



"""simple docstring"""




import bisect
import copy
from collections import defaultdict

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

from utils import logger


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			int:






      '''simple docstring'''


      lowercase  :      Tuple					=   copy.deepcopy(_UpperCAmelCase				)
      lowercase  :      Dict					=   sorted(_UpperCAmelCase				)
      lowercase  :      Dict					=   [bisect.bisect_right(_UpperCAmelCase	,      _UpperCAmelCase				) for y in x]
      return quantized


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase=0				)     ->			List[Any]:






      '''simple docstring'''


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





class   a__		(						SCREAMING_SNAKE_CASE__     ):


   def __init__(							self      :							Any,     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Tuple			)    ->       Optional[int]:
         if not isinstance(lowerCAmelCase,     lowerCAmelCase			):
               raise ValueError(
                   'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowerCAmelCase			)			)
         lowercase  :      str					=   sampler
         lowercase  :      Any					=   group_ids
         lowercase  :      List[Any]					=   batch_size


   def __iter__(							self      :							int			)    ->       Dict:
         lowercase  :      Tuple					=   defaultdict(lowerCAmelCase			)
         lowercase  :      Any					=   defaultdict(lowerCAmelCase			)

         lowercase  :      int					=   0
         for idx in self.sampler:
               lowercase  :      Optional[Any]					=   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  :      Dict					=   len(self			)
         lowercase  :      Optional[Any]					=   expected_num_batches - num_batches
         if num_remaining > 0:
               # for the remaining batches, group the batches by similar lengths
               lowercase  :      Optional[int]					=   []
               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  :      Optional[int]					=   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      :							str			)    ->       List[Any]:
         return (len(self.sampler			) + self.batch_size - 1) // self.batch_size





"""simple docstring"""




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


      lowercase  :      List[str]					=   min(re.search('^ +'	,      _UpperCAmelCase				).end() if t.startswith(' '				) else 0 for t in string.splitlines()				)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_UpperCAmelCase				)]				)


_UpperCamelCase:				Any    							=  '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

_UpperCamelCase:				Dict    							=  '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


_UpperCamelCase:				Dict    							=  '\\n# Begin of markdown\n\nSome cool dataset card\n'





class   a__		(						unittest.TestCase     ):


   def        lowercase				(							self      :							int			)    ->       Dict:
         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      Tuple					=   Path(lowerCAmelCase			) / 'README.md'
               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      List[str]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      str					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      Optional[Any]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertEqual(lowerCAmelCase,     {}			)






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      Any					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '			)
         with self.assertRaises(lowerCAmelCase			):
               DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      int					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)
