


'''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 					lowerCAmelCase_     (    UpperCAmelCase  ):






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase=False      )->			Union[str, Any]:
            _A		,							_A		,							_A				=       (
                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(_UpperCamelCase      ) ,    'wb'      ).close()
            _A				=       pa.Table.from_pydict({'filename': [Path(_UpperCamelCase      ).name] * 100}      )
            if take == -1:
                 _A				=       len(_UpperCamelCase      ) - skip
            if skip is not None and take is not None:
                 _A				=       pa_table.slice(_UpperCamelCase ,    _UpperCamelCase      )
            return pa_table






class 					lowerCAmelCase_     (    UpperCAmelCase  ):






       def      UpperCamelCase	(   self      )->			List[Any]:
            _A				=       'my_name'
            _A				=       SplitInfo(name='train' ,    num_examples=100      )
            _A				=       SplitInfo(name='test' ,    num_examples=100      )
            _A				=       [train_info, test_info]
            _A				=       SplitDict()
            split_dict.add(_UpperCamelCase      )
            split_dict.add(_UpperCamelCase      )
            _A				=       DatasetInfo(splits=_UpperCamelCase      )

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _A				=       ReaderTest(_UpperCamelCase ,    _UpperCamelCase      )

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

                 _A				=       ['train', 'test[:33%]']
                 _A				=       [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'      )]
                 for instructions in [instructionsa, instructionsa]:
                      _A				=       [reader.read(_UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      ) for instr in instructions]
                      _A		,							_A				=       (Dataset(**_UpperCamelCase      ) for dataset_kwargs in datasets_kwargs)
                      self.assertEqual(train_dset['filename'][0] ,    F'{name}-train'      )
                      self.assertEqual(train_dset.num_rows ,    100      )
                      self.assertEqual(train_dset.num_columns ,    1      )
                      self.assertIsInstance(train_dset.split ,    _UpperCamelCase      )
                      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 ,    _UpperCamelCase      )
                      self.assertEqual(str(test_dset.split      ) ,    'test[:33%]'      )
                      del train_dset, test_dset






       def      UpperCamelCase	(   self      )->			Dict:
            _A				=       'my_name'
            _A				=       SplitInfo(name='train' ,    num_examples=1000 ,    shard_lengths=[100] * 10      )
            _A				=       [train_info]
            _A				=       SplitDict()
            split_dict.add(_UpperCamelCase      )
            _A				=       DatasetInfo(splits=_UpperCamelCase      )

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _A				=       ReaderTest(_UpperCamelCase ,    _UpperCamelCase      )

                 _A				=       'train[:33%]'
                 _A				=       Dataset(**reader.read(_UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )      )
                 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 ,    330      )
                 self.assertEqual(dset.num_columns ,    1      )






       def      UpperCamelCase	(   self      )->			Dict:
            _A				=       SplitInfo(name='train' ,    num_examples=100      )
            _A				=       SplitInfo(name='test' ,    num_examples=100      )
            _A				=       SplitDict()
            split_dict.add(_UpperCamelCase      )
            split_dict.add(_UpperCamelCase      )
            _A				=       DatasetInfo(splits=_UpperCamelCase      )

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _A				=       ReaderTest(_UpperCamelCase ,    _UpperCamelCase      )

                 _A				=       [
                     {'filename': os.path.join(_UpperCamelCase ,    'train'      )},
                     {'filename': os.path.join(_UpperCamelCase ,    'test'      ), 'skip': 10, 'take': 10},
                 ]
                 _A				=       Dataset(**reader.read_files(_UpperCamelCase ,    original_instructions='train+test[10:20]'      )      )
                 self.assertEqual(dset.num_rows ,    110      )
                 self.assertEqual(dset.num_columns ,    1      )
                 del dset






@pytest.mark.parametrize('in_memory'       ,						[False, True]		)
def 				lowerCamelCase_   (   __UpperCamelCase				:					Union[str, Any]       ,						__UpperCamelCase				:					List[Any]       ,						__UpperCamelCase				:					List[Any]		)				->  Any:







     """simple docstring"""
     _A				=       arrow_file
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          _A				=       ArrowReader.read_table(__UpperCamelCase       ,						in_memory=__UpperCamelCase		)
     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 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[Any]       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					Union[str, Any]		)				->  List[Any]:







     """simple docstring"""
     _A				=       arrow_file
     _A				=       ArrowReader(''       ,						__UpperCamelCase		)
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          _A				=       reader.read_files([{'filename': filename}]       ,						in_memory=__UpperCamelCase		)
     assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
     _A				=       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 				lowerCamelCase_   (   )				->  Any:







     """simple docstring"""
     assert ReadInstruction('train'       ,						to=1_0       ,						unit='abs'		).to_spec() == "train[:10]"
     assert ReadInstruction('train'       ,						from_=-8_0       ,						to=1_0       ,						unit='%'		).to_spec() == "train[-80%:10%]"

