

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      _a				( UpperCamelCase__							):




      """simple docstring"""

      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							Dict	, UpperCAmelCase       :							List[str]=False  ):
            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(lowerCAmelCase__  )	, "wb"  ).close()
            A_	     =							pa.Table.from_pydict({"filename": [Path(lowerCAmelCase__  ).name] * 100}  )
            if take == -1:
                  A_	     =							len(lowerCAmelCase__  ) - skip
            if skip is not None and take is not None:
                  A_	     =							pa_table.slice(lowerCAmelCase__	, lowerCAmelCase__  )
            return pa_table




class      _a				( UpperCamelCase__							):




      """simple docstring"""

      def 		__A  (      self       :							List[str]  ):
            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(lowerCAmelCase__  )
            split_dict.add(lowerCAmelCase__  )
            A_	     =							DatasetInfo(splits=lowerCAmelCase__  )

            with tempfile.TemporaryDirectory() as tmp_dir:
                  A_	     =							ReaderTest(lowerCAmelCase__	, lowerCAmelCase__  )

                  A_	     =							"test[:33%]"
                  A_	     =							Dataset(**reader.read(lowerCAmelCase__	, lowerCAmelCase__	, lowerCAmelCase__  )  )
                  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(lowerCAmelCase__	, lowerCAmelCase__	, lowerCAmelCase__  ) for instr in instructions]
                        A_	     =							(Dataset(**lowerCAmelCase__  ) 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	, lowerCAmelCase__  )
                        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	, lowerCAmelCase__  )
                        self.assertEqual(str(test_dset.split  )	, "test[:33%]"  )
                        del train_dset, test_dset





      def 		__A  (      self       :							Optional[Any]  ):
            A_	     =							"my_name"
            A_	     =							SplitInfo(name="train"	, num_examples=1000	, shard_lengths=[100] * 10  )
            A_	     =							[train_info]
            A_	     =							SplitDict()
            split_dict.add(lowerCAmelCase__  )
            A_	     =							DatasetInfo(splits=lowerCAmelCase__  )

            with tempfile.TemporaryDirectory() as tmp_dir:
                  A_	     =							ReaderTest(lowerCAmelCase__	, lowerCAmelCase__  )

                  A_	     =							"train[:33%]"
                  A_	     =							Dataset(**reader.read(lowerCAmelCase__	, lowerCAmelCase__	, lowerCAmelCase__  )  )
                  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 		__A  (      self       :							Optional[int]  ):
            A_	     =							SplitInfo(name="train"	, num_examples=100  )
            A_	     =							SplitInfo(name="test"	, num_examples=100  )
            A_	     =							SplitDict()
            split_dict.add(lowerCAmelCase__  )
            split_dict.add(lowerCAmelCase__  )
            A_	     =							DatasetInfo(splits=lowerCAmelCase__  )

            with tempfile.TemporaryDirectory() as tmp_dir:
                  A_	     =							ReaderTest(lowerCAmelCase__	, lowerCAmelCase__  )

                  A_	     =							[
                      {"filename": os.path.join(lowerCAmelCase__	, "train"  )},
                      {"filename": os.path.join(lowerCAmelCase__	, "test"  ), "skip": 10, "take": 10},
                  ]
                  A_	     =							Dataset(**reader.read_files(lowerCAmelCase__	, 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 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   Optional[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 			__snake_case	(   __UpperCamelCase  :   Tuple							,__UpperCamelCase  :   List[Any]							,__UpperCamelCase  :   str						):



      """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 			__snake_case	(   ):



      """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%]"

      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 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							"dummy"
      A_	     =							[SplitInfo(name="train"							,num_examples=100						)]
      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 == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(__UpperCamelCase							,f'''{name}-train.arrow'''						), "skip": 0, "take": 33}
      ]

      A_	     =							[SplitInfo(name="train"							,num_examples=100							,shard_lengths=[10] * 10						)]
      A_	     =							make_file_instructions(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
      assert isinstance(__UpperCamelCase							,__UpperCamelCase						)
      assert file_instructions.num_examples == 33
      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 			__snake_case	(   __UpperCamelCase  :   Tuple							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

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








import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__a						:Dict            = (30, 487)
__a						:int            = (36, 1024)
__a						:Tuple            = (100, 100)
__a						:Dict            = 100

__a						:List[str]            = datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

__a      ,			__a						:str            = os.path.split(__file__)
__a						:Optional[Any]            = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      with ArrowWriter(features=__UpperCamelCase							,path=os.path.join(__UpperCamelCase							,"beta.arrow"						)						) as writer:
            for key, record in dummy_data:
                  A_	     =							my_features.encode_example(__UpperCamelCase						)
                  writer.write(__UpperCamelCase						)
            A_     ,    A_	     =							writer.finalize()

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   List[str]							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for col in feats:
            A_	     =							dataset[col]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for col in feats:
            A_	     =							dataset[col]

def 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							{}
      A_	     =							(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            A_	     =							datasets.Features({"image": ArrayaD(__UpperCamelCase							,dtype="float32"						)}						)
            A_	     =							generate_examples(features=__UpperCamelCase							,num_examples=__UpperCamelCase						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"						)						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": SPEED_TEST_SHAPE}						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Value("float32"						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}							,)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with open(__UpperCamelCase							,"wb"						) as f:
            f.write(json.dumps(__UpperCamelCase						).encode("utf-8"						)						)


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()