






import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      copy.deepcopy(lowerCamelCase_    )
				lowercase__       			=      sorted(lowerCamelCase_    )
				lowercase__       			=      [bisect.bisect_right(lowerCamelCase_		,       lowerCamelCase_    ) for y in x]
				return quantized






def   a      (						lowerCamelCase_		,       lowerCamelCase_=0    ):






				'''simple docstring'''
				lowercase__       			=      np.arange(start=3		,       stop=lowerCamelCase_		,       step=4    ).tolist() if k > 0 else [10]
				lowercase__       			=      _quantize(lowerCamelCase_		,       lowerCamelCase_    )
				# count number of elements per group
				lowercase__       			=      np.unique(lowerCamelCase_		,       return_counts=lowerCamelCase_    )[1]
				lowercase__       			=      [0] + bins + [np.inf]
				logger.info('''Using {} as bins for aspect lengths quantization'''.format(lowerCamelCase_    )    )
				logger.info('''Count of instances per bin: {}'''.format(lowerCamelCase_    )    )
				return groups




class 				_UpperCAmelCase (	A__     ):

				"""simple docstring"""




				def __init__(					self						:   str,  lowerCamelCase						:   Dict,  lowerCamelCase						:   Dict,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								if not isinstance(lowerCamelCase,  lowerCamelCase  ):
												raise ValueError(
												    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(lowerCamelCase  )  )
								lowercase__       			=      sampler
								lowercase__       			=      group_ids
								lowercase__       			=      batch_size




				def __iter__(					self						:   int  ):

								'''simple docstring'''
								lowercase__       			=      defaultdict(lowerCamelCase  )
								lowercase__       			=      defaultdict(lowerCamelCase  )

								lowercase__       			=      0
								for idx in self.sampler:
												lowercase__       			=      self.group_ids[idx]
												buffer_per_group[group_id].append(lowerCamelCase  )
												samples_per_group[group_id].append(lowerCamelCase  )
												if len(buffer_per_group[group_id]  ) == self.batch_size:
																yield buffer_per_group[group_id]  # TODO
																num_batches += 1
																del buffer_per_group[group_id]
												assert len(buffer_per_group[group_id]  ) < self.batch_size

								# now we have run out of elements that satisfy
								# the group criteria, let's return the remaining
								# elements so that the size of the sampler is
								# deterministic
								lowercase__       			=      len(self  )
								lowercase__       			=      expected_num_batches - num_batches
								if num_remaining > 0:
												# for the remaining batches, group the batches by similar lengths
												lowercase__       			=      []
												for group_id, idxs in sorted(buffer_per_group.items(),  key=lambda lowerCamelCase  : x[0]  ):
																batch_idx.extend(lowerCamelCase  )
																if len(lowerCamelCase  ) >= self.batch_size:
																				yield batch_idx[: self.batch_size]
																				lowercase__       			=      batch_idx[self.batch_size :]
																				num_remaining -= 1
												if len(lowerCamelCase  ) > 0:
																yield batch_idx
																num_remaining -= 1
								assert num_remaining == 0




				def __len__(					self						:   int  ):

								'''simple docstring'''
								return (len(self.sampler  ) + self.batch_size - 1) // self.batch_size












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   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      tmp_path / '''text.txt'''
				lowercase__       			=      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   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      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   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






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






@require_pil
def   a      (						lowerCamelCase_    ):






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






@pytest.mark.parametrize('''sample_by'''		,       ['''line''', '''paragraph''', '''document''']    )
def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






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




