

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


A_	:			Union[str, Any]  	=      logging.get_logger(__name__)

A_	:			str  	=      {
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	'''lxmert'''
					UpperCAmelCase__:       Optional[Any]  							=	{}






					def __init__(			self   ,	A__=3_0522   ,	A__=768   ,	A__=12   ,	A__=9500   ,	A__=1600   ,	A__=400   ,	A__=3072   ,	A__="gelu"   ,	A__=0.1   ,	A__=0.1   ,	A__=512   ,	A__=2   ,	A__=0.0_2   ,	A__=1e-12   ,	A__=9   ,	A__=5   ,	A__=5   ,	A__=2048   ,	A__=4   ,	A__=6.6_7   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	**A__   ,	):
						A__							:   str					       =		vocab_size
						A__							:   int					       =		hidden_size
						A__							:   List[Any]					       =		num_attention_heads
						A__							:   Union[str, Any]					       =		hidden_act
						A__							:   Tuple					       =		intermediate_size
						A__							:   Optional[int]					       =		hidden_dropout_prob
						A__							:   List[str]					       =		attention_probs_dropout_prob
						A__							:   str					       =		max_position_embeddings
						A__							:   int					       =		type_vocab_size
						A__							:   Any					       =		initializer_range
						A__							:   int					       =		layer_norm_eps
						A__							:   Optional[Any]					       =		num_qa_labels
						A__							:   List[Any]					       =		num_object_labels
						A__							:   List[Any]					       =		num_attr_labels
						A__							:   int					       =		l_layers
						A__							:   Union[str, Any]					       =		x_layers
						A__							:   Optional[int]					       =		r_layers
						A__							:   str					       =		visual_feat_dim
						A__							:   Union[str, Any]					       =		visual_pos_dim
						A__							:   Tuple					       =		visual_loss_normalizer
						A__							:   Optional[int]					       =		task_matched
						A__							:   str					       =		task_mask_lm
						A__							:   Optional[int]					       =		task_obj_predict
						A__							:   Optional[Any]					       =		task_qa
						A__							:   int					       =		visual_obj_loss
						A__							:   List[Any]					       =		visual_attr_loss
						A__							:   int					       =		visual_feat_loss
						A__							:   Dict					       =		{"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
						super().__init__(**A__      )






from typing import TYPE_CHECKING

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


A_	:			Optional[int]  	=      {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['FNetTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Tuple  	=      ['FNetTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			List[Any]  	=      [
	    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FNetForMaskedLM',
	    'FNetForMultipleChoice',
	    'FNetForNextSentencePrediction',
	    'FNetForPreTraining',
	    'FNetForQuestionAnswering',
	    'FNetForSequenceClassification',
	    'FNetForTokenClassification',
	    'FNetLayer',
	    'FNetModel',
	    'FNetPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet import FNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet_fast import FNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fnet import (
		    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FNetForMaskedLM,
		    FNetForMultipleChoice,
		    FNetForNextSentencePrediction,
		    FNetForPreTraining,
		    FNetForQuestionAnswering,
		    FNetForSequenceClassification,
		    FNetForTokenClassification,
		    FNetLayer,
		    FNetModel,
		    FNetPreTrainedModel,
		)


else:
	import sys

	A_	:			Optional[Any]  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



