
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


SCREAMING_SNAKE_CASE   :		str					 =       logging.get_logger(__name__)

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






class 					__lowercase		(					A       ):
					__magic_name__						:							Any     	=	RetriBertConfig
					__magic_name__						:							Optional[Any]     	=	None
					__magic_name__						:							str     	=	'''retribert'''


					def        lowerCAmelCase_    (	self       ,       a__						)		->							int:





									'''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						)
SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       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.'''							,       A							,       )
class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							None:





									'''simple docstring'''
									super().__init__(a__						)
									A_       	=  config.projection_dim

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

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

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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__=-1       ,       )		->							int:





									'''simple docstring'''
									# 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
													A_       	=  input_ids.device
													A_       	=  input_ids.size()
													A_       	=  torch.zeros(a__       ,       dtype=torch.long       ,       device=a__						)
													A_       	=  [None] * sent_encoder.config.num_hidden_layers
													A_       	=  sent_encoder.get_extended_attention_mask(
													    a__       ,       a__						)

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

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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=None       ,       a__=-1       ,       )		->							int:





									'''simple docstring'''
									A_       	=  self.embed_sentences_checkpointed(
									    a__       ,       a__       ,       self.bert_query       ,       a__       ,       )
									return self.project_query(a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=None       ,       a__=-1       ,       )		->							Optional[Any]:





									'''simple docstring'''
									A_       	=  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       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__ = -1       ,       )		->							torch.FloatTensor:





									'''simple docstring'''
									A_       	=  input_ids_query.device
									A_       	=  self.embed_questions(a__       ,       a__       ,       a__						)
									A_       	=  self.embed_answers(a__       ,       a__       ,       a__						)
									A_       	=  torch.mm(a__       ,       a_reps.t()						)
									A_       	=  self.ce_loss(a__       ,       torch.arange(compare_scores.shape[1]						).to(a__						)						)
									A_       	=  self.ce_loss(compare_scores.t()       ,       torch.arange(compare_scores.shape[0]						).to(a__						)						)
									A_       	=  (loss_qa + loss_aq) / 2
									return loss

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


SCREAMING_SNAKE_CASE   :		Optional[int]					 =       logging.get_logger(__name__)






class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	['''pixel_values''']


					def __init__(	self       ,       a__ = True       ,       a__ = None       ,       a__ = True       ,       a__ = None       ,       a__ = PILImageResampling.BICUBIC       ,       a__ = True       ,       a__ = 1 / 2_5_5       ,       a__ = True       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							None:





									'''simple docstring'''
									super().__init__(**a__						)
									A_       	=  crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
									A_       	=  get_size_dict(a__						)

									A_       	=  do_center_crop
									A_       	=  crop_size
									A_       	=  do_resize
									A_       	=  size
									A_       	=  resample
									A_       	=  do_rescale
									A_       	=  rescale_factor
									A_       	=  do_normalize
									A_       	=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									A_       	=  image_std if image_std is not None else IMAGENET_DEFAULT_STD


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  self.size if size is None else size
									A_       	=  get_size_dict(a__						)
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)

									A_	,					A_       	=  get_image_size(a__						)
									A_       	=  min(a__       ,       a__						)
									A_       	=  (size['''height'''] / crop_size['''height''']) * min_dim
									A_       	=  (size['''width'''] / crop_size['''width''']) * min_dim
									return center_crop(a__       ,       size=(cropped_height, cropped_width)       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = PIL.Image.BICUBIC       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  get_size_dict(a__						)
									if "height" not in size or "width" not in size:
													raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"						)
									return resize(
									    a__       ,       size=(size['''height'''], size['''width'''])       ,       resample=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							Union[str, Any]:





									'''simple docstring'''
									return rescale(a__       ,       scale=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									return normalize(a__       ,       mean=a__       ,       std=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = ChannelDimension.FIRST       ,       **a__       ,       )		->							PIL.Image.Image:





									'''simple docstring'''
									A_       	=  do_center_crop if do_center_crop is not None else self.do_center_crop
									A_       	=  crop_size if crop_size is not None else self.crop_size
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  do_resize if do_resize is not None else self.do_resize
									A_       	=  size if size is not None else self.size
									A_       	=  get_size_dict(a__						)
									A_       	=  resample if resample is not None else self.resample
									A_       	=  do_rescale if do_rescale is not None else self.do_rescale
									A_       	=  rescale_factor if rescale_factor is not None else self.rescale_factor
									A_       	=  do_normalize if do_normalize is not None else self.do_normalize
									A_       	=  image_mean if image_mean is not None else self.image_mean
									A_       	=  image_std if image_std is not None else self.image_std

									A_       	=  make_list_of_images(a__						)

									if not valid_images(a__						):
													raise ValueError(
													    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
													    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

									if do_center_crop and crop_size is None:
													raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''						)

									if do_resize and size is None:
													raise ValueError('''Size must be specified if do_resize is True.'''						)

									if do_rescale and rescale_factor is None:
													raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.'''						)

									# All transformations expect numpy arrays.
									A_       	=  [to_numpy_array(a__						) for image in images]

									if do_center_crop:
													A_       	=  [self.center_crop(a__       ,       a__       ,       size=a__						) for image in images]

									if do_resize:
													A_       	=  [self.resize(image=a__       ,       size=a__       ,       resample=a__						) for image in images]

									if do_rescale:
													A_       	=  [self.rescale(image=a__       ,       scale=a__						) for image in images]

									if do_normalize:
													A_       	=  [self.normalize(image=a__       ,       mean=a__       ,       std=a__						) for image in images]

									A_       	=  [to_channel_dimension_format(a__       ,       a__						) for image in images]

									A_       	=  {'''pixel_values''': images}
									return BatchFeature(data=a__       ,       tensor_type=a__						)