
'''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


UpperCAmelCase            = logging.get_logger(__name__)

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

class     a (					__magic_name__     ):
				_snake_case		     =			RetriBertConfig
				_snake_case		     =			None
				_snake_case		     =			'''retribert'''




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					List[Any]   ):
										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   )




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.'''					,__magic_name__					,)
class     a (					__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					RetriBertConfig   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     config.projection_dim

										snake_case							:  Optional[int]			=     BertModel(SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     None if config.share_encoders else BertModel(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     nn.Dropout(config.hidden_dropout_prob   )
										snake_case							:  str			=     nn.Linear(config.hidden_size,     config.projection_dim,     bias=SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     nn.Linear(config.hidden_size,     config.projection_dim,     bias=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Tuple			=     nn.CrossEntropyLoss(reduction='''mean'''   )

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




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Tuple=-1,     ):
										# 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							:  Optional[Any]			=     input_ids.device
																snake_case							:  int			=     input_ids.size()
																snake_case							:  Any			=     torch.zeros(SCREAMING_SNAKE_CASE_,     dtype=torch.long,     device=SCREAMING_SNAKE_CASE_   )
																snake_case							:  Optional[Any]			=     [None] * sent_encoder.config.num_hidden_layers
																snake_case							:  torch.Tensor			=     sent_encoder.get_extended_attention_mask(
																    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )

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

																# run embedding layer on everything at once
																snake_case							:  Union[str, Any]			=     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							:  List[str]			=     []
																for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size   )   ):
																						snake_case							:  Any			=     embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																						snake_case							:  str			=     extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																						snake_case							:  int			=     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 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					List[Any]=-1,     ):
										snake_case							:  Optional[Any]			=     self.embed_sentences_checkpointed(
										    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     self.bert_query,     SCREAMING_SNAKE_CASE_,     )
										return self.project_query(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					List[str]=None,     SCREAMING_SNAKE_CASE_						:					int=-1,     ):
										snake_case							:  int			=     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 						__snake_case				(				self						:					str,     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,     ):
										snake_case							:  Optional[int]			=     input_ids_query.device
										snake_case							:  List[str]			=     self.embed_questions(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[int]			=     self.embed_answers(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     torch.mm(SCREAMING_SNAKE_CASE_,     a_reps.t()   )
										snake_case							:  Optional[Any]			=     self.ce_loss(SCREAMING_SNAKE_CASE_,     torch.arange(compare_scores.shape[1]   ).to(SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  Any			=     self.ce_loss(compare_scores.t(),     torch.arange(compare_scores.shape[0]   ).to(SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  List[str]			=     (loss_qa + loss_aq) / 2
										return loss



'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


a_           =    logging.get_logger(__name__)

a_           =    {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class  UpperCAmelCase_					(						snake_case__       ):
 UpperCAmelCase_		     =    """blenderbot"""
 UpperCAmelCase_		     =    ["""past_key_values"""]
 UpperCAmelCase_		     =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





 def __init__(     self			,							lowercase_=80_08			,							lowercase_=1_28			,							lowercase_=2			,							lowercase_=1_02_40			,							lowercase_=32			,							lowercase_=24			,							lowercase_=1_02_40			,							lowercase_=32			,							lowercase_=0.0			,							lowercase_=0.0			,							lowercase_=True			,							lowercase_=True			,							lowercase_="gelu"			,							lowercase_=25_60			,							lowercase_=0.1			,							lowercase_=0.0			,							lowercase_=0.0			,							lowercase_=0.02			,							lowercase_=1			,							lowercase_=False			,							lowercase_=0			,							lowercase_=1			,							lowercase_=2			,							lowercase_=3			,							lowercase_=2			,							**lowercase_			,							):
     snake_case_			:     str        =    vocab_size
     snake_case_			:     str        =    max_position_embeddings
     snake_case_			:     Optional[Any]        =    d_model
     snake_case_			:     Any        =    encoder_ffn_dim
     snake_case_			:     Optional[Any]        =    encoder_layers
     snake_case_			:     List[Any]        =    encoder_attention_heads
     snake_case_			:     List[Any]        =    decoder_ffn_dim
     snake_case_			:     Optional[int]        =    decoder_layers
     snake_case_			:     Optional[int]        =    decoder_attention_heads
     snake_case_			:     Any        =    dropout
     snake_case_			:     List[str]        =    attention_dropout
     snake_case_			:     int        =    activation_dropout
     snake_case_			:     Dict        =    activation_function
     snake_case_			:     Any        =    init_std
     snake_case_			:     Union[str, Any]        =    encoder_layerdrop
     snake_case_			:     Optional[int]        =    decoder_layerdrop
     snake_case_			:     Any        =    use_cache
     snake_case_			:     List[str]        =    encoder_layers
     snake_case_			:     str        =    scale_embedding  # scale factor will be sqrt(d_model) if True

     super().__init__(
         pad_token_id=lowercase_			,							bos_token_id=lowercase_			,							eos_token_id=lowercase_			,							is_encoder_decoder=lowercase_			,							decoder_start_token_id=lowercase_			,							encoder_no_repeat_ngram_size=lowercase_			,							forced_eos_token_id=lowercase_			,							**lowercase_			,							)






class  UpperCAmelCase_					(						snake_case__       ):





