


'''simple docstring'''







import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
class    lowercase_	(       a__  ):


    def   __a				(	self    ,   a    ,   a=False       ):
          UpperCamelCase__   ,      UpperCamelCase__   ,      UpperCamelCase__      =							(
              filename_skip_take["filename"],
              filename_skip_take["skip"] if "skip" in filename_skip_take else None,
              filename_skip_take["take"] if "take" in filename_skip_take else None,
          )
          open(os.path.join(a       )    ,   "wb"       ).close()
          UpperCamelCase__      =							pa.Table.from_pydict({"filename": [Path(a       ).name] * 1_00}       )
          if take == -1:
                UpperCamelCase__      =							len(a       ) - skip
          if skip is not None and take is not None:
                UpperCamelCase__      =							pa_table.slice(a    ,   a       )
          return pa_table

class    lowercase_	(       a__  ):


    def   __a				(	self       ):
          UpperCamelCase__      =							"my_name"
          UpperCamelCase__      =							SplitInfo(name="train"    ,   num_examples=1_00       )
          UpperCamelCase__      =							SplitInfo(name="test"    ,   num_examples=1_00       )
          UpperCamelCase__      =							[train_info, test_info]
          UpperCamelCase__      =							SplitDict()
          split_dict.add(a       )
          split_dict.add(a       )
          UpperCamelCase__      =							DatasetInfo(splits=a       )

          with tempfile.TemporaryDirectory() as tmp_dir:
                UpperCamelCase__      =							ReaderTest(a    ,   a       )

                UpperCamelCase__      =							"test[:33%]"
                UpperCamelCase__      =							Dataset(**reader.read(a    ,   a    ,   a       )       )
                self.assertEqual(dset["filename"][0]    ,   f'''{name}-test'''       )
                self.assertEqual(dset.num_rows    ,   33       )
                self.assertEqual(dset.num_columns    ,   1       )

                UpperCamelCase__      =							["train", "test[:33%]"]
                UpperCamelCase__      =							[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"       )]
                for instructions in [instructionsa, instructionsa]:
                      UpperCamelCase__      =							[reader.read(a    ,   a    ,   a       ) for instr in instructions]
                      UpperCamelCase__   ,      UpperCamelCase__      =							(Dataset(**a       ) for dataset_kwargs in datasets_kwargs)
                      self.assertEqual(train_dset["filename"][0]    ,   f'''{name}-train'''       )
                      self.assertEqual(train_dset.num_rows    ,   1_00       )
                      self.assertEqual(train_dset.num_columns    ,   1       )
                      self.assertIsInstance(train_dset.split    ,   a       )
                      self.assertEqual(str(train_dset.split       )    ,   "train"       )
                      self.assertEqual(test_dset["filename"][0]    ,   f'''{name}-test'''       )
                      self.assertEqual(test_dset.num_rows    ,   33       )
                      self.assertEqual(test_dset.num_columns    ,   1       )
                      self.assertIsInstance(test_dset.split    ,   a       )
                      self.assertEqual(str(test_dset.split       )    ,   "test[:33%]"       )
                      del train_dset, test_dset


    def   __a				(	self       ):
          UpperCamelCase__      =							"my_name"
          UpperCamelCase__      =							SplitInfo(name="train"    ,   num_examples=10_00    ,   shard_lengths=[1_00] * 10       )
          UpperCamelCase__      =							[train_info]
          UpperCamelCase__      =							SplitDict()
          split_dict.add(a       )
          UpperCamelCase__      =							DatasetInfo(splits=a       )

          with tempfile.TemporaryDirectory() as tmp_dir:
                UpperCamelCase__      =							ReaderTest(a    ,   a       )

                UpperCamelCase__      =							"train[:33%]"
                UpperCamelCase__      =							Dataset(**reader.read(a    ,   a    ,   a       )       )
                self.assertEqual(dset["filename"][0]    ,   f'''{name}-train-00000-of-00010'''       )
                self.assertEqual(dset["filename"][-1]    ,   f'''{name}-train-00003-of-00010'''       )
                self.assertEqual(dset.num_rows    ,   3_30       )
                self.assertEqual(dset.num_columns    ,   1       )


    def   __a				(	self       ):
          UpperCamelCase__      =							SplitInfo(name="train"    ,   num_examples=1_00       )
          UpperCamelCase__      =							SplitInfo(name="test"    ,   num_examples=1_00       )
          UpperCamelCase__      =							SplitDict()
          split_dict.add(a       )
          split_dict.add(a       )
          UpperCamelCase__      =							DatasetInfo(splits=a       )

          with tempfile.TemporaryDirectory() as tmp_dir:
                UpperCamelCase__      =							ReaderTest(a    ,   a       )

