'''simple docstring'''




from ..utils import DummyObject, requires_backends





class 					__magic_name__		(					metaclass=_UpperCAmelCase):
			UpperCamelCase__       = ['''torch''', '''torchsde''']







			def __init__(			self       :						int       ,    *lowercase_       :						Optional[Any]       ,    **lowercase_       :						str					):
										requires_backends(self       ,    ["""torch""", """torchsde"""]					)







			@classmethod
			def 			SCREAMING_SNAKE_CASE_  (			cls       :						Optional[int]       ,    *lowercase_       :						str       ,    **lowercase_       :						str					):
										requires_backends(cls       ,    ["""torch""", """torchsde"""]					)







			@classmethod
			def 			SCREAMING_SNAKE_CASE_  (			cls       :						List[str]       ,    *lowercase_       :						str       ,    **lowercase_       :						Optional[int]					):
										requires_backends(cls       ,    ["""torch""", """torchsde"""]					)






'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowercase		:				Dict	       =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Dict	       =		["BloomTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Dict	       =		[
						    "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BloomForCausalLM",
						    "BloomModel",
						    "BloomPreTrainedModel",
						    "BloomForSequenceClassification",
						    "BloomForTokenClassification",
						    "BloomForQuestionAnswering",
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

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





