



"""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


_UpperCamelCase: Tuple										=     logging.get_logger(__name__)
logging.set_verbosity_info()


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase )   ->	Optional[Any]:






      '''simple docstring'''


      if "xprophetnet" in prophetnet_checkpoint_path:
            lowercase     :					List[Any] 		=      XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase_ )
            lowercase     :					int 		=      XLMProphetNetForConditionalGeneration.from_pretrained(
                lowerCAmelCase_		,					output_loading_info=lowerCAmelCase_ )
      else:
            lowercase     :					Union[str, Any] 		=      ProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase_ )
            lowercase     :					Union[str, Any] 		=      ProphetNetForConditionalGeneration.from_pretrained(
                lowerCAmelCase_		,					output_loading_info=lowerCAmelCase_ )

      lowercase     :					Any 		=      ['key_proj', 'value_proj', 'query_proj']

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

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

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

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

                        elif attribute == "key_proj":
                              lowercase     :					str 		=      nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
                              lowercase     :					Optional[Any] 		=      nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
                        elif attribute == "value_proj":
                              lowercase     :					Optional[Any] 		=      nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
                              lowercase     :					Union[str, Any] 		=      nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
                        lowercase     :					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."
                        lowercase     :					Union[str, Any] 		=      nn.Parameter(old_model.embed_positions.weight[:5_12, :] )
                        lowercase     :					Optional[int] 		=      True
                        break

                  if attribute.isdigit():
                        lowercase     :					Tuple 		=      model[int(lowerCAmelCase_ )]
                        lowercase     :					Dict 		=      old_model[int(lowerCAmelCase_ )]
                  else:
                        lowercase     :					List[Any] 		=      getattr(lowerCAmelCase_		,					lowerCAmelCase_ )

                        if old_attribute == "":
                              lowercase     :					Optional[Any] 		=      old_model
                        else:
                              if not hasattr(lowerCAmelCase_		,					lowerCAmelCase_ ):
                                    raise ValueError(f'''{old_model} does not have {old_attribute}''' )
                              lowercase     :					List[Any] 		=      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__":
     _UpperCamelCase: Optional[int]										=     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.'
     )
     _UpperCamelCase: Any										=     parser.parse_args()
     convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''

import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
							import sqlitea

							import sqlalchemy

class 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Union[str, "sqlalchemy.sql.Selectable"]	,_UpperCAmelCase					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,_UpperCAmelCase					:       Optional[Features] = None	,_UpperCAmelCase					:       str = None	,_UpperCAmelCase					:       bool = False	,**_UpperCAmelCase					:       Dict	,):
									super().__init__(features=_UpperCAmelCase	,cache_dir=_UpperCAmelCase	,keep_in_memory=_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      Tuple        =							Sql(
									    cache_dir=_UpperCAmelCase	,features=_UpperCAmelCase	,sql=_UpperCAmelCase	,con=_UpperCAmelCase	,**_UpperCAmelCase	,)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Optional[Any]        =							None
									_a     :      Dict        =							None
									_a     :      Dict        =							None
									_a     :      Optional[int]        =							None

									self.builder.download_and_prepare(
									    download_config=_UpperCAmelCase	,download_mode=_UpperCAmelCase	,verification_mode=_UpperCAmelCase	,base_path=_UpperCAmelCase	,)

									# Build dataset for splits
									_a     :      List[str]        =							self.builder.as_dataset(
									    split='train'	,verification_mode=_UpperCAmelCase	,in_memory=self.keep_in_memory		)
									return dataset

class 	__magic_name__						:







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Dataset	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       Optional[int] = None	,**_UpperCAmelCase					:       Dict	,):
									if num_proc is not None and num_proc <= 0:
											raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""		)

									_a     :      Dict        =							dataset
									_a     :      List[Any]        =							name
									_a     :      Tuple        =							con
									_a     :      Union[str, Any]        =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_a     :      List[Any]        =							num_proc
									_a     :      Tuple        =							to_sql_kwargs







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Tuple        =							self.to_sql_kwargs.pop('sql'	,_UpperCAmelCase		)
									_a     :      str        =							self.to_sql_kwargs.pop('con'	,_UpperCAmelCase		)
									_a     :      Optional[Any]        =							self.to_sql_kwargs.pop('index'	,_UpperCAmelCase		)

									_a     :      Any        =							self._write(index=_UpperCAmelCase	,**self.to_sql_kwargs		)
									return written







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Dict		):
									_a       ,					_a       ,					_a     :      Any        =							args
									_a     :      Tuple        =							{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
									_a     :      Dict        =							query_table(
									    table=self.dataset.data	,key=slice(_UpperCAmelCase	,offset + self.batch_size		)	,indices=self.dataset._indices	,)
									_a     :      Tuple        =							batch.to_pandas()
									_a     :      Dict        =							df.to_sql(self.name	,self.con	,index=_UpperCAmelCase	,**_UpperCAmelCase		)
									return num_rows or len(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       List[Any]		):
									_a     :      Union[str, Any]        =							0

									if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0	,len(self.dataset		)	,self.batch_size		)	,unit='ba'	,disable=not logging.is_progress_bar_enabled()	,desc='Creating SQL from Arrow format'	,):
													written += self._batch_sql((offset, index, to_sql_kwargs)		)
									else:
											_a       ,					_a     :      List[Any]        =							len(self.dataset		), self.batch_size
											with multiprocessing.Pool(self.num_proc		) as pool:
													for num_rows in logging.tqdm(
													    pool.imap(
													        self._batch_sql	,[(offset, index, to_sql_kwargs) for offset in range(0	,_UpperCAmelCase	,_UpperCAmelCase		)]	,)	,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size	,unit='ba'	,disable=not logging.is_progress_bar_enabled()	,desc='Creating SQL from Arrow format'	,):
															written += num_rows

									return written




