import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def       __magic_name__(  __UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      def wrapper(*__UpperCAmelCase ,						**__UpperCAmelCase		):
            _lowerCamelCase			    =				timeit.default_timer()
            _lowerCamelCase			    =				func(*__UpperCAmelCase ,						**__UpperCAmelCase		)
            _lowerCamelCase			    =				timeit.default_timer() - starttime
            return delta

      _lowerCamelCase			    =				func.__name__

      return wrapper
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=100 ,						__UpperCAmelCase=None		)						->    Union[str, Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      _lowerCamelCase			    =				seq_shapes or {}
      for i in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				{}
            for col_id, (k, v) in enumerate(features.items()		):
                  if isinstance(__UpperCAmelCase ,						_ArrayXD		):
                        _lowerCamelCase			    =				np.random.rand(*v.shape		).astype(v.dtype		)
                  elif isinstance(__UpperCAmelCase ,						datasets.Value		):
                        if v.dtype == "string":
                              _lowerCamelCase			    =				'''The small grey turtle was surprisingly fast when challenged.'''
                        else:
                              _lowerCamelCase			    =				np.random.randint(10 ,						size=1		).astype(v.dtype		).item()
                  elif isinstance(__UpperCAmelCase ,						datasets.Sequence		):
                        while isinstance(__UpperCAmelCase ,						datasets.Sequence		):
                              _lowerCamelCase			    =				v.feature
                        _lowerCamelCase			    =				seq_shapes[k]
                        _lowerCamelCase			    =				np.random.rand(*__UpperCAmelCase		).astype(v.dtype		)
                  _lowerCamelCase			    =				data

            dummy_data.append((i, example)		)

      return dummy_data




def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=100 ,						__UpperCAmelCase=None		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				generate_examples(__UpperCAmelCase ,						num_examples=__UpperCAmelCase ,						seq_shapes=__UpperCAmelCase		)

      with ArrowWriter(features=__UpperCAmelCase ,						path=__UpperCAmelCase		) as writer:
            for key, record in dummy_data:
                  _lowerCamelCase			    =				features.encode_example(__UpperCAmelCase		)
                  writer.write(__UpperCAmelCase		)

            _lowerCamelCase    ,					_lowerCamelCase			    =				writer.finalize()

      if not num_final_examples == num_examples:
            raise ValueError(
                F'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'		)

      _lowerCamelCase			    =				datasets.Dataset.from_file(filename=__UpperCAmelCase ,						info=datasets.DatasetInfo(features=__UpperCAmelCase		)		)

      return dataset
from typing import List

import numpy as np
def       __magic_name__(  __UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				{key: len(__UpperCAmelCase		) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)}
      if len(set(lists_lengths.values()		)		) > 1:
            raise RuntimeError(
                (
                    '''Sharding is ambiguous for this dataset: '''
                    + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
                    + '''\n'''.join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items()		)
                    + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
                    + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
                )		)
      _lowerCamelCase			    =				max(lists_lengths.values() ,						default=0		)
      return max(1 ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[range]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      for group_idx in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            _lowerCamelCase			    =				shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            _lowerCamelCase			    =				range(__UpperCAmelCase ,						start + num_shards_to_add		)
            shards_indices_per_group.append(__UpperCAmelCase		)
      return shards_indices_per_group
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[dict]:



      '''simple docstring'''

      _lowerCamelCase			    =				_number_of_shards_in_gen_kwargs(__UpperCAmelCase		)
      if num_shards == 1:
            return [dict(__UpperCAmelCase		)]
      else:
            _lowerCamelCase			    =				_distribute_shards(num_shards=__UpperCAmelCase ,						max_num_jobs=__UpperCAmelCase		)
            return [
                {
                    key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                    if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)
                    else value
                    for key, value in gen_kwargs.items()
                }
                for group_idx in range(len(__UpperCAmelCase		)		)
            ]
def       __magic_name__(  __UpperCAmelCase		)						->    dict:



      '''simple docstring'''

      return {
          key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
          if isinstance(gen_kwargs_list[0][key] ,						__UpperCAmelCase		)
          else gen_kwargs_list[0][key]
          for key in gen_kwargs_list[0]
      }




def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    dict:



      '''simple docstring'''

      _lowerCamelCase			    =				{len(__UpperCAmelCase		) for value in gen_kwargs.values() if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)}
      _lowerCamelCase			    =				{}
      for size in list_sizes:
            _lowerCamelCase			    =				list(range(__UpperCAmelCase		)		)
            rng.shuffle(indices_per_size[size]		)
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      _lowerCamelCase			    =				dict(__UpperCAmelCase		)
      for key, value in shuffled_kwargs.items():
            if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		):
                  _lowerCamelCase			    =				[value[i] for i in indices_per_size[len(__UpperCAmelCase		)]]
      return shuffled_kwargs