



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 :				Dict							=logging.get_logger(__name__)

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


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    RetriBertConfig
				lowercase__     =    None
				lowercase__     =    """retribert"""

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


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




UpperCAmelCase :				Tuple							=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    _lowercase (a_   ):
				'''simple docstring'''







				def __init__(			self  ,			snake_case__     ):

									'''simple docstring'''


									super().__init__(snake_case__     )
									UpperCamelCase_			= config.projection_dim

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

									UpperCamelCase_			= nn.CrossEntropyLoss(reduction="mean"     )

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

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__=-1  ,			):

									'''simple docstring'''


									if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
														return sent_encoder(snake_case__  ,			attention_mask=snake_case__     )[1]
									else:
														# prepare implicit variables
														UpperCamelCase_			= input_ids.device
														UpperCamelCase_			= input_ids.size()
														UpperCamelCase_			= torch.zeros(snake_case__  ,			dtype=torch.long  ,			device=snake_case__     )
														UpperCamelCase_			= [None] * sent_encoder.config.num_hidden_layers
														UpperCamelCase_			= sent_encoder.get_extended_attention_mask(
														    snake_case__  ,			snake_case__     )

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

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

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__=None  ,			snake_case__=-1  ,			):

									'''simple docstring'''


									UpperCamelCase_			= self.embed_sentences_checkpointed(
									    snake_case__  ,			snake_case__  ,			self.bert_query  ,			snake_case__  ,			)
									return self.project_query(snake_case__     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__=None  ,			snake_case__=-1  ,			):

									'''simple docstring'''


									UpperCamelCase_			= self.embed_sentences_checkpointed(
									    snake_case__  ,			snake_case__  ,			self.bert_query if self.bert_doc is None else self.bert_doc  ,			snake_case__  ,			)
									return self.project_doc(snake_case__     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__ = -1  ,			):

									'''simple docstring'''


									UpperCamelCase_			= input_ids_query.device
									UpperCamelCase_			= self.embed_questions(snake_case__  ,			snake_case__  ,			snake_case__     )
									UpperCamelCase_			= self.embed_answers(snake_case__  ,			snake_case__  ,			snake_case__     )
									UpperCamelCase_			= torch.mm(snake_case__  ,			a_reps.t()     )
									UpperCamelCase_			= self.ce_loss(snake_case__  ,			torch.arange(compare_scores.shape[1]     ).to(snake_case__     )     )
									UpperCamelCase_			= self.ce_loss(compare_scores.t()  ,			torch.arange(compare_scores.shape[0]     ).to(snake_case__     )     )
									UpperCamelCase_			= (loss_qa + loss_aq) / 2
									return loss



"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

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

SCREAMING_SNAKE_CASE_  :      Optional[int]							=      {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

SCREAMING_SNAKE_CASE_  :      Dict							=      {
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
}

SCREAMING_SNAKE_CASE_  :      Optional[Any]							=      {
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}



@lru_cache()
def 						UpperCAmelCase__ (					)   ->   Optional[int]:




   """simple docstring"""
   lowerCamelCase__						       =      (
       list(range(ord("!" )						,     ord("~" ) + 1 ) ) + list(range(ord("¡" )						,     ord("¬" ) + 1 ) ) + list(range(ord("®" )						,     ord("ÿ" ) + 1 ) )
   )
   lowerCamelCase__						       =      bs[:]
   lowerCamelCase__						       =      0
   for b in range(2**8 ):
      if b not in bs:
         bs.append(A__ )
         cs.append(2**8 + n )
         n += 1
   lowerCamelCase__						       =      [chr(A__ ) for n in cs]
   return dict(zip(A__						,     A__ ) )



def 						UpperCAmelCase__ (					A__ )   ->   int:




   """simple docstring"""
   lowerCamelCase__						       =      set()
   lowerCamelCase__						       =      word[0]
   for char in word[1:]:
      pairs.add((prev_char, char) )
      lowerCamelCase__						       =      char
   return pairs


class 		_A							(      __a       ):
      __a             =						VOCAB_FILES_NAMES
      __a             =						PRETRAINED_VOCAB_FILES_MAP
      __a             =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a             =						['input_ids', 'attention_mask']





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__="replace"     ,    SCREAMING_SNAKE_CASE__="<|endoftext|>"     ,    SCREAMING_SNAKE_CASE__="<|endoftext|>"     ,    SCREAMING_SNAKE_CASE__="<|endoftext|>"     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=False     ,    SCREAMING_SNAKE_CASE__=False     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       List[str]:
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else bos_token
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else eos_token
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else unk_token
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else pad_token
         super().__init__(
             errors=SCREAMING_SNAKE_CASE__     ,    unk_token=SCREAMING_SNAKE_CASE__     ,    bos_token=SCREAMING_SNAKE_CASE__     ,    eos_token=SCREAMING_SNAKE_CASE__     ,    pad_token=SCREAMING_SNAKE_CASE__     ,    add_prefix_space=SCREAMING_SNAKE_CASE__     ,    add_bos_token=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__     ,    )
         lowerCamelCase__						       =      add_bos_token

         with open(SCREAMING_SNAKE_CASE__     ,    encoding="utf-8"		) as vocab_handle:
            lowerCamelCase__						       =      json.load(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      {v: k for k, v in self.encoder.items()}
         lowerCamelCase__						       =      errors  # how to handle errors in decoding
         lowerCamelCase__						       =      bytes_to_unicode()
         lowerCamelCase__						       =      {v: k for k, v in self.byte_encoder.items()}
         with open(SCREAMING_SNAKE_CASE__     ,    encoding="utf-8"		) as merges_handle:
            lowerCamelCase__						       =      merges_handle.read().split("\n"		)[1:-1]
         lowerCamelCase__						       =      [tuple(merge.split()		) for merge in bpe_merges]
         lowerCamelCase__						       =      dict(zip(SCREAMING_SNAKE_CASE__     ,    range(len(SCREAMING_SNAKE_CASE__		)		)		)		)
         lowerCamelCase__						       =      {}
         lowerCamelCase__						       =      add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         lowerCamelCase__						       =      re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"		)





