



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_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  def 						_lowercase					(			self				:     Optional[Any]      ,     _a				:     Optional[int]      ,     _a				:     int=False       ) -> int:
    __lowerCamelCase	:	Union[str, Any]   	=			(
        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()
    __lowerCamelCase	:	str   	=			pa.Table.from_pydict({'filename': [Path(_a       ).name] * 100}       )
    if take == -1:
      __lowerCamelCase	:	List[str]   	=			len(_a       ) - skip
    if skip is not None and take is not None:
      __lowerCamelCase	:	List[str]   	=			pa_table.slice(_a      ,     _a       )
    return pa_table






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  def 						_lowercase					(			self				:     Any       ) -> Any:
    __lowerCamelCase	:	Optional[Any]   	=			'my_name'
    __lowerCamelCase	:	List[Any]   	=			SplitInfo(name='train'      ,     num_examples=100       )
    __lowerCamelCase	:	List[Any]   	=			SplitInfo(name='test'      ,     num_examples=100       )
    __lowerCamelCase	:	str   	=			[train_info, test_info]
    __lowerCamelCase	:	Tuple   	=			SplitDict()
    split_dict.add(_a       )
    split_dict.add(_a       )
    __lowerCamelCase	:	List[str]   	=			DatasetInfo(splits=_a       )

    with tempfile.TemporaryDirectory() as tmp_dir:
      __lowerCamelCase	:	Any   	=			ReaderTest(_a      ,     _a       )

      __lowerCamelCase	:	int   	=			'test[:33%]'
      __lowerCamelCase	:	Optional[int]   	=			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       )

      __lowerCamelCase	:	List[Any]   	=			['train', 'test[:33%]']
      __lowerCamelCase	:	str   	=			[Split.TRAIN, ReadInstruction.from_spec('test[:33%]'       )]
      for instructions in [instructionsa, instructionsa]:
        __lowerCamelCase	:	List[str]   	=			[reader.read(_a      ,     _a      ,     _a       ) for instr in instructions]
        __lowerCamelCase	:	Any   	=			(Dataset(**_a       ) 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      ,     _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 						_lowercase					(			self				:     Optional[Any]       ) -> Union[str, Any]:
    __lowerCamelCase	:	int   	=			'my_name'
    __lowerCamelCase	:	Any   	=			SplitInfo(name='train'      ,     num_examples=1000      ,     shard_lengths=[100] * 10       )
    __lowerCamelCase	:	Union[str, Any]   	=			[train_info]
    __lowerCamelCase	:	List[str]   	=			SplitDict()
    split_dict.add(_a       )
    __lowerCamelCase	:	Union[str, Any]   	=			DatasetInfo(splits=_a       )

    with tempfile.TemporaryDirectory() as tmp_dir:
      __lowerCamelCase	:	Optional[int]   	=			ReaderTest(_a      ,     _a       )

      __lowerCamelCase	:	Optional[int]   	=			'train[:33%]'
      __lowerCamelCase	:	str   	=			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      ,     330       )
      self.assertEqual(dset.num_columns      ,     1       )




  def 						_lowercase					(			self				:     str       ) -> int:
    __lowerCamelCase	:	Union[str, Any]   	=			SplitInfo(name='train'      ,     num_examples=100       )
    __lowerCamelCase	:	Union[str, Any]   	=			SplitInfo(name='test'      ,     num_examples=100       )
    __lowerCamelCase	:	Optional[Any]   	=			SplitDict()
    split_dict.add(_a       )
    split_dict.add(_a       )
    __lowerCamelCase	:	str   	=			DatasetInfo(splits=_a       )

    with tempfile.TemporaryDirectory() as tmp_dir:
      __lowerCamelCase	:	int   	=			ReaderTest(_a      ,     _a       )

      __lowerCamelCase	:	List[Any]   	=			[
          {'filename': os.path.join(_a      ,     'train'       )},
          {'filename': os.path.join(_a      ,     'test'       ), 'skip': 10, 'take': 10},
      ]
      __lowerCamelCase	:	Dict   	=			Dataset(**reader.read_files(_a      ,     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 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  __lowerCamelCase	:	List[Any]   	=			arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    __lowerCamelCase	:	Optional[int]   	=			ArrowReader.read_table(_lowerCAmelCase	,in_memory=_lowerCAmelCase	)
  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 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				Optional[int]:
  __lowerCamelCase	:	Optional[Any]   	=			arrow_file
  __lowerCamelCase	:	Union[str, Any]   	=			ArrowReader(''	,_lowerCAmelCase	)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    __lowerCamelCase	:	Union[str, Any]   	=			reader.read_files([{'filename': filename}]	,in_memory=_lowerCAmelCase	)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  __lowerCamelCase	:	Dict   	=			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 	a_					(							)				->				Dict:
  assert ReadInstruction('train'	,to=10	,unit='abs'	).to_spec() == "train[:10]"
  assert ReadInstruction('train'	,from_=-80	,to=10	,unit='%'	).to_spec() == "train[-80%:10%]"

  __lowerCamelCase	:	Union[str, Any]   	=			'train+test'
  assert ReadInstruction.from_spec(_lowerCAmelCase	).to_spec() == spec_train_test

  __lowerCamelCase	:	Optional[int]   	=			'train[2:10]'
  assert ReadInstruction.from_spec(_lowerCAmelCase	).to_spec() == spec_train_abs

  __lowerCamelCase	:	Union[str, Any]   	=			'train[15%:-20%]'
  assert ReadInstruction.from_spec(_lowerCAmelCase	).to_spec() == spec_train_pct

  __lowerCamelCase	:	Dict   	=			'train[:10%](closest)'
  assert ReadInstruction.from_spec(_lowerCAmelCase	).to_spec() == "train[:10%]"

  __lowerCamelCase	:	Optional[Any]   	=			'train[:10%](pct1_dropremainder)'
  assert ReadInstruction.from_spec(_lowerCAmelCase	).to_spec() == spec_train_pct_rounding

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

def 	a_					(							)				->				Union[str, Any]:
  __lowerCamelCase	:	Optional[Any]   	=			'dummy'
  __lowerCamelCase	:	Dict   	=			[SplitInfo(name='train'	,num_examples=100	)]
  __lowerCamelCase	:	str   	=			'train[:33%]'
  __lowerCamelCase	:	Optional[Any]   	=			'arrow'
  __lowerCamelCase	:	Union[str, Any]   	=			'prefix'

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

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


@pytest.mark.parametrize('name, expected_exception'	,[(None, TypeError), ('', ValueError)]	)
def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[str]:
  __lowerCamelCase	:	int   	=			[SplitInfo(name='train'	,num_examples=100	)]
  __lowerCamelCase	:	int   	=			'train'
  __lowerCamelCase	:	str   	=			'arrow'
  __lowerCamelCase	:	str   	=			'prefix_path'
  with pytest.raises(_lowerCAmelCase	):
    __lowerCamelCase	:	str   	=			make_file_instructions(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase							    =						{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['ImageGPTFeatureExtractor']
   _UpperCamelCase							    =						['ImageGPTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ImageGPTForCausalImageModeling',
       'ImageGPTForImageClassification',
       'ImageGPTModel',
       'ImageGPTPreTrainedModel',
       'load_tf_weights_in_imagegpt',
   ]


if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )

else:
   import sys

   _UpperCamelCase							    =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






