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      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  def __init__(							self       :				Optional[Any]     ,   __magic_name__       :				Union[str, "sqlalchemy.sql.Selectable"]     ,   __magic_name__       :				Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]     ,   __magic_name__       :				Optional[Features] = None     ,   __magic_name__       :				str = None     ,   __magic_name__       :				bool = False     ,   **__magic_name__       :				Dict     ,   )     ->		List[Any]:
        super().__init__(features=__magic_name__     ,   cache_dir=__magic_name__     ,   keep_in_memory=__magic_name__     ,   **__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     Sql(
            cache_dir=__magic_name__     ,   features=__magic_name__     ,   sql=__magic_name__     ,   con=__magic_name__     ,   **__magic_name__     ,   )







  def       __A   (							self       :				Dict      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     None
        SCREAMING_SNAKE_CASE_      =     None
        SCREAMING_SNAKE_CASE_      =     None
        SCREAMING_SNAKE_CASE_      =     None

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

        # Build dataset for splits
        SCREAMING_SNAKE_CASE_      =     self.builder.as_dataset(
            split="train"     ,   verification_mode=__magic_name__     ,   in_memory=self.keep_in_memory      )
        return dataset



class      lowerCamelCase    :
  """simple docstring"""







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

        SCREAMING_SNAKE_CASE_      =     dataset
        SCREAMING_SNAKE_CASE_      =     name
        SCREAMING_SNAKE_CASE_      =     con
        SCREAMING_SNAKE_CASE_      =     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        SCREAMING_SNAKE_CASE_      =     num_proc
        SCREAMING_SNAKE_CASE_      =     to_sql_kwargs







  def       __A   (							self       :				Union[str, Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.to_sql_kwargs.pop("sql"     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.to_sql_kwargs.pop("con"     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.to_sql_kwargs.pop("index"     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     self._write(index=__magic_name__     ,   **self.to_sql_kwargs      )
        return written







  def       __A   (							self       :				str     ,   __magic_name__       :				int      )     ->		str:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     args
        SCREAMING_SNAKE_CASE_      =     {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
        SCREAMING_SNAKE_CASE_      =     query_table(
            table=self.dataset.data     ,   key=slice(__magic_name__     ,   offset + self.batch_size      )     ,   indices=self.dataset._indices     ,   )
        SCREAMING_SNAKE_CASE_      =     batch.to_pandas()
        SCREAMING_SNAKE_CASE_      =     df.to_sql(self.name     ,   self.con     ,   index=__magic_name__     ,   **__magic_name__      )
        return num_rows or len(__magic_name__      )







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				str     ,   **__magic_name__       :				Any      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     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:
              SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     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     ,   __magic_name__     ,   __magic_name__      )]     ,   )     ,   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



import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A   :     Optional[int]       =				logging.getLogger(__name__)


A   :     int       =				"Hello world! cécé herlolip"


A   :     List[Any]       =				namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     BertAbsConfig(
          temp_dir="."   ,					finetune_bert=__UpperCamelCase   ,					large=__UpperCamelCase   ,					share_emb=__UpperCamelCase   ,					use_bert_emb=__UpperCamelCase   ,					encoder="bert"   ,					max_pos=5_1_2   ,					enc_layers=6   ,					enc_hidden_size=5_1_2   ,					enc_heads=8   ,					enc_ff_size=5_1_2   ,					enc_dropout=0.2   ,					dec_layers=6   ,					dec_hidden_size=7_6_8   ,					dec_heads=8   ,					dec_ff_size=2_0_4_8   ,					dec_dropout=0.2   ,					)
      SCREAMING_SNAKE_CASE_      =     torch.load(__UpperCamelCase   ,					lambda __UpperCamelCase   ,					__UpperCamelCase : storage )
      SCREAMING_SNAKE_CASE_      =     AbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" )   ,					__UpperCamelCase )
      original.eval()

      SCREAMING_SNAKE_CASE_      =     BertAbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" ) )
      new_model.eval()

      # -------------------
      # Convert the weights
      # -------------------

      logging.info("convert the model" )
      new_model.bert.load_state_dict(original.bert.state_dict() )
      new_model.decoder.load_state_dict(original.decoder.state_dict() )
      new_model.generator.load_state_dict(original.generator.state_dict() )

      # ----------------------------------
      # Make sure the outpus are identical
      # ----------------------------------

      logging.info("Make sure that the models' outputs are identical" )
      SCREAMING_SNAKE_CASE_      =     BertTokenizer.from_pretrained("bert-base-uncased" )

      # prepare the model inputs
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample éàalj'-." )
      encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample 3 éàalj'-." )
      decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )

      # failsafe to make sure the weights reset does not affect the
      # loaded weights.
      assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0

      # forward pass
      SCREAMING_SNAKE_CASE_      =     encoder_input_ids
      SCREAMING_SNAKE_CASE_      =     decoder_input_ids
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     None

      # The original model does not apply the geneator layer immediatly but rather in
      # the beam search (where it combines softmax + linear layer). Since we already
      # apply the softmax in our generation process we only apply the linear layer here.
      # We make sure that the outputs of the full stack are identical
      SCREAMING_SNAKE_CASE_      =     original(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     original.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     new_model(
          __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     new_model.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
      print("Maximum absolute difference beween weights: {:.2f}".format(__UpperCamelCase ) )
      SCREAMING_SNAKE_CASE_      =     torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
      print("Maximum absolute difference beween weights: {:.2f}".format(__UpperCamelCase ) )

      SCREAMING_SNAKE_CASE_      =     torch.allclose(__UpperCamelCase   ,					__UpperCamelCase   ,					atol=1E-3 )
      if are_identical:
            logging.info("all weights are equal up to 1e-3" )
      else:
            raise ValueError("the weights are different. The new model is likely different from the original one." )

      # The model has been saved with torch.save(model) and this is bound to the exact
      # directory structure. We save the state_dict instead.
      logging.info("saving the model's state dictionary" )
      torch.save(
          new_model.state_dict()   ,					"./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin" )


if __name__ == "__main__":
      A   :     Any       =				argparse.ArgumentParser()
      parser.add_argument(
          "--bertabs_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.",
      )
      A   :     int       =				parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )


