

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_          =   {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_          =   ['ReformerTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_          =   ['ReformerTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_          =   [
					    'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ReformerAttention',
					    'ReformerForMaskedLM',
					    'ReformerForQuestionAnswering',
					    'ReformerForSequenceClassification',
					    'ReformerLayer',
					    'ReformerModel',
					    'ReformerModelWithLMHead',
					    'ReformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_reformer import ReformerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_reformer_fast import ReformerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_reformer import (
										    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ReformerAttention,
										    ReformerForMaskedLM,
										    ReformerForQuestionAnswering,
										    ReformerForSequenceClassification,
										    ReformerLayer,
										    ReformerModel,
										    ReformerModelWithLMHead,
										    ReformerPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE_          =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





from __future__ import annotations

def 			lowerCamelCase__			(       _a):
	if len(_a) == 0:
		return []
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							min(_a), max(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							int(max_value - min_value) + 1
	SCREAMING_SNAKE_CASE :   list[list]					  =							[[] for _ in range(_a)]

	for i in my_list:
		buckets[int(i - min_value)].append(_a)

	return [v for bucket in buckets for v in sorted(_a)]


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]