






'''simple docstring'''







from typing import List

import numpy as np





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       int:
							lowerCamelCase__					:	Any         =       {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__					:	Any         =       max(lists_lengths.values()				,			default=0      )
							return max(1				,			UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       List[range]:
							lowerCamelCase__					:	Optional[Any]         =       []
							for group_idx in range(UpperCamelCase      ):
														lowerCamelCase__					:	Tuple         =       num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
														if num_shards_to_add == 0:
																					break
														lowerCamelCase__					:	List[str]         =       shards_indices_per_group[-1].stop if shards_indices_per_group else 0
														lowerCamelCase__					:	Tuple         =       range(UpperCamelCase				,			start + num_shards_to_add      )
														shards_indices_per_group.append(UpperCamelCase      )
							return shards_indices_per_group





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       List[dict]:
							lowerCamelCase__					:	Optional[int]         =       _number_of_shards_in_gen_kwargs(UpperCamelCase      )
							if num_shards == 1:
														return [dict(UpperCamelCase      )]
							else:
														lowerCamelCase__					:	Union[str, Any]         =       _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  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       dict:
							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  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       dict:
							lowerCamelCase__					:	int         =       {len(UpperCamelCase      ) for value in gen_kwargs.values() if isinstance(UpperCamelCase				,			UpperCamelCase      )}
							lowerCamelCase__					:	Dict         =       {}
							for size in list_sizes:
														lowerCamelCase__					:	int         =       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__					:	Union[str, Any]         =       dict(UpperCamelCase      )
							for key, value in shuffled_kwargs.items():
														if isinstance(UpperCamelCase				,			UpperCamelCase      ):
																					lowerCamelCase__					:	Dict         =       [value[i] for i in indices_per_size[len(UpperCamelCase      )]]
							return shuffled_kwargs









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


_A	:			Dict         ={'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			Optional[int]         =['''HerbertTokenizerFast''']


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

			_A	:			Optional[int]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

