


'''simple docstring'''






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


lowercase							=logging.get_logger(__name__)
logging.set_verbosity_info()





def lowerCamelCase__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							str							):







   '''simple docstring'''




   if "xprophetnet" in prophetnet_checkpoint_path:
      _UpperCAmelCase       :       Dict							   =XLMProphetNetForConditionalGenerationOld.from_pretrained(__lowerCamelCase							)
      _UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =XLMProphetNetForConditionalGeneration.from_pretrained(
          __lowerCamelCase	,   output_loading_info=__lowerCamelCase							)
   else:
      _UpperCAmelCase       :       Optional[Any]							   =ProphetNetForConditionalGenerationOld.from_pretrained(__lowerCamelCase							)
      _UpperCAmelCase   ,	_UpperCAmelCase       :       List[str]							   =ProphetNetForConditionalGeneration.from_pretrained(
          __lowerCamelCase	,   output_loading_info=__lowerCamelCase							)

   _UpperCAmelCase       :       int							   =['key_proj', 'value_proj', 'query_proj']

   _UpperCAmelCase       :       str							   ={
       'self_attn': 'ngram_self_attn',
       'cross_attn': 'encoder_attn',
       'cross_attn_layer_norm': 'encoder_attn_layer_norm',
       'feed_forward_layer_norm': 'final_layer_norm',
       'feed_forward': '',
       'intermediate': 'fc1',
       'output': 'fc2',
       'key_proj': 'k_proj',
       'query_proj': 'q_proj',
       'value_proj': 'v_proj',
       'word_embeddings': 'embed_tokens',
       'embeddings_layer_norm': 'emb_layer_norm',
       'relative_pos_embeddings': 'relative_linear',
       'ngram_embeddings': 'ngram_input_embed',
       'position_embeddings': 'embed_positions',
   }

   for key in loading_info["missing_keys"]:
      _UpperCAmelCase       :       List[Any]							   =key.split('.'							)

      if attributes[0] == "lm_head":
         _UpperCAmelCase       :       int							   =prophet
         _UpperCAmelCase       :       Optional[Any]							   =prophet_old
      else:
         _UpperCAmelCase       :       Dict							   =prophet.prophetnet
         _UpperCAmelCase       :       int							   =prophet_old.model

      _UpperCAmelCase       :       Optional[int]							   =False
      for attribute in attributes:
         if attribute in mapping:
            _UpperCAmelCase       :       Optional[int]							   =mapping[attribute]
            if not hasattr(__lowerCamelCase	,   __lowerCamelCase							) and len(__lowerCamelCase							) > 0:
               _UpperCAmelCase       :       int							   =attribute
         elif hasattr(__lowerCamelCase	,   __lowerCamelCase							):
            _UpperCAmelCase       :       Any							   =attribute

         if attribute == "weight":
            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
            _UpperCAmelCase       :       Optional[int]							   =old_model.weight
            logger.info(f"{attribute} is initialized."							)
            _UpperCAmelCase       :       Optional[int]							   =True
            break
         elif attribute == "bias":
            assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
            _UpperCAmelCase       :       Any							   =old_model.bias
            logger.info(f"{attribute} is initialized"							)
            _UpperCAmelCase       :       Any							   =True
            break
         elif attribute in special_keys and hasattr(__lowerCamelCase	,   'in_proj_weight'							):
            _UpperCAmelCase       :       str							   =old_model.in_proj_weight.shape[0] // 3
            _UpperCAmelCase       :       List[Any]							   =getattr(__lowerCamelCase	,   __lowerCamelCase							)
            param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
            param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
            if attribute == "query_proj":
               _UpperCAmelCase       :       Union[str, Any]							   =nn.Parameter(old_model.in_proj_weight[:embed_dim, :]							)
               _UpperCAmelCase       :       Union[str, Any]							   =nn.Parameter(old_model.in_proj_bias[:embed_dim]							)

            elif attribute == "key_proj":
               _UpperCAmelCase       :       Optional[Any]							   =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]							)
               _UpperCAmelCase       :       Tuple							   =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]							)
            elif attribute == "value_proj":
               _UpperCAmelCase       :       List[Any]							   =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]							)
               _UpperCAmelCase       :       Dict							   =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]							)
            _UpperCAmelCase       :       Union[str, Any]							   =True
            break
         elif attribute == "position_embeddings":
            assert (
                model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
            ), "Hidden size has to match"
            assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
            _UpperCAmelCase       :       Dict							   =nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]							)
            _UpperCAmelCase       :       Any							   =True
            break

