
'''simple docstring'''







from typing import List

import numpy as np







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	_snake_case     		=	{key: len(__A			) for key, value in gen_kwargs.items() if isinstance(__A    ,       __A			)}
	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.'
		    )			)
	_snake_case     		=	max(lists_lengths.values()    ,       default=0			)
	return max(1    ,       __A			)







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







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







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			dict:
	return {
	    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
	    if isinstance(gen_kwargs_list[0][key]    ,       __A			)
	    else gen_kwargs_list[0][key]
	    for key in gen_kwargs_list[0]
	}







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



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase					:							str   =      {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Any   =      [
					    "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "NezhaForNextSentencePrediction",
					    "NezhaForMaskedLM",
					    "NezhaForPreTraining",
					    "NezhaForMultipleChoice",
					    "NezhaForQuestionAnswering",
					    "NezhaForSequenceClassification",
					    "NezhaForTokenClassification",
					    "NezhaModel",
					    "NezhaPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nezha import (
										    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NezhaForMaskedLM,
										    NezhaForMultipleChoice,
										    NezhaForNextSentencePrediction,
										    NezhaForPreTraining,
										    NezhaForQuestionAnswering,
										    NezhaForSequenceClassification,
										    NezhaForTokenClassification,
										    NezhaModel,
										    NezhaPreTrainedModel,
										)


else:
					import sys

					lowercase					:							Union[str, Any]   =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