                UpperCamelCase__      =							[
                    {"filename": os.path.join(a    ,   "train"       )},
                    {"filename": os.path.join(a    ,   "test"       ), "skip": 10, "take": 10},
                ]
                UpperCamelCase__      =							Dataset(**reader.read_files(a    ,   original_instructions="train+test[10:20]"       )       )
                self.assertEqual(dset.num_rows    ,   1_10       )
                self.assertEqual(dset.num_columns    ,   1       )
                del dset







@pytest.mark.parametrize("in_memory" ,     [False, True]       )
def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							arrow_file
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            UpperCamelCase__      =							ArrowReader.read_table(__A ,     in_memory=__A       )
      assert table.shape == dataset.data.shape
      assert set(table.column_names       ) == set(dataset.data.column_names       )
      assert dict(table.to_pydict()       ) == dict(dataset.data.to_pydict()       )  # to_pydict returns OrderedDict







@pytest.mark.parametrize("in_memory" ,     [False, True]       )
def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[str]:


      '''simple docstring'''



      UpperCamelCase__      =							arrow_file
      UpperCamelCase__      =							ArrowReader("" ,     __A       )
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            UpperCamelCase__      =							reader.read_files([{"filename": filename}] ,     in_memory=__A       )
      assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
      UpperCamelCase__      =							dataset_kwargs["arrow_table"]
      assert table.shape == dataset.data.shape
      assert set(table.column_names       ) == set(dataset.data.column_names       )
      assert dict(table.to_pydict()       ) == dict(dataset.data.to_pydict()       )  # to_pydict returns OrderedDict







def _UpperCamelCase			(	)				->						Any:


      '''simple docstring'''



      assert ReadInstruction("train" ,     to=10 ,     unit="abs"       ).to_spec() == "train[:10]"
      assert ReadInstruction("train" ,     from_=-80 ,     to=10 ,     unit="%"       ).to_spec() == "train[-80%:10%]"

      UpperCamelCase__      =							"train+test"
      assert ReadInstruction.from_spec(__A       ).to_spec() == spec_train_test

      UpperCamelCase__      =							"train[2:10]"
      assert ReadInstruction.from_spec(__A       ).to_spec() == spec_train_abs

      UpperCamelCase__      =							"train[15%:-20%]"
      assert ReadInstruction.from_spec(__A       ).to_spec() == spec_train_pct

      UpperCamelCase__      =							"train[:10%](closest)"
      assert ReadInstruction.from_spec(__A       ).to_spec() == "train[:10%]"

      UpperCamelCase__      =							"train[:10%](pct1_dropremainder)"
      assert ReadInstruction.from_spec(__A       ).to_spec() == spec_train_pct_rounding

      UpperCamelCase__      =							"train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
      assert ReadInstruction.from_spec(__A       ).to_spec() == spec_train_test_pct_rounding







def _UpperCamelCase			(	)				->						Optional[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							"dummy"
      UpperCamelCase__      =							[SplitInfo(name="train" ,     num_examples=100       )]
      UpperCamelCase__      =							"train[:33%]"
      UpperCamelCase__      =							"arrow"
      UpperCamelCase__      =							"prefix"

      UpperCamelCase__      =							make_file_instructions(__A ,     __A ,     __A ,     __A ,     __A       )
      assert isinstance(__A ,     __A       )
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(__A ,     F'''{name}-train.arrow'''       ), "skip": 0, "take": 33}
      ]

      UpperCamelCase__      =							[SplitInfo(name="train" ,     num_examples=100 ,     shard_lengths=[10] * 10       )]
      UpperCamelCase__      =							make_file_instructions(__A ,     __A ,     __A ,     __A ,     __A       )
      assert isinstance(__A ,     __A       )
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(__A ,     F'''{name}-train-00000-of-00010.arrow'''       ), "skip": 0, "take": -1},
          {"filename": os.path.join(__A ,     F'''{name}-train-00001-of-00010.arrow'''       ), "skip": 0, "take": -1},
          {"filename": os.path.join(__A ,     F'''{name}-train-00002-of-00010.arrow'''       ), "skip": 0, "take": -1},
          {"filename": os.path.join(__A ,     F'''{name}-train-00003-of-00010.arrow'''       ), "skip": 0, "take": 3},
      ]







@pytest.mark.parametrize("name, expected_exception" ,     [(None, TypeError), ("", ValueError)]       )
def _UpperCamelCase			(	__A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							[SplitInfo(name="train" ,     num_examples=100       )]
      UpperCamelCase__      =							"train"
      UpperCamelCase__      =							"arrow"
      UpperCamelCase__      =							"prefix_path"
      with pytest.raises(__A       ):
            UpperCamelCase__      =							make_file_instructions(__A ,     __A ,     __A ,     __A ,     __A       )









'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(__A ,     __A ,     __A       )

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
      torch.save(model.state_dict() ,     __A ,     _use_new_zipfile_serialization=__A       )


if __name__ == "__main__":
  a__    :   str								=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--bert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  a__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





