




"""simple docstring"""






from typing import TYPE_CHECKING

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


lowercase__  		=		{
    """configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
    """convert_funnel_original_tf_checkpoint_to_pytorch""": [],
    """tokenization_funnel""": ["""FunnelTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__  		=		["""FunnelTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__  		=		[
			    """FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """FunnelBaseModel""",
			    """FunnelForMaskedLM""",
			    """FunnelForMultipleChoice""",
			    """FunnelForPreTraining""",
			    """FunnelForQuestionAnswering""",
			    """FunnelForSequenceClassification""",
			    """FunnelForTokenClassification""",
			    """FunnelModel""",
			    """FunnelPreTrainedModel""",
			    """load_tf_weights_in_funnel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__  		=		[
			    """TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFFunnelBaseModel""",
			    """TFFunnelForMaskedLM""",
			    """TFFunnelForMultipleChoice""",
			    """TFFunnelForPreTraining""",
			    """TFFunnelForQuestionAnswering""",
			    """TFFunnelForSequenceClassification""",
			    """TFFunnelForTokenClassification""",
			    """TFFunnelModel""",
			    """TFFunnelPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
			from .tokenization_funnel import FunnelTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_funnel_fast import FunnelTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_funnel import (
						    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						    FunnelPreTrainedModel,
						    load_tf_weights_in_funnel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_funnel import (
						    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						    TFFunnelPreTrainedModel,
						)

else:
			import sys

			lowercase__  		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """deit"""
			def __init__(						self						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase="gelu"						,   lowercase=0.0						,   lowercase=0.0						,   lowercase=0.02						,   lowercase=1E-12						,   lowercase=224						,   lowercase=16						,   lowercase=3						,   lowercase=True						,   lowercase=16						,   **lowercase						,   ):
							super().__init__(**lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					hidden_size
							_lowerCamelCase  :    List[str]					=					num_hidden_layers
							_lowerCamelCase  :    Tuple					=					num_attention_heads
							_lowerCamelCase  :    Any					=					intermediate_size
							_lowerCamelCase  :    int					=					hidden_act
							_lowerCamelCase  :    Any					=					hidden_dropout_prob
							_lowerCamelCase  :    str					=					attention_probs_dropout_prob
							_lowerCamelCase  :    str					=					initializer_range
							_lowerCamelCase  :    Union[str, Any]					=					layer_norm_eps
							_lowerCamelCase  :    Optional[Any]					=					image_size
							_lowerCamelCase  :    Optional[int]					=					patch_size
							_lowerCamelCase  :    Dict					=					num_channels
							_lowerCamelCase  :    Dict					=					qkv_bias
							_lowerCamelCase  :    Dict					=					encoder_stride






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    version.parse("""1.11"""		)
			@property
			def       A_					(						self						):
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]						)






			@property
			def       A_					(						self						):
							return 1E-4