






"""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		:	str      =		logging.get_logger(__name__)

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





class  _UpperCAmelCase	(			_A							):
					SCREAMING_SNAKE_CASE_		:    Optional[int]							   =       RetriBertConfig
					SCREAMING_SNAKE_CASE_		:    Dict							   =       None
					SCREAMING_SNAKE_CASE_		:    Tuple							   =       "retribert"




					def   A  (    self					:						List[str]   ,						A					:						List[Any]       )  ->  str:
							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       )
__A		:	Optional[int]      =		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."       ,						_A       ,						)
class  _UpperCAmelCase	(			_A							):




					def __init__(    self					:						List[str]   ,						A					:						RetriBertConfig       )  ->  None:
							super().__init__(A       )
							lowercase_			:			List[str]								=       config.projection_dim

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

							lowercase_			:			Dict								=       nn.CrossEntropyLoss(reduction='''mean'''       )

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




					def   A  (    self					:						List[str]   ,						A					:						Optional[Any]   ,						A					:						List[str]   ,						A					:						int   ,						A					:						Dict=-1   ,						)  ->  Optional[int]:
							# 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
									lowercase_			:			List[str]								=       input_ids.device
									lowercase_			:			Dict								=       input_ids.size()
									lowercase_			:			Any								=       torch.zeros(A   ,						dtype=torch.long   ,						device=A       )
									lowercase_			:			str								=       [None] * sent_encoder.config.num_hidden_layers
									lowercase_			:			torch.Tensor								=       sent_encoder.get_extended_attention_mask(
									    A   ,						A       )

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

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




					def   A  (    self					:						Tuple   ,						A					:						Union[str, Any]   ,						A					:						Any=None   ,						A					:						str=-1   ,						)  ->  List[str]:
							lowercase_			:			List[Any]								=       self.embed_sentences_checkpointed(
							    A   ,						A   ,						self.bert_query   ,						A   ,						)
							return self.project_query(A       )




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











"""simple docstring"""



import math
import os

import cva
import numpy as np

__A		:	int      =		255.0



def 						lowercase   (    __snake_case    :			float		, __snake_case    :			float     ):
		lowercase_			:			Any								=       np.mean((original - contrast) ** 2     )
		if mse == 0:
				return 1_0_0

		return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__snake_case     )     )



def 						lowercase   (    ):
		lowercase_			:			Any								=       os.path.dirname(os.path.realpath(__snake_case     )     )
		# Loading images (original image and compressed image)
		lowercase_			:			Union[str, Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/original_image.png'''     )     )
		lowercase_			:			List[Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/compressed_image.png'''     )		, 1     )

		lowercase_			:			Tuple								=       cva.imread(os.path.join(__snake_case		, '''image_data/PSNR-example-base.png'''     )     )
		lowercase_			:			List[str]								=       cva.imread(
		    os.path.join(__snake_case		, '''image_data/PSNR-example-comp-10.jpg'''     )		, 1     )

		# Value expected: 29.73dB
		print('''-- First Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )

		# # Value expected: 31.53dB (Wikipedia Example)
		print('''\n-- Second Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )


if __name__ == "__main__":
						main()



