"""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


__UpperCamelCase										=     logging.get_logger(__name__)

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




class 					UpperCamelCase   (							_lowerCAmelCase  ):
       SCREAMING_SNAKE_CASE_						      = RetriBertConfig
       SCREAMING_SNAKE_CASE_						      = None
       SCREAMING_SNAKE_CASE_						      = "retribert"







       def 						a_		(					self,				lowerCAmelCase__) -> int:



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


__UpperCamelCase										=     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."       ,						_lowerCAmelCase       ,						)
class 					UpperCamelCase   (							_lowerCAmelCase  ):







       def __init__(					self,				lowerCAmelCase__) -> None:
            super().__init__(__UpperCAmelCase)
            snake_case_   			=   config.projection_dim

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

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

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







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__=-1,				) -> Union[str, Any]:



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

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

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







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__=None,				lowerCAmelCase__=-1,				) -> Tuple:
            snake_case_   			=   self.embed_sentences_checkpointed(
                __UpperCAmelCase,				__UpperCAmelCase,				self.bert_query,				__UpperCAmelCase,				)
            return self.project_query(__UpperCAmelCase)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__=None,				lowerCAmelCase__=-1,				) -> Optional[int]:
            snake_case_   			=   self.embed_sentences_checkpointed(
                __UpperCAmelCase,				__UpperCAmelCase,				self.bert_query if self.bert_doc is None else self.bert_doc,				__UpperCAmelCase,				)
            return self.project_doc(__UpperCAmelCase)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__ = -1,				) -> torch.FloatTensor:
            snake_case_   			=   input_ids_query.device
            snake_case_   			=   self.embed_questions(__UpperCAmelCase,				__UpperCAmelCase,				__UpperCAmelCase)
            snake_case_   			=   self.embed_answers(__UpperCAmelCase,				__UpperCAmelCase,				__UpperCAmelCase)
            snake_case_   			=   torch.mm(__UpperCAmelCase,				a_reps.t())
            snake_case_   			=   self.ce_loss(__UpperCAmelCase,				torch.arange(compare_scores.shape[1]).to(__UpperCAmelCase))
            snake_case_   			=   self.ce_loss(compare_scores.t(),				torch.arange(compare_scores.shape[0]).to(__UpperCAmelCase))
            snake_case_   			=   (loss_qa + loss_aq) / 2
            return loss


"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase       :    int									=			logging.get_logger(__name__)

UpperCAmelCase       :    Optional[int]									=			["UtteranceLevel"]



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   List[str]  				=					torch.load(_lowercase							, map_location='''cpu'''						)
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

					lowerCamelCase__		:   List[Any]  				=					checkpoint['''Downstream''']

					lowerCamelCase__		:   int  				=					HubertConfig.from_pretrained(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					HubertForSequenceClassification.from_pretrained(_lowercase							, config=_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					WavaVecaFeatureExtractor.from_pretrained(
					    _lowercase							, return_attention_mask=_lowercase							, do_normalize=_lowercase						)

					if hf_congfig.use_weighted_layer_sum:
										lowerCamelCase__		:   Dict  				=					checkpoint['''Featurizer''']['''weights''']

					lowerCamelCase__		:   Any  				=					downstream_dict['''projector.weight''']
					lowerCamelCase__		:   Optional[int]  				=					downstream_dict['''projector.bias''']
					lowerCamelCase__		:   Dict  				=					downstream_dict['''model.post_net.linear.weight''']
					lowerCamelCase__		:   str  				=					downstream_dict['''model.post_net.linear.bias''']

					hf_feature_extractor.save_pretrained(_lowercase						)
					hf_model.save_pretrained(_lowercase						)


if __name__ == "__main__":
					UpperCAmelCase       :    int									=			argparse.ArgumentParser()
					parser.add_argument(
					    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
					)
					parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
					parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
					UpperCAmelCase       :    Dict									=			parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






