



"""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


lowerCAmelCase__      =    logging.getLogger(__name__)






class 	__snake_case	(     _lowercase):




  def __init__(			self					:						Union[str, Any]						, __lowerCAmelCase					:						Optional[int]						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						int=None		):






         """simple docstring"""






         super().__init__(
             __lowerCAmelCase						, question_encoder_tokenizer=__lowerCAmelCase						, generator_tokenizer=__lowerCAmelCase						, index=__lowerCAmelCase						, init_retrieval=__lowerCAmelCase						, )
         _lowerCamelCase						:    Dict         =			None




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict						, __lowerCAmelCase					:						int		):






         """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
                _lowerCamelCase						:    List[str]         =			self._infer_socket_ifname()
                # avoid clash with the NCCL port
                _lowerCamelCase						:    Dict         =			str(distributed_port + 1		)
                _lowerCamelCase						:    str         =			dist.new_group(ranks=__lowerCAmelCase						, 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 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         return dist.get_rank(group=self.process_group		) == 0




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						Union[str, Any]						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Tuple=torch.floataa		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			torch.empty(__lowerCAmelCase						, dtype=__lowerCAmelCase		)
         dist.scatter(__lowerCAmelCase						, src=0						, scatter_list=__lowerCAmelCase						, group=self.process_group		)
         return target_tensor




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			psutil.net_if_addrs()
         # a hacky way to deal with varying network interface names
         _lowerCamelCase						:    str         =			next((addr for addr in addrs if addr.startswith('''e'''		))						, __lowerCAmelCase		)
         return ifname




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						np.ndarray						, __lowerCAmelCase					:						int		):






         """simple docstring"""






         if not dist.is_initialized():
                _lowerCamelCase						:    Any         =			self._main_retrieve(__lowerCAmelCase						, __lowerCAmelCase		)
                return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase		)

         # distributed training
         _lowerCamelCase						:    Dict         =			dist.get_world_size(group=self.process_group		)

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

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

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






"""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


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






def 	snake_case_  (				A_ :		str,    A_ :		str						):


       '''simple docstring'''





       if "xprophetnet" in prophetnet_checkpoint_path:
              _lowerCamelCase						:    Any         =			XLMProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    List[str]         =			XLMProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)
       else:
              _lowerCamelCase						:    str         =			ProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			ProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)

       _lowerCamelCase						:    Optional[Any]         =			['''key_proj''', '''value_proj''', '''query_proj''']

       _lowerCamelCase						:    List[Any]         =			{
           '''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"]:
              _lowerCamelCase						:    Union[str, Any]         =			key.split('''.'''						)

              if attributes[0] == "lm_head":
                     _lowerCamelCase						:    str         =			prophet
                     _lowerCamelCase						:    List[Any]         =			prophet_old
              else:
                     _lowerCamelCase						:    Optional[int]         =			prophet.prophetnet
                     _lowerCamelCase						:    Optional[Any]         =			prophet_old.model

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

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

                            elif attribute == "key_proj":
                                   _lowerCamelCase						:    int         =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
                            elif attribute == "value_proj":
                                   _lowerCamelCase						:    Tuple         =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
                            _lowerCamelCase						:    Dict         =			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_12, "We want 512 position_embeddings."
                            _lowerCamelCase						:    List[str]         =			nn.Parameter(old_model.embed_positions.weight[:5_12, :]						)
                            _lowerCamelCase						:    Optional[Any]         =			True
                            break

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

                            if old_attribute == "":
                                   _lowerCamelCase						:    str         =			old_model
                            else:
                                   if not hasattr(A_,    A_						):
                                          raise ValueError(F'''{old_model} does not have {old_attribute}'''						)
                                   _lowerCamelCase						:    Optional[int]         =			getattr(A_,    A_						)

              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(A_						)


if __name__ == "__main__":
   lowerCAmelCase__      =    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.'''
   )
   lowerCAmelCase__      =    parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


