"""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


_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)

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






class      _SCREAMING_SNAKE_CASE							(			_SCREAMING_SNAKE_CASE				):



   '''simple docstring'''



   __UpperCAmelCase	:							Optional[Any]    =    RetriBertConfig
   __UpperCAmelCase	:							Optional[Any]    =    None
   __UpperCAmelCase	:							Dict    =    "retribert"







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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

_SCREAMING_SNAKE_CASE	:		Optional[int]   =   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."    ,				_SCREAMING_SNAKE_CASE    ,				)
class      _SCREAMING_SNAKE_CASE							(			_SCREAMING_SNAKE_CASE				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      super().__init__(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					config.projection_dim

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

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

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=-1 ,				):


      '''simple docstring'''

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

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

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=-1 ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.embed_sentences_checkpointed(
          _SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				self.bert_query ,				_SCREAMING_SNAKE_CASE ,				)
      return self.project_query(_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=-1 ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.embed_sentences_checkpointed(
          _SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				self.bert_query if self.bert_doc is None else self.bert_doc ,				_SCREAMING_SNAKE_CASE ,				)
      return self.project_doc(_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = -1 ,				):


      '''simple docstring'''

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












import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Any:

    """simple docstring"""


    if "model" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''model.'''       ,  ''''''   )
    if "norm1" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm1'''       ,  '''attention.output.LayerNorm'''   )
    if "norm2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm2'''       ,  '''output.LayerNorm'''   )
    if "norm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm'''       ,  '''LayerNorm'''   )
    if "transformer" in orig_key:
        __UpperCamelCase			=    orig_key.split('''.'''   )[0].split('''_'''   )[-1]
        __UpperCamelCase			=    orig_key.replace(F"transformer_{layer_num}"       ,  F"encoder.layer.{layer_num}"   )
    if "mha.attn" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha.attn'''       ,  '''attention.self'''   )
    if "mha" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha'''       ,  '''attention'''   )
    if "W_q" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_q'''       ,  '''self.query'''   )
    if "W_k" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_k'''       ,  '''self.key'''   )
    if "W_v" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_v'''       ,  '''self.value'''   )
    if "ff.0" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.0'''       ,  '''intermediate.dense'''   )
    if "ff.2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.2'''       ,  '''output.dense'''   )
    if "ff" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff'''       ,  '''output.dense'''   )
    if "mlm_class" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm.mlm_class'''       ,  '''cls.predictions.decoder'''   )
    if "mlm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm'''       ,  '''cls.predictions.transform'''   )
    if "backbone.backbone.encoders" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''backbone.backbone.encoders'''       ,  '''encoder.layer'''   )
    if "cls" not in orig_key:
        __UpperCamelCase			=    '''mra.''' + orig_key

    return orig_key




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase			=    orig_state_dict.pop(lowercase_   )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __UpperCamelCase			=    val

    __UpperCamelCase			=    orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase			=    torch.arange(lowercase_   ).expand((1, -1)   ) + 2

    return orig_state_dict




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''model_state_dict''']
    __UpperCamelCase			=    MraConfig.from_json_file(lowercase_   )
    __UpperCamelCase			=    MraForMaskedLM(lowercase_   )

    __UpperCamelCase			=    convert_checkpoint_helper(config.max_position_embeddings       ,  lowercase_   )

    print(model.load_state_dict(lowercase_   )   )
    model.eval()
    model.save_pretrained(lowercase_   )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Mra model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_ 		=  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






