


'''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	:   Any   		=					logging.get_logger(__name__)

__lowercase	:   int   		=					{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}





class _snake_case (			A_ ):
							"""simple docstring"""





							__a				       =  "swiftformer"







							def __init__( self      ,					__A=3      ,					__A=[3, 3, 6, 4]      ,					__A=[48, 56, 112, 220]      ,					__A=4      ,					__A=[True, True, True, True]      ,					__A="gelu"      ,					__A=3      ,					__A=2      ,					__A=1      ,					__A=0.0      ,					__A=True      ,					__A=1E-5      ,					__A=1E-5      ,					**__A      ,					)  ->Union[str, Any]:



											"""simple docstring"""







											super().__init__(**A_						)
											__lowerCamelCase		:   List[str]           =       num_channels
											__lowerCamelCase		:   Any           =       depths
											__lowerCamelCase		:   Optional[int]           =       embed_dims
											__lowerCamelCase		:   Dict           =       mlp_ratio
											__lowerCamelCase		:   Optional[int]           =       downsamples
											__lowerCamelCase		:   List[Any]           =       hidden_act
											__lowerCamelCase		:   List[Any]           =       down_patch_size
											__lowerCamelCase		:   Any           =       down_stride
											__lowerCamelCase		:   Optional[int]           =       down_pad
											__lowerCamelCase		:   Tuple           =       drop_path_rate
											__lowerCamelCase		:   Dict           =       use_layer_scale
											__lowerCamelCase		:   str           =       layer_scale_init_value
											__lowerCamelCase		:   Any           =       batch_norm_eps





class _snake_case (			A_ ):
							"""simple docstring"""





							__a				       =  version.parse('''1.11''' )







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Mapping[str, Mapping[int, str]]:



											"""simple docstring"""







											return OrderedDict(
											    [
											        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
											    ]						)







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->float:



											"""simple docstring"""







											return 1E-4









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A									=		{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]


if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flaubert import (
          FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertForMultipleChoice,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertModel,
          FlaubertPreTrainedModel,
          FlaubertWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_flaubert import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertPreTrainedModel,
          TFFlaubertWithLMHeadModel,
      )

else:
   import sys

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





