"""simple docstring"""


import numpy as np


def   __UpperCAmelCase     (	_snake_case		:       Union[str, Any],				_snake_case		:       str,				_snake_case		:       Any       ):
   _lowercase							     =       n_draws * class_counts / class_counts.sum()
   # floored means we don't overshoot n_samples, but probably undershoot
   _lowercase							     =       np.floor(_snake_case       )
   # we add samples according to how much "left over" probability
   # they had, until we arrive at n_samples
   _lowercase							     =       int(n_draws - floored.sum()       )
   if need_to_add > 0:
      _lowercase							     =       continuous - floored
      _lowercase							     =       np.sort(np.unique(_snake_case       )       )[::-1]
      # add according to remainder, but break ties
      # randomly to avoid biases
      for value in values:
         (_lowercase       )							     =       np.where(remainder == value       )
         # if we need_to_add less than what's in inds
         # we draw randomly from them.
         # if we need to add more, we add them all and
         # go to the next value
         _lowercase							     =       min(len(_snake_case       ),				_snake_case       )
         _lowercase							     =       rng.choice(_snake_case,				size=_snake_case,				replace=_snake_case       )
         floored[inds] += 1
         need_to_add -= add_now
         if need_to_add == 0:
            break
   return floored.astype(np.intaa       )


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       List[Any],				_snake_case		:       Dict,				_snake_case		:       Dict,				_snake_case		:       Tuple=1_0       ):
   _lowercase							     =       np.unique(_snake_case,				return_inverse=_snake_case       )
   _lowercase							     =       classes.shape[0]
   _lowercase							     =       np.bincount(_snake_case       )
   if np.min(_snake_case       ) < 2:
      raise ValueError("Minimum class count error"       )
   if n_train < n_classes:
      raise ValueError(
          "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)       )
   if n_test < n_classes:
      raise ValueError(
          "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)       )
   _lowercase							     =       np.split(np.argsort(_snake_case,				kind="mergesort"       ),				np.cumsum(_snake_case       )[:-1]       )
   for _ in range(_snake_case       ):
      _lowercase							     =       approximate_mode(_snake_case,				_snake_case,				_snake_case       )
      _lowercase							     =       class_counts - n_i
      _lowercase							     =       approximate_mode(_snake_case,				_snake_case,				_snake_case       )

      _lowercase							     =       []
      _lowercase							     =       []

      for i in range(_snake_case       ):
         _lowercase							     =       rng.permutation(class_counts[i]       )
         _lowercase							     =       class_indices[i].take(_snake_case,				mode="clip"       )
         train.extend(perm_indices_class_i[: n_i[i]]       )
         test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]       )
      _lowercase							     =       rng.permutation(_snake_case       )
      _lowercase							     =       rng.permutation(_snake_case       )

      yield train, test






'''simple docstring'''






import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				_A :				int				=  tmp_path / '''text.txt'''
				_A :				int				=  textwrap.dedent(
				    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        ''')
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				str				=  tmp_path / '''text_with_image.txt'''
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.mark.parametrize('''keep_linebreaks'''   , [True, False])
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read().splitlines(keepends=lowerCAmelCase)
				_A :				Any				=  Text(keep_linebreaks=lowerCAmelCase   , encoding='''utf-8''')
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				Dict				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content





@require_pil
def      lowercase  (  lowerCAmelCase :						Dict):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Tuple				=  f.read().splitlines()[0]
				_A :				Optional[int]				=  Text(encoding='''utf-8'''   , features=Features({'''image''': Image()}))
				_A :				Any				=  text._generate_tables([[text_file_with_image]])
				_A :				str				=  pa.concat_tables([table for _, table in generator])
				assert pa_table.schema.field('''image''').type == Image()()
				_A :				List[str]				=  pa_table.to_pydict()['''image''']
				assert generated_content == [{"path": image_file, "bytes": None}]





@pytest.mark.parametrize('''sample_by'''   , ['''line''', '''paragraph''', '''document'''])
def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read()
				if sample_by == "line":
								_A :				str				=  expected_content.splitlines()
				elif sample_by == "paragraph":
								_A :				str				=  expected_content.split('''\n\n''')
				elif sample_by == "document":
								_A :				Tuple				=  [expected_content]
				_A :				str				=  Text(sample_by=lowerCAmelCase   , encoding='''utf-8'''   , chunksize=100)
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				int				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content