      @property
      def        _lowerCamelCase	(	self		) ->       Optional[int]:
         return len(self.encoder		)





      def        _lowerCamelCase	(	self		) ->       str:
         return dict(self.encoder     ,    **self.added_tokens_encoder		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Any:
         if token in self.cache:
            return self.cache[token]
         lowerCamelCase__						       =      tuple(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      get_pairs(SCREAMING_SNAKE_CASE__		)

         if not pairs:
            return token

         while True:
            lowerCamelCase__						       =      min(SCREAMING_SNAKE_CASE__     ,    key=lambda SCREAMING_SNAKE_CASE__		: self.bpe_ranks.get(SCREAMING_SNAKE_CASE__     ,    float("inf"		)		)		)
            if bigram not in self.bpe_ranks:
               break
            lowerCamelCase__       ,							lowerCamelCase__						       =      bigram
            lowerCamelCase__						       =      []
            lowerCamelCase__						       =      0
            while i < len(SCREAMING_SNAKE_CASE__		):
               try:
                  lowerCamelCase__						       =      word.index(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
               except ValueError:
                  new_word.extend(word[i:]		)
                  break
               else:
                  new_word.extend(word[i:j]		)
                  lowerCamelCase__						       =      j

               if word[i] == first and i < len(SCREAMING_SNAKE_CASE__		) - 1 and word[i + 1] == second:
                  new_word.append(first + second		)
                  i += 2
               else:
                  new_word.append(word[i]		)
                  i += 1
            lowerCamelCase__						       =      tuple(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						       =      new_word
            if len(SCREAMING_SNAKE_CASE__		) == 1:
               break
            else:
               lowerCamelCase__						       =      get_pairs(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      " ".join(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      word
         return word





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=None		) ->       Optional[int]:
         if self.add_bos_token:
            lowerCamelCase__						       =      [self.bos_token_id]
         else:
            lowerCamelCase__						       =      []

         lowerCamelCase__						       =      bos_token_ids + token_ids_a

         if token_ids_a is None:
            return output

         return output + bos_token_ids + token_ids_a





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = False		) ->       List[int]:
         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=SCREAMING_SNAKE_CASE__     ,    token_ids_a=SCREAMING_SNAKE_CASE__     ,    already_has_special_tokens=SCREAMING_SNAKE_CASE__		)

         if not self.add_bos_token:
            return super().get_special_tokens_mask(
                token_ids_a=SCREAMING_SNAKE_CASE__     ,    token_ids_a=SCREAMING_SNAKE_CASE__     ,    already_has_special_tokens=SCREAMING_SNAKE_CASE__		)

         if token_ids_a is None:
            return [1] + ([0] * len(SCREAMING_SNAKE_CASE__		))
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE__		)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__		))





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         lowerCamelCase__						       =      []
         for token in re.findall(self.pat     ,    SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						       =      "".join(
                self.byte_encoder[b] for b in token.encode("utf-8"		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE__		).split(" "		)		)
         return bpe_tokens





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         return self.encoder.get(SCREAMING_SNAKE_CASE__     ,    self.encoder.get(self.unk_token		)		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Any:
         return self.decoder.get(SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       str:
         lowerCamelCase__						       =      "".join(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"     ,    errors=self.errors		)
         return text





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE__		):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
            return
         lowerCamelCase__						       =      os.path.join(
             SCREAMING_SNAKE_CASE__     ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
         lowerCamelCase__						       =      os.path.join(
             SCREAMING_SNAKE_CASE__     ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

         with open(SCREAMING_SNAKE_CASE__     ,    "w"     ,    encoding="utf-8"		) as f:
            f.write(json.dumps(self.encoder     ,    indent=2     ,    sort_keys=SCREAMING_SNAKE_CASE__     ,    ensure_ascii=SCREAMING_SNAKE_CASE__		) + "\n"		)

         lowerCamelCase__						       =      0
         with open(SCREAMING_SNAKE_CASE__     ,    "w"     ,    encoding="utf-8"		) as writer:
            writer.write("#version: 0.2\n"		)
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,    key=lambda SCREAMING_SNAKE_CASE__		: kv[1]		):
               if index != token_index:
                  logger.warning(
                      f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                      " Please check that the tokenizer is not corrupted!"		)
                  lowerCamelCase__						       =      token_index
               writer.write(" ".join(SCREAMING_SNAKE_CASE__		) + "\n"		)
               index += 1

         return vocab_file, merge_file





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=False     ,    **SCREAMING_SNAKE_CASE__		) ->       int:
         lowerCamelCase__						       =      kwargs.pop("add_prefix_space"     ,    self.add_prefix_space		)
         if is_split_into_words or add_prefix_space:
            lowerCamelCase__						       =      " " + text
         return (text, kwargs)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       List[int]:
         lowerCamelCase__						       =      []
         for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(SCREAMING_SNAKE_CASE__     ,    add_special_tokens=SCREAMING_SNAKE_CASE__		) + [self.eos_token_id]		)
         if len(SCREAMING_SNAKE_CASE__		) > self.model_max_length:
            lowerCamelCase__						       =      input_ids[-self.model_max_length :]
         return input_ids




