



'''simple docstring'''
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE			:   Dict							   ={
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE			:   List[str]							   =['''PerceiverFeatureExtractor''']
					__SCREAMING_SNAKE_CASE			:   Dict							   =['''PerceiverImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE			:   Optional[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

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






'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase    :     Optional[Any]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     List[Any]         =       {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "roberta-prelayernorm"
   def __init__(      self      :			Optional[int], UpperCamelCase__      :			List[Any]=5_02_65, UpperCamelCase__      :			int=7_68, UpperCamelCase__      :			str=12, UpperCamelCase__      :			Union[str, Any]=12, UpperCamelCase__      :			Tuple=30_72, UpperCamelCase__      :			Tuple="gelu", UpperCamelCase__      :			List[Any]=0.1, UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			Any=5_12, UpperCamelCase__      :			Union[str, Any]=2, UpperCamelCase__      :			Optional[Any]=0.02, UpperCamelCase__      :			str=1e-12, UpperCamelCase__      :			str=1, UpperCamelCase__      :			List[str]=0, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Optional[Any]="absolute", UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			List[str]=None, **UpperCamelCase__      :			Any, )  ->			Dict:
        super().__init__(pad_token_id=UpperCamelCase__, bos_token_id=UpperCamelCase__, eos_token_id=UpperCamelCase__, **UpperCamelCase__     )

        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  hidden_act
        _A    		=  intermediate_size
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  initializer_range
        _A    		=  layer_norm_eps
        _A    		=  position_embedding_type
        _A    		=  use_cache
        _A    		=  classifier_dropout







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
             _A    		=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             _A    		=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]     )