     _A				=       'train+test'
     assert ReadInstruction.from_spec(__UpperCamelCase		).to_spec() == spec_train_test

     _A				=       'train[2:10]'
     assert ReadInstruction.from_spec(__UpperCamelCase		).to_spec() == spec_train_abs

     _A				=       'train[15%:-20%]'
     assert ReadInstruction.from_spec(__UpperCamelCase		).to_spec() == spec_train_pct

     _A				=       'train[:10%](closest)'
     assert ReadInstruction.from_spec(__UpperCamelCase		).to_spec() == "train[:10%]"

     _A				=       'train[:10%](pct1_dropremainder)'
     assert ReadInstruction.from_spec(__UpperCamelCase		).to_spec() == spec_train_pct_rounding

     _A				=       'train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
     assert ReadInstruction.from_spec(__UpperCamelCase		).to_spec() == spec_train_test_pct_rounding

def 				lowerCamelCase_   (   )				->  Union[str, Any]:







     """simple docstring"""
     _A				=       'dummy'
     _A				=       [SplitInfo(name='train'       ,						num_examples=1_0_0		)]
     _A				=       'train[:33%]'
     _A				=       'arrow'
     _A				=       'prefix'

     _A				=       make_file_instructions(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
     assert isinstance(__UpperCamelCase       ,						__UpperCamelCase		)
     assert file_instructions.num_examples == 3_3
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(__UpperCamelCase       ,						F'{name}-train.arrow'		), "skip": 0, "take": 3_3}
     ]

     _A				=       [SplitInfo(name='train'       ,						num_examples=1_0_0       ,						shard_lengths=[1_0] * 1_0		)]
     _A				=       make_file_instructions(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
     assert isinstance(__UpperCamelCase       ,						__UpperCamelCase		)
     assert file_instructions.num_examples == 3_3
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(__UpperCamelCase       ,						F'{name}-train-00000-of-00010.arrow'		), "skip": 0, "take": -1},
         {"filename": os.path.join(__UpperCamelCase       ,						F'{name}-train-00001-of-00010.arrow'		), "skip": 0, "take": -1},
         {"filename": os.path.join(__UpperCamelCase       ,						F'{name}-train-00002-of-00010.arrow'		), "skip": 0, "take": -1},
         {"filename": os.path.join(__UpperCamelCase       ,						F'{name}-train-00003-of-00010.arrow'		), "skip": 0, "take": 3},
     ]

@pytest.mark.parametrize('name, expected_exception'       ,						[(None, TypeError), ('', ValueError)]		)
def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[int]       ,						__UpperCamelCase				:					Union[str, Any]		)				->  Dict:







     """simple docstring"""
     _A				=       [SplitInfo(name='train'       ,						num_examples=1_0_0		)]
     _A				=       'train'
     _A				=       'arrow'
     _A				=       'prefix_path'
     with pytest.raises(__UpperCamelCase		):
          _A				=       make_file_instructions(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)









'''simple docstring'''
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()

def 				lowerCamelCase_   (   __UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					str		)				->  List[str]:







     """simple docstring"""
     # Initialise PyTorch model
     _A				=       BertConfig.from_pretrained(
         'bert-large-cased'       ,						vocab_size=__UpperCamelCase       ,						max_position_embeddings=5_1_2       ,						is_decoder=__UpperCamelCase       ,						add_cross_attention=__UpperCamelCase       ,						)
     _A				=       bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     _A				=       BertGenerationConfig(**__UpperCamelCase		)
     if is_encoder:
          _A				=       BertGenerationEncoder(__UpperCamelCase		)
     else:
          _A				=       BertGenerationDecoder(__UpperCamelCase		)
     print(F'Building PyTorch model from configuration: {config}'		)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert_generation(
         __UpperCamelCase       ,						__UpperCamelCase       ,						model_class='bert'       ,						is_encoder_named_decoder=__UpperCamelCase       ,						is_encoder=__UpperCamelCase       ,						)

     # Save pytorch-model
     print(F'Save PyTorch model and config to {pytorch_dump_path}'		)
     model.save_pretrained(__UpperCamelCase		)


if __name__ == "__main__":
    lowerCAmelCase  =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--is_encoder_named_decoder""",
        action="""store_true""",
        help="""If decoder has to be renamed to encoder in PyTorch model.""",
    )
    parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
    parser.add_argument("""--vocab_size""", default=50_358, type=int, help="""Vocab size of model""")
    lowerCAmelCase  =				parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_hub_path,
        args.pytorch_dump_path,
        args.is_encoder_named_decoder,
        args.vocab_size,
        is_encoder=args.is_encoder,
    )





