





from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class        snake_case_		(			_A):







		def __init__(		self			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     __lowercase = False			,     __lowercase = False			,     __lowercase = None			,     **__lowercase			,     )				->   List[Any]:
				lowerCamelCase   : Dict								=path_or_paths
				lowerCamelCase   : Optional[int]								=split if split or isinstance(__lowercase			,     __lowercase   ) else '''train'''
				lowerCamelCase   : Tuple								=features
				lowerCamelCase   : Tuple								=cache_dir
				lowerCamelCase   : Optional[Any]								=keep_in_memory
				lowerCamelCase   : Any								=streaming
				lowerCamelCase   : Dict								=num_proc
				lowerCamelCase   : List[Any]								=kwargs







		@abstractmethod
		def 			__lowercase   (		self   )				->   Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
				pass

class        snake_case_		(			_A):







		def __init__(		self			,     __lowercase = None			,     __lowercase = None			,     __lowercase = False			,     __lowercase = False			,     __lowercase = None			,     **__lowercase			,     )				->   List[str]:
				lowerCamelCase   : Tuple								=features
				lowerCamelCase   : Union[str, Any]								=cache_dir
				lowerCamelCase   : Optional[int]								=keep_in_memory
				lowerCamelCase   : List[str]								=streaming
				lowerCamelCase   : List[str]								=num_proc
				lowerCamelCase   : List[str]								=kwargs







		@abstractmethod
		def 			__lowercase   (		self   )				->   Union[Dataset, IterableDataset]:
				pass








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_							=			{
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''InstructBlipQFormerModel''',
				    '''InstructBlipPreTrainedModel''',
				    '''InstructBlipForConditionalGeneration''',
				    '''InstructBlipVisionModel''',
				]

if TYPE_CHECKING:
				from .configuration_instructblip import (
				    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    InstructBlipConfig,
				    InstructBlipQFormerConfig,
				    InstructBlipVisionConfig,
				)
				from .processing_instructblip import InstructBlipProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_instructblip import (
								    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    InstructBlipForConditionalGeneration,
								    InstructBlipPreTrainedModel,
								    InstructBlipQFormerModel,
								    InstructBlipVisionModel,
								)

else:
				import sys

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

