




from typing import TYPE_CHECKING

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


__A						:							Tuple							=  {
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A						:							Union[str, Any]							=  ["""PerceiverFeatureExtractor"""]
		__A						:							Optional[int]							=  ["""PerceiverImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A						:							int							=  [
		    """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """PerceiverForImageClassificationConvProcessing""",
		    """PerceiverForImageClassificationFourier""",
		    """PerceiverForImageClassificationLearned""",
		    """PerceiverForMaskedLM""",
		    """PerceiverForMultimodalAutoencoding""",
		    """PerceiverForOpticalFlow""",
		    """PerceiverForSequenceClassification""",
		    """PerceiverLayer""",
		    """PerceiverModel""",
		    """PerceiverPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

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









from __future__ import annotations
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			create_state_space_tree(SCREAMING_SNAKE_CASE					,	[]					,	0					,	[0 for i in range(len(SCREAMING_SNAKE_CASE   )   )]   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	):
			'''simple docstring'''

			if index == len(SCREAMING_SNAKE_CASE   ):
						print(SCREAMING_SNAKE_CASE   )
						return

			for i in range(len(SCREAMING_SNAKE_CASE   )   ):
						if not index_used[i]:
									current_sequence.append(sequence[i]   )
									SCREAMING_SNAKE_CASE           =					True
									create_state_space_tree(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	index + 1					,	SCREAMING_SNAKE_CASE   )
									current_sequence.pop()
									SCREAMING_SNAKE_CASE           =					False
__A						:							list[int | str]							=  [3, 1, 2, 4]
generate_all_permutations(sequence)

__A						:							list[int | str]							=  ["A", "B", "C"]




generate_all_permutations(sequence_a)






