





'''simple docstring'''




from typing import TYPE_CHECKING

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


a__					:			List[Any]          =     {
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			int          =     ['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Optional[int]          =     [
			    '''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

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










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__					:			int          =     logging.get_logger(__name__)






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		None:
		warnings.warn(
		    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
		    ' Please use PerceiverImageProcessor instead.'      ,       UpperCamelCase_      ,       )
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)



