




'''simple docstring'''





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

a		:  Optional[int]   			=			{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "mask_emb": "masked_spec_embed",
}





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):







    for attribute in key.split("""."""	):
        _a							:       int       					=			getattr(_UpperCamelCase      ,  _UpperCamelCase	)

    if weight_type is not None:
        _a							:       int       					=			getattr(_UpperCamelCase      ,  _UpperCamelCase	).shape
    else:
        _a							:       int       					=			hf_pointer.shape

    assert hf_shape == value.shape, (
        F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
        F''' {value.shape} for {full_name}'''
    )

    if weight_type == "weight":
        _a							:       List[Any]       					=			value
    elif weight_type == "weight_g":
        _a							:       List[Any]       					=			value
    elif weight_type == "weight_v":
        _a							:       int       					=			value
    elif weight_type == "bias":
        _a							:       Any       					=			value
    else:
        _a							:       str       					=			value

    logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''	)





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       int       					=			[]
    _a							:       int       					=			fairseq_model.state_dict()

    _a							:       Union[str, Any]       					=			hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        _a							:       Optional[Any]       					=			False
        if "conv_layers" in name:
            load_conv_layer(
                _UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase      ,  hf_model.config.feat_extract_norm == """group"""      ,  )
            _a							:       Optional[Any]       					=			True
        else:
            for key, mapped_key in MAPPING.items():
                _a							:       Optional[int]       					=			mapped_key

                if key in name:
                    _a							:       Tuple       					=			True
                    if "*" in mapped_key:
                        _a							:       Tuple       					=			name.split(_UpperCamelCase	)[0].split("""."""	)[-2]
                        _a							:       Tuple       					=			mapped_key.replace("""*"""      ,  _UpperCamelCase	)
                    if "weight_g" in name:
                        _a							:       Tuple       					=			"""weight_g"""
                    elif "weight_v" in name:
                        _a							:       List[Any]       					=			"""weight_v"""
                    elif "weight" in name:
                        _a							:       str       					=			"""weight"""
                    elif "bias" in name:
                        _a							:       Optional[int]       					=			"""bias"""
                    else:
                        _a							:       List[str]       					=			None
                    set_recursively(_UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase	)
                continue
        if not is_used:
            unused_weights.append(_UpperCamelCase	)

    logger.warning(F'''Unused weights: {unused_weights}'''	)





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       str       					=			full_name.split("""conv_layers."""	)[-1]
    _a							:       Union[str, Any]       					=			name.split("""."""	)
    _a							:       str       					=			int(items[0]	)
    _a							:       Optional[Any]       					=			int(items[1]	)

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
            )
            _a							:       Dict       					=			value
            logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
            )
            _a							:       str       					=			value
            logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                " found."
            )
            _a							:       int       					=			value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
            )
            _a							:       int       					=			value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
    else:
        unused_weights.append(_UpperCamelCase	)





def UpperCAmelCase						(					UpperCAmelCase_	):
    _a							:       Tuple       					=			HubertConfig()
    _a							:       Optional[int]       					=			model.config

    _a							:       Optional[int]       					=			fs_config.activation_dropout
    _a							:       Any       					=			False
    _a							:       Optional[int]       					=			fs_config.attention_dropout
    _a							:       Optional[Any]       					=			False
    _a							:       Tuple       					=			eval(fs_config.extractor_conv_feature_layers	)
    _a							:       Any       					=			[x[0] for x in conv_layers]
    _a							:       List[Any]       					=			[x[1] for x in conv_layers]
    _a							:       Tuple       					=			[x[2] for x in conv_layers]
    _a							:       Optional[Any]       					=			"""gelu"""
    _a							:       List[Any]       					=			"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
    _a							:       Any       					=			False
    _a							:       List[str]       					=			0.0
    _a							:       int       					=			0.0
    _a							:       List[str]       					=			fs_config.activation_fn
    _a							:       Any       					=			fs_config.dropout
    _a							:       Union[str, Any]       					=			fs_config.encoder_embed_dim
    _a							:       List[str]       					=			0.02
    _a							:       str       					=			fs_config.encoder_ffn_embed_dim
    _a							:       Optional[int]       					=			1e-5
    _a							:       int       					=			0.0
    _a							:       Any       					=			fs_config.encoder_attention_heads
    _a							:       Optional[Any]       					=			fs_config.conv_pos_groups
    _a							:       Optional[int]       					=			fs_config.conv_pos
    _a							:       str       					=			len(_UpperCamelCase	)
    _a							:       Optional[int]       					=			fs_config.encoder_layers

    return config





@torch.no_grad()
def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=None	):
    _a							:       int       					=			distilhubert().model.model

    if config_path is not None:
        _a							:       List[Any]       					=			HubertConfig.from_pretrained(_UpperCamelCase	)
    else:
        _a							:       Any       					=			convert_config(_UpperCamelCase	)
    _a							:       Optional[int]       					=			model.eval()

    _a							:       Tuple       					=			WavaVecaFeatureExtractor(
        feature_size=1      ,  sampling_rate=16_000      ,  padding_value=0      ,  do_normalize=_UpperCamelCase      ,  return_attention_mask=_UpperCamelCase      ,  )
    _a							:       Dict       					=			HubertModel(_UpperCamelCase	)

    recursively_load_weights(_UpperCamelCase      ,  _UpperCamelCase	)

    feature_extractor.save_pretrained(_UpperCamelCase	)
    hf_model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
  a		:  Optional[int]   			=			argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  a		:  Dict   			=			parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)












'''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