 @property
 def 							snake_case__		(     self):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     int        =    OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ])
         if self.use_past:
             snake_case_			:     Any        =    {0: "batch"}
             snake_case_			:     Optional[int]        =    {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
             snake_case_			:     int        =    {0: "batch", 1: "decoder_sequence"}
             snake_case_			:     int        =    {0: "batch", 1: "decoder_sequence"}
         if self.use_past:
             self.fill_with_past_key_values_(lowercase_			,							direction="inputs")
     elif self.task == "causal-lm":
         snake_case_			:     Tuple        =    OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ])
         if self.use_past:
             snake_case_      ,			snake_case_			:     Any        =    self.num_layers
             for i in range(lowercase_):
                 snake_case_			:     Dict        =    {0: "batch", 2: "past_sequence + sequence"}
                 snake_case_			:     Dict        =    {0: "batch", 2: "past_sequence + sequence"}
     else:
         snake_case_			:     str        =    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
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 							snake_case__		(     self):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     Optional[int]        =    super().outputs
     else:
         snake_case_			:     Dict        =    super(lowercase_			,							self).outputs
         if self.use_past:
             snake_case_      ,			snake_case_			:     Any        =    self.num_layers
             for i in range(lowercase_):
                 snake_case_			:     str        =    {0: "batch", 2: "past_sequence + sequence"}
                 snake_case_			:     int        =    {0: "batch", 2: "past_sequence + sequence"}
     return common_outputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = None			,							):
     snake_case_			:     List[Any]        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)
     # Generate decoder inputs
     snake_case_			:     int        =    seq_length if not self.use_past else 1
     snake_case_			:     List[str]        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)
     snake_case_			:     str        =    {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
     snake_case_			:     Any        =    dict(**lowercase_			,							**lowercase_)

     if self.use_past:
         if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
         else:
             import torch
         snake_case_      ,			snake_case_			:     int        =    common_inputs["input_ids"].shape
         snake_case_			:     str        =    common_inputs["decoder_input_ids"].shape[1]
         snake_case_      ,			snake_case_			:     Tuple        =    self.num_attention_heads
         snake_case_			:     str        =    (
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         snake_case_			:     Dict        =    decoder_seq_length
         snake_case_			:     List[Any]        =    (
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )
         snake_case_			:     Optional[Any]        =    torch.cat(
             [common_inputs["decoder_attention_mask"], torch.ones(lowercase_			,							lowercase_)]			,							dim=1)
         snake_case_			:     Tuple        =    []
         snake_case_      ,			snake_case_			:     Tuple        =    self.num_layers

         for _ in range(lowercase_):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(lowercase_),
                     torch.zeros(lowercase_),
                     torch.zeros(lowercase_),
                     torch.zeros(lowercase_),
                 ))
     return common_inputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = None			,							):
     snake_case_			:     str        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)

     if self.use_past:
         if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
         else:
             import torch
         snake_case_      ,			snake_case_			:     Optional[Any]        =    common_inputs["input_ids"].shape
         snake_case_			:     List[str]        =    seqlen
         snake_case_      ,			snake_case_			:     Optional[int]        =    self.num_layers
         snake_case_      ,			snake_case_			:     Union[str, Any]        =    self.num_attention_heads
         snake_case_			:     Optional[int]        =    (
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         snake_case_			:     Dict        =    common_inputs["attention_mask"].dtype
         snake_case_			:     Optional[Any]        =    torch.cat(
             [common_inputs["attention_mask"], torch.ones(lowercase_			,							lowercase_			,							dtype=lowercase_)]			,							dim=1)
         snake_case_			:     Tuple        =    [
             (torch.zeros(lowercase_), torch.zeros(lowercase_)) for _ in range(lowercase_)
         ]
     return common_inputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = None			,							):
     # 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
     snake_case_			:     Any        =    compute_effective_axis_dimension(
         lowercase_			,							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
     snake_case_			:     Tuple        =    tokenizer.num_special_tokens_to_add(lowercase_)
     snake_case_			:     Tuple        =    compute_effective_axis_dimension(
         lowercase_			,							fixed_dimension=OnnxConfig.default_fixed_sequence			,							num_token_to_add=lowercase_)

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





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = None			,							):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     Optional[Any]        =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             lowercase_			,							batch_size=lowercase_			,							seq_length=lowercase_			,							is_pair=lowercase_			,							framework=lowercase_)

     elif self.task == "causal-lm":
         snake_case_			:     Dict        =    self._generate_dummy_inputs_for_causal_lm(
             lowercase_			,							batch_size=lowercase_			,							seq_length=lowercase_			,							is_pair=lowercase_			,							framework=lowercase_)
     else:
         snake_case_			:     Dict        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowercase_			,							batch_size=lowercase_			,							seq_length=lowercase_			,							is_pair=lowercase_			,							framework=lowercase_)

     return common_inputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     int        =    super()._flatten_past_key_values_(lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)
     else:
         snake_case_			:     List[Any]        =    super(lowercase_			,							self)._flatten_past_key_values_(
             lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_):
     if direction not in ["inputs", "outputs"]:
         raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given')

     snake_case_			:     Union[str, Any]        =    "past_key_values" if direction == "inputs" else "present"
     snake_case_      ,			snake_case_			:     Union[str, Any]        =    self.num_layers

     snake_case_			:     List[Any]        =    "past_encoder_sequence"
     snake_case_			:     str        =    "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

     for i in range(lowercase_):
         snake_case_			:     Union[str, Any]        =    {0: "batch", 2: decoder_sequence}
         snake_case_			:     str        =    {0: "batch", 2: decoder_sequence}
         snake_case_			:     Any        =    {0: "batch", 2: encoder_sequence}
         snake_case_			:     Dict        =    {0: "batch", 2: encoder_sequence}




