






from collections import OrderedDict
from typing import Mapping

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


snake_case_   :	Dict							      =logging.get_logger(__name__)

snake_case_   :	Optional[int]							      ={
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Any 					= SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		UpperCAmelCase_							:       Tuple 					= 'squeezebert'






		def __init__(      self							, lowercase__=30522							, lowercase__=768							, lowercase__=12							, lowercase__=12							, lowercase__=3072							, lowercase__="gelu"							, lowercase__=0.1							, lowercase__=0.1							, lowercase__=512							, lowercase__=2							, lowercase__=0.02							, lowercase__=1e-12							, lowercase__=0							, lowercase__=768							, lowercase__=4							, lowercase__=4							, lowercase__=4							, lowercase__=1							, lowercase__=4							, lowercase__=4							, **lowercase__							, )				->				str:
				super().__init__(pad_token_id=lowercase__							, **lowercase__						)

				__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    =							embedding_size
				__A    =							q_groups
				__A    =							k_groups
				__A    =							v_groups
				__A    =							post_attention_groups
				__A    =							intermediate_groups
				__A    =							output_groups





class        a__      (	lowerCAmelCase__		):






		@property
		def 					_lowerCamelCase					(      self						)				->				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),
				        ("token_type_ids", dynamic_axis),
				    ]						)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A							=       {'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _A							=       [
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

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



