


from typing import TYPE_CHECKING

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


snake_case							:	Optional[int]					      =				{'''configuration_mbart''': ['''MBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MBartConfig''', '''MBartOnnxConfig''']}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	str					      =				['''MBartTokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	Optional[int]					      =				['''MBartTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	Union[str, Any]					      =				[
						    '''MBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MBartForCausalLM''',
						    '''MBartForConditionalGeneration''',
						    '''MBartForQuestionAnswering''',
						    '''MBartForSequenceClassification''',
						    '''MBartModel''',
						    '''MBartPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	str					      =				[
						    '''TFMBartForConditionalGeneration''',
						    '''TFMBartModel''',
						    '''TFMBartPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	Optional[int]					      =				[
						    '''FlaxMBartForConditionalGeneration''',
						    '''FlaxMBartForQuestionAnswering''',
						    '''FlaxMBartForSequenceClassification''',
						    '''FlaxMBartModel''',
						    '''FlaxMBartPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbart import MBartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbart_fast import MBartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mbart import (
												    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MBartForCausalLM,
												    MBartForConditionalGeneration,
												    MBartForQuestionAnswering,
												    MBartForSequenceClassification,
												    MBartModel,
												    MBartPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mbart import (
												    FlaxMBartForConditionalGeneration,
												    FlaxMBartForQuestionAnswering,
												    FlaxMBartForSequenceClassification,
												    FlaxMBartModel,
												    FlaxMBartPreTrainedModel,
												)

else:
						import sys

						snake_case							:	Tuple					      =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:	Tuple					      =				logging.get_logger(__name__)

snake_case							:	List[Any]					      =				{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   int   				=      '''efficientformer'''
					def __init__(				self       :List[str]      ,__snake_case       :List[int] = [3, 2, 6, 4]      ,__snake_case       :List[int] = [48, 96, 2_24, 4_48]      ,__snake_case       :List[bool] = [True, True, True, True]      ,__snake_case       :int = 4_48      ,__snake_case       :int = 32      ,__snake_case       :int = 4      ,__snake_case       :int = 7      ,__snake_case       :int = 5      ,__snake_case       :int = 8      ,__snake_case       :int = 4      ,__snake_case       :float = 0.0      ,__snake_case       :int = 16      ,__snake_case       :int = 3      ,__snake_case       :int = 3      ,__snake_case       :int = 3      ,__snake_case       :int = 2      ,__snake_case       :int = 1      ,__snake_case       :float = 0.0      ,__snake_case       :int = 1      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,__snake_case       :float = 1E-5      ,__snake_case       :str = "gelu"      ,__snake_case       :float = 0.02      ,__snake_case       :float = 1E-12      ,__snake_case       :int = 2_24      ,__snake_case       :float = 1E-05      ,**__snake_case       :Dict      ,) ->   None:
												super().__init__(**__snake_case				)

												a__					=					hidden_act
												a__					=					hidden_dropout_prob
												a__					=					hidden_sizes
												a__					=					num_hidden_layers
												a__					=					num_attention_heads
												a__					=					initializer_range
												a__					=					layer_norm_eps
												a__					=					patch_size
												a__					=					num_channels
												a__					=					depths
												a__					=					mlp_expansion_ratio
												a__					=					downsamples
												a__					=					dim
												a__					=					key_dim
												a__					=					attention_ratio
												a__					=					resolution
												a__					=					pool_size
												a__					=					downsample_patch_size
												a__					=					downsample_stride
												a__					=					downsample_pad
												a__					=					drop_path_rate
												a__					=					num_metaad_blocks
												a__					=					distillation
												a__					=					use_layer_scale
												a__					=					layer_scale_init_value
												a__					=					image_size
												a__					=					batch_norm_eps




