




'''simple docstring'''






from typing import TYPE_CHECKING

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


lowercase	:      List[str]				=       {
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase	:      str				=       ['''MarkupLMTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase	:      Optional[int]				=       [
     '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MarkupLMForQuestionAnswering''',
     '''MarkupLMForSequenceClassification''',
     '''MarkupLMForTokenClassification''',
     '''MarkupLMModel''',
     '''MarkupLMPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
 from .feature_extraction_markuplm import MarkupLMFeatureExtractor
 from .processing_markuplm import MarkupLMProcessor
 from .tokenization_markuplm import MarkupLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_markuplm_fast import MarkupLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_markuplm import (
      MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
      MarkupLMPreTrainedModel,
  )


else:
 import sys

 lowercase	:      List[str]				=       _LazyModule(__name__, globals()['''__file__'''], _import_structure)










'''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   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A=False ):






        '''simple docstring'''




        lowercase,     lowercase,     lowercase  :		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()
        lowercase  :		List[Any]						=      pa.Table.from_pydict({'''filename''': [Path(A ).name] * 100} )
        if take == -1:
               lowercase  :		int						=      len(A ) - skip
        if skip is not None and take is not None:
               lowercase  :		Optional[Any]						=      pa_table.slice(A   ,		A )
        return pa_table






class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      '''my_name'''
        lowercase  :		Tuple						=      SplitInfo(name='''train'''   ,		num_examples=100 )
        lowercase  :		Union[str, Any]						=      SplitInfo(name='''test'''   ,		num_examples=100 )
        lowercase  :		Any						=      [train_info, test_info]
        lowercase  :		Union[str, Any]						=      SplitDict()
        split_dict.add(A )
        split_dict.add(A )
        lowercase  :		List[str]						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		Union[str, Any]						=      ReaderTest(A   ,		A )

               lowercase  :		str						=      '''test[:33%]'''
               lowercase  :		List[Any]						=      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 )

               lowercase  :		Dict						=      ['''train''', '''test[:33%]''']
               lowercase  :		List[Any]						=      [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]''' )]
               for instructions in [instructionsa, instructionsa]:
                      lowercase  :		Union[str, Any]						=      [reader.read(A   ,		A   ,		A ) for instr in instructions]
                      lowercase,     lowercase  :		Tuple						=      (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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      '''my_name'''
        lowercase  :		Dict						=      SplitInfo(name='''train'''   ,		num_examples=1000   ,		shard_lengths=[100] * 10 )
        lowercase  :		Tuple						=      [train_info]
        lowercase  :		str						=      SplitDict()
        split_dict.add(A )
        lowercase  :		str						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		int						=      ReaderTest(A   ,		A )

               lowercase  :		Dict						=      '''train[:33%]'''
               lowercase  :		Tuple						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      SplitInfo(name='''train'''   ,		num_examples=100 )
        lowercase  :		Dict						=      SplitInfo(name='''test'''   ,		num_examples=100 )
        lowercase  :		List[str]						=      SplitDict()
        split_dict.add(A )
        split_dict.add(A )
        lowercase  :		List[Any]						=      DatasetInfo(splits=A )

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

               lowercase  :		Optional[int]						=      [
                   {'''filename''': os.path.join(A   ,		'''train''' )},
                   {'''filename''': os.path.join(A   ,		'''test''' ), '''skip''': 10, '''take''': 10},
               ]
               lowercase  :		int						=      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      )				->Union[str, Any]:






       '''simple docstring'''




       lowercase  :		Any						=      arrow_file
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              lowercase  :		str						=      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      )				->Dict:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      arrow_file
       lowercase  :		Union[str, Any]						=      ArrowReader(''''''   ,				lowerCamelCase      )
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              lowercase  :		List[str]						=      reader.read_files([{'''filename''': filename}]   ,				in_memory=lowerCamelCase      )
       assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
       lowercase  :		Optional[int]						=      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_( )				->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%]"

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

       lowercase  :		Any						=      '''train[2:10]'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_abs

       lowercase  :		List[str]						=      '''train[15%:-20%]'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_pct

       lowercase  :		Tuple						=      '''train[:10%](closest)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == "train[:10%]"

       lowercase  :		Any						=      '''train[:10%](pct1_dropremainder)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_pct_rounding

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




def 		a_( )				->str:






       '''simple docstring'''




       lowercase  :		int						=      '''dummy'''
       lowercase  :		int						=      [SplitInfo(name='''train'''   ,				num_examples=100      )]
       lowercase  :		Union[str, Any]						=      '''train[:33%]'''
       lowercase  :		str						=      '''arrow'''
       lowercase  :		int						=      '''prefix'''

       lowercase  :		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.arrow"""      ), "skip": 0, "take": 33}
       ]

       lowercase  :		List[str]						=      [SplitInfo(name='''train'''   ,				num_examples=100   ,				shard_lengths=[10] * 10      )]
       lowercase  :		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      )				->int:






       '''simple docstring'''




       lowercase  :		List[str]						=      [SplitInfo(name='''train'''   ,				num_examples=100      )]
       lowercase  :		List[Any]						=      '''train'''
       lowercase  :		Any						=      '''arrow'''
       lowercase  :		Union[str, Any]						=      '''prefix_path'''
       with pytest.raises(lowerCamelCase      ):
              lowercase  :		List[Any]						=      make_file_instructions(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )




