
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


lowerCamelCase__        =						logging.get_logger(__name__)

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







class 	A__				(	__magic_name__      ):
				lowercase            =    RetriBertConfig
				lowercase            =    None
				lowercase            =    'retribert'






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : Any  ):






							'''simple docstring'''







							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  )






lowerCamelCase__        =						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.'		,  __magic_name__		,  )
class 	A__				(	__magic_name__      ):






				def __init__(		self   : str       ,	a   : RetriBertConfig  ):






							'''simple docstring'''







							super().__init__(a  )
							lowerCAmelCase__ : Optional[int]   =						config.projection_dim

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

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

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






				def 				_lowerCamelCase						(		self   : str       ,	a   : List[Any]       ,	a   : Dict       ,	a   : List[str]       ,	a   : Union[str, Any]=-1       ,	):






							'''simple docstring'''







							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__ : List[str]   =						input_ids.device
										lowerCAmelCase__ : List[str]   =						input_ids.size()
										lowerCAmelCase__ : Optional[int]   =						torch.zeros(a       ,	dtype=torch.long       ,	device=a  )
										lowerCAmelCase__ : Optional[Any]   =						[None] * sent_encoder.config.num_hidden_layers
										lowerCAmelCase__ : torch.Tensor   =						sent_encoder.get_extended_attention_mask(
										    a       ,	a  )

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

										# run embedding layer on everything at once
										lowerCAmelCase__ : int   =						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__ : List[str]   =						[]
										for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size  )  ):
													lowerCAmelCase__ : str   =						embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
													lowerCAmelCase__ : Union[str, Any]   =						extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
													lowerCAmelCase__ : Dict   =						checkpoint.checkpoint(a       ,	a       ,	a  )
													pooled_output_list.append(a  )
										return torch.cat(a       ,	dim=0  )






				def 				_lowerCamelCase						(		self   : Tuple       ,	a   : int       ,	a   : List[Any]=None       ,	a   : Any=-1       ,	):






							'''simple docstring'''







							lowerCAmelCase__ : Tuple   =						self.embed_sentences_checkpointed(
							    a       ,	a       ,	self.bert_query       ,	a       ,	)
							return self.project_query(a  )






				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : List[Any]       ,	a   : List[str]=None       ,	a   : List[Any]=-1       ,	):






							'''simple docstring'''







							lowerCAmelCase__ : List[str]   =						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 				_lowerCamelCase						(		self   : int       ,	a   : torch.LongTensor       ,	a   : Optional[torch.FloatTensor]       ,	a   : torch.LongTensor       ,	a   : Optional[torch.FloatTensor]       ,	a   : int = -1       ,	):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						input_ids_query.device
							lowerCAmelCase__ : List[str]   =						self.embed_questions(a       ,	a       ,	a  )
							lowerCAmelCase__ : Dict   =						self.embed_answers(a       ,	a       ,	a  )
							lowerCAmelCase__ : str   =						torch.mm(a       ,	a_reps.t()  )
							lowerCAmelCase__ : Optional[int]   =						self.ce_loss(a       ,	torch.arange(compare_scores.shape[1]  ).to(a  )  )
							lowerCAmelCase__ : str   =						self.ce_loss(compare_scores.t()       ,	torch.arange(compare_scores.shape[0]  ).to(a  )  )
							lowerCAmelCase__ : List[Any]   =						(loss_qa + loss_aq) / 2
							return loss

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowerCamelCase__        =						load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__        =						train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

lowerCamelCase__        =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase__        =						[[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase__        =						knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)