbart"
						a__ =	["past_key_values"]
						a__ =	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







						def __init__(						self   :      int						,						__lowerCamelCase   :      Optional[int]=5_02_65						,						__lowerCamelCase   :      str=10_24						,						__lowerCamelCase   :      str=12						,						__lowerCamelCase   :      Dict=40_96						,						__lowerCamelCase   :      str=16						,						__lowerCamelCase   :      int=12						,						__lowerCamelCase   :      Optional[Any]=40_96						,						__lowerCamelCase   :      Any=16						,						__lowerCamelCase   :      Optional[Any]=0.0						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      Dict=True						,						__lowerCamelCase   :      List[str]=True						,						__lowerCamelCase   :      Optional[int]="gelu"						,						__lowerCamelCase   :      List[Any]=10_24						,						__lowerCamelCase   :      Dict=0.1						,						__lowerCamelCase   :      Tuple=0.0						,						__lowerCamelCase   :      List[str]=0.0						,						__lowerCamelCase   :      Union[str, Any]=0.02						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      List[Any]=False						,						__lowerCamelCase   :      Optional[Any]=1						,						__lowerCamelCase   :      str=0						,						__lowerCamelCase   :      Optional[Any]=2						,						__lowerCamelCase   :      Dict=2						,						**__lowerCamelCase   :      int						,						)   ->					Union[str, Any]:
									A				:       Union[str, Any]														=    vocab_size
									A				:       List[str]														=    max_position_embeddings
									A				:       Optional[int]														=    d_model
									A				:       Any														=    encoder_ffn_dim
									A				:       Union[str, Any]														=    encoder_layers
									A				:       int														=    encoder_attention_heads
									A				:       Union[str, Any]														=    decoder_ffn_dim
									A				:       Any														=    decoder_layers
									A				:       Tuple														=    decoder_attention_heads
									A				:       Any														=    dropout
									A				:       Union[str, Any]														=    attention_dropout
									A				:       str														=    activation_dropout
									A				:       Dict														=    activation_function
									A				:       Optional[int]														=    init_std
									A				:       Optional[Any]														=    encoder_layerdrop
									A				:       Union[str, Any]														=    decoder_layerdrop
									A				:       Tuple														=    classifier_dropout
									A				:       Union[str, Any]														=    use_cache
									A				:       List[str]														=    encoder_layers
									A				:       List[str]														=    scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__lowerCamelCase						,						bos_token_id=__lowerCamelCase						,						eos_token_id=__lowerCamelCase						,						is_encoder_decoder=__lowerCamelCase						,						forced_eos_token_id=__lowerCamelCase						,						**__lowerCamelCase						,						)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[str]														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)

												if self.use_past:
															A				:       str														=    {0: "batch"}
															A				:       Optional[int]														=    {0: "batch", 1: "past_decoder_sequence + sequence"}
												else:
															A				:       Optional[int]														=    {0: "batch", 1: "decoder_sequence"}
															A				:       Union[str, Any]														=    {0: "batch", 1: "decoder_sequence"}

												if self.use_past:
															self.fill_with_past_key_values_(__lowerCamelCase						,						direction="inputs"			)
									elif self.task == "causal-lm":
												# TODO: figure this case out.
												A				:       Dict														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)
												if self.use_past:
															A							,			A				:       Union[str, Any]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[str]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       int														=    {0: "batch", 2: "past_sequence + sequence"}
									else:
												A				:       Optional[int]														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
												        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
												    ]			)

									return common_inputs







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       str														=    super().outputs
									else:
												A				:       int														=    super(__lowerCamelCase						,						self			).outputs
												if self.use_past:
															A							,			A				:       Optional[int]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[Any]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       Tuple														=    {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       Tuple														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)

									# Generate decoder inputs
									A				:       Optional[int]														=    seq_length if not self.use_past else 1
									A				:       str														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									A				:       Tuple														=    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									A				:       Union[str, Any]														=    dict(**__lowerCamelCase						,						**__lowerCamelCase			)

									if self.use_past:
												if not is_torch_available():
															raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
												else:
															import torch
												A							,			A				:       List[Any]														=    common_inputs["input_ids"].shape
												A				:       Any														=    common_inputs["decoder_input_ids"].shape[1]
												A							,			A				:       Union[str, Any]														=    self.num_attention_heads
												A				:       Tuple														=    (
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												A				:       Dict														=    decoder_seq_length + 3
												A				:       Union[str, Any]														=    (
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)

												A				:       int														=    torch.cat(
												    [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase			)]						,						dim=1			)

												A				:       Optional[int]														=    []
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												A							,			A				:       Dict														=    self.num_layers
												A				:       Dict														=    min(__lowerCamelCase						,						__lowerCamelCase			)
												A				:       Optional[Any]														=    max(__lowerCamelCase						,						__lowerCamelCase			) - min_num_layers
												A				:       Tuple														=    "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

												for _ in range(__lowerCamelCase			):
															common_inputs["past_key_values"].append(
															    (
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															    )			)
												# TODO: test this.
												A				:       List[str]														=    encoder_shape if remaining_side_name == "encoder" else decoder_shape
												for _ in range(__lowerCamelCase						,						__lowerCamelCase			):
															common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			))			)
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       List[Any]														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)

									if self.use_past:
												if not is_torch_available():
															raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
												else:
															import torch
												A							,			A				:       List[Any]														=    common_inputs["input_ids"].shape
												# Not using the same length for past_key_values
												A				:       Any														=    seqlen + 2
												A							,			A				:       List[Any]														=    self.num_layers
												A							,			A				:       int														=    self.num_attention_heads
												A				:       int														=    (
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												A				:       Tuple														=    common_inputs["attention_mask"].dtype
												A				:       Dict														=    torch.cat(
												    [common_inputs["attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase						,						dtype=__lowerCamelCase			)]						,						dim=1			)
												A				:       Optional[Any]														=    [
												    (torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			)) for _ in range(__lowerCamelCase			)
												]
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									A				:       Any														=    compute_effective_axis_dimension(
									    __lowerCamelCase						,						fixed_dimension=OnnxConfig.default_fixed_batch						,						num_token_to_add=0			)

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									A				:       Any														=    tokenizer.num_special_tokens_to_add(__lowerCamelCase			)
									A				:       Tuple														=    compute_effective_axis_dimension(
									    __lowerCamelCase						,						fixed_dimension=OnnxConfig.default_fixed_sequence						,						num_token_to_add=__lowerCamelCase			)

									# Generate dummy inputs according to compute batch and sequence
									A				:       Dict														=    [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
									A				:       Tuple														=    dict(tokenizer(__lowerCamelCase						,						return_tensors=__lowerCamelCase			)			)
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[Any]														=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)

									elif self.task == "causal-lm":
												A				:       Tuple														=    self._generate_dummy_inputs_for_causal_lm(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)
									else:
												A				:       List[Any]														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)

									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Union[str, Any]						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      List[Any]			)   ->					Dict:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       Any														=    super()._flatten_past_key_values_(__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									else:
												A				:       Optional[Any]														=    super(__lowerCamelCase						,						self			)._flatten_past_key_values_(
												    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)