         if attribute.isdigit():
            _UpperCAmelCase       :       Optional[Any]							   =model[int(__lowerCamelCase							)]
            _UpperCAmelCase       :       List[Any]							   =old_model[int(__lowerCamelCase							)]
         else:
            _UpperCAmelCase       :       Union[str, Any]							   =getattr(__lowerCamelCase	,   __lowerCamelCase							)

            if old_attribute == "":
               _UpperCAmelCase       :       List[str]							   =old_model
            else:
               if not hasattr(__lowerCamelCase	,   __lowerCamelCase							):
                  raise ValueError(f"{old_model} does not have {old_attribute}"							)
               _UpperCAmelCase       :       str							   =getattr(__lowerCamelCase	,   __lowerCamelCase							)

      if not is_key_init:
         raise ValueError(f"{key} was not correctly initialized!"							)

   print(f"Saving model to {pytorch_dump_folder_path}"							)
   prophet.save_pretrained(__lowerCamelCase							)


if __name__ == "__main__":
 lowercase							=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 lowercase							=parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''






import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


lowercase							=logging.getLogger(__name__)






class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case=None)						->  List[Any]:




          '''simple docstring'''
          super().__init__(
              snake_case  ,							question_encoder_tokenizer=snake_case  ,							generator_tokenizer=snake_case  ,							index=snake_case  ,							init_retrieval=snake_case  ,							)
          _UpperCAmelCase       :       Union[str, Any]							   =None






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[Any]:




          '''simple docstring'''
          logger.info('initializing retrieval')

          # initializing a separate process group for retrieval as the default
          # nccl backend doesn't support gather/scatter operations while gloo
          # is too slow to replace nccl for the core gpu communication
          if dist.is_initialized():
             logger.info('dist initialized')
             # needs to be set manually
             _UpperCAmelCase       :       Optional[Any]							   =self._infer_socket_ifname()
             # avoid clash with the NCCL port
             _UpperCAmelCase       :       Optional[int]							   =str(distributed_port + 1)
             _UpperCAmelCase       :       Any							   =dist.new_group(ranks=snake_case  ,							backend='gloo')

          # initialize retriever only on the main worker
          if not dist.is_initialized() or self._is_main():
             logger.info('dist not initialized / main')
             self.index.init_index()

          # all processes wait untill the retriever is initialized by the main process
          if dist.is_initialized():
             torch.distributed.barrier(group=self.process_group)






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          return dist.get_rank(group=self.process_group) == 0






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case=torch.floataa)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =torch.empty(snake_case  ,							dtype=snake_case)
          dist.scatter(snake_case  ,							src=0  ,							scatter_list=snake_case  ,							group=self.process_group)
          return target_tensor






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =psutil.net_if_addrs()
          # a hacky way to deal with varying network interface names
          _UpperCAmelCase       :       str							   =next((addr for addr in addrs if addr.startswith('e'))  ,							snake_case)
          return ifname






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  Tuple[np.ndarray, List[dict]]:




          '''simple docstring'''

          # single GPU training
          if not dist.is_initialized():
             _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[Any]							   =self._main_retrieve(snake_case  ,							snake_case)
             return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(snake_case)

          # distributed training
          _UpperCAmelCase       :       Optional[int]							   =dist.get_world_size(group=self.process_group)

          # gather logic
          _UpperCAmelCase       :       str							   =None
          if self._is_main():
             _UpperCAmelCase       :       Union[str, Any]							   =[torch.empty(question_hidden_states.shape  ,							dtype=torch.floataa) for _ in range(snake_case)]
          dist.gather(torch.tensor(snake_case)  ,							dst=0  ,							gather_list=snake_case  ,							group=self.process_group)

          # scatter logic
          _UpperCAmelCase       :       Optional[Any]							   =question_hidden_states.shape[0]
          _UpperCAmelCase       :       List[Any]							   =[]
          _UpperCAmelCase       :       Any							   =[]
          if self._is_main():
             assert len(snake_case) == world_size
             _UpperCAmelCase   ,	_UpperCAmelCase       :       Tuple							   =self._main_retrieve(torch.cat(snake_case).numpy()  ,							snake_case)
             _UpperCAmelCase   ,	_UpperCAmelCase       :       Union[str, Any]							   =torch.tensor(snake_case), torch.tensor(snake_case)
             _UpperCAmelCase       :       List[str]							   =self._chunk_tensor(snake_case  ,							snake_case)
             _UpperCAmelCase       :       Union[str, Any]							   =self._chunk_tensor(snake_case  ,							snake_case)
          _UpperCAmelCase       :       int							   =self._scattered(snake_case  ,							[n_queries, n_docs]  ,							target_type=torch.intaa)
          _UpperCAmelCase       :       Dict							   =self._scattered(snake_case  ,							[n_queries, n_docs, question_hidden_states.shape[1]])

          return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(snake_case)

