




'''simple docstring'''





from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


a		:  Union[str, Any]   			=			logging.get_logger(__name__)

a		:  str   			=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a		:  Optional[int]   			=			{
    '''vocab_file''': {
        '''openai/clip-vit-base-patch32''': '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''openai/clip-vit-base-patch32''': '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''openai/clip-vit-base-patch32''': (
            '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'''
        ),
    },
}

a		:  str   			=			{
    '''openai/clip-vit-base-patch32''': 7_7,
}




class     UpperCamelCase					(  snake_case__					):
  a    :Optional[Any]      					=				VOCAB_FILES_NAMES
  a    :Union[str, Any]      					=				PRETRAINED_VOCAB_FILES_MAP
  a    :int      					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a    :Any      					=				['input_ids', 'attention_mask']
  a    :Dict      					=				CLIPTokenizer



  def __init__( self		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase="<|endoftext|>"		,    __lowerCAmelCase="<|startoftext|>"		,    __lowerCAmelCase="<|endoftext|>"		,    __lowerCAmelCase="<|endoftext|>"		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      super().__init__(
          __lowerCAmelCase		,    __lowerCAmelCase		,    tokenizer_file=__lowerCAmelCase		,    unk_token=__lowerCAmelCase		,    bos_token=__lowerCAmelCase		,    eos_token=__lowerCAmelCase		,    pad_token=__lowerCAmelCase		,    **__lowerCAmelCase		,    )

      if not isinstance(self.backend_tokenizer.pre_tokenizer		,    pre_tokenizers.Sequence   ):
          raise ValueError(
              """The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"""
              """ heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"""
              """ to be compatible with this version.The easiest way to do so is"""
              """ `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"""
              """ to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"""
              """ transformers."""   )

      self._wrap_decode_method_backend_tokenizer()



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Tuple       					=			self.backend_tokenizer.decode

      def new_decode_method(*__lowerCAmelCase		,    **__lowerCAmelCase   ):
          _a							:       List[Any]       					=			orig_decode_method(*__lowerCAmelCase		,    **__lowerCAmelCase   )
          _a							:       Dict       					=			text.replace(self.backend_tokenizer.model.end_of_word_suffix		,    """ """   ).strip()
          return text

      _a							:       str       					=			new_decode_method



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      _a							:       Dict       					=			[self.bos_token_id]
      _a							:       Tuple       					=			[self.eos_token_id]

      if token_ids_a is None:
          return bos_token + token_ids_a + eos_token
      return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      _a							:       Dict       					=			[self.bos_token_id]
      _a							:       Optional[Any]       					=			[self.eos_token_id]

      if token_ids_a is None:
          return len(bos_token + token_ids_a + eos_token   ) * [0]
      return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token   ) * [0]



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      _a							:       Dict       					=			self._tokenizer.model.save(__lowerCAmelCase		,    name=__lowerCAmelCase   )
      return tuple(__lowerCAmelCase   )








'''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		:  List[str]   			=			logging.get_logger(__name__)

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




class     UpperCamelCase					(  snake_case__					):
  a    :List[Any]      					=				RetriBertConfig
  a    :int      					=				None
  a    :List[Any]      					=				'retribert'



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,    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(__lowerCAmelCase		,    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(__lowerCAmelCase		,    nn.LayerNorm   ):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0   )






a		:  Union[str, Any]   			=			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		,    __lowerCAmelCase   ):







      '''simple docstring'''


      super().__init__(__lowerCAmelCase   )
      _a							:       Tuple       					=			config.projection_dim

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

      _a							:       Tuple       					=			nn.CrossEntropyLoss(reduction="""mean"""   )

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



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
          return sent_encoder(__lowerCAmelCase		,    attention_mask=__lowerCAmelCase   )[1]
      else:
          # prepare implicit variables
          _a							:       Optional[int]       					=			input_ids.device
          _a							:       Optional[Any]       					=			input_ids.size()
          _a							:       str       					=			torch.zeros(__lowerCAmelCase		,    dtype=torch.long		,    device=__lowerCAmelCase   )
          _a							:       Optional[int]       					=			[None] * sent_encoder.config.num_hidden_layers
          _a							:       torch.Tensor       					=			sent_encoder.get_extended_attention_mask(
              __lowerCAmelCase		,    __lowerCAmelCase   )

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

          # run embedding layer on everything at once
          _a							:       Optional[Any]       					=			sent_encoder.embeddings(
              input_ids=__lowerCAmelCase		,    position_ids=__lowerCAmelCase		,    token_type_ids=__lowerCAmelCase		,    inputs_embeds=__lowerCAmelCase   )
          # run encoding and pooling on one mini-batch at a time
          _a							:       Optional[int]       					=			[]
          for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size   )   ):
              _a							:       Union[str, Any]       					=			embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
              _a							:       Any       					=			extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
              _a							:       Optional[int]       					=			checkpoint.checkpoint(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
              pooled_output_list.append(__lowerCAmelCase   )
          return torch.cat(__lowerCAmelCase		,    dim=0   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      _a							:       Any       					=			self.embed_sentences_checkpointed(
          __lowerCAmelCase		,    __lowerCAmelCase		,    self.bert_query		,    __lowerCAmelCase		,    )
      return self.project_query(__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      _a							:       List[Any]       					=			self.embed_sentences_checkpointed(
          __lowerCAmelCase		,    __lowerCAmelCase		,    self.bert_query if self.bert_doc is None else self.bert_doc		,    __lowerCAmelCase		,    )
      return self.project_doc(__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = -1		,    ):







      '''simple docstring'''


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


