




from typing import TYPE_CHECKING

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


_A   =			{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A   =			[
       "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "XLMForMultipleChoice",
       "XLMForQuestionAnswering",
       "XLMForQuestionAnsweringSimple",
       "XLMForSequenceClassification",
       "XLMForTokenClassification",
       "XLMModel",
       "XLMPreTrainedModel",
       "XLMWithLMHeadModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A   =			[
       "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFXLMForMultipleChoice",
       "TFXLMForQuestionAnsweringSimple",
       "TFXLMForSequenceClassification",
       "TFXLMForTokenClassification",
       "TFXLMMainLayer",
       "TFXLMModel",
       "TFXLMPreTrainedModel",
       "TFXLMWithLMHeadModel",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

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



import warnings
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


_A   =			logging.get_logger(__name__)

_A   =			{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							List[Any]									=		"segformer"





     def __init__(self				:   List[Any]    ,   _A				:   int=3    ,   _A				:   List[Any]=4    ,   _A				:   Any=[2, 2, 2, 2]    ,   _A				:   Dict=[8, 4, 2, 1]    ,   _A				:   List[Any]=[3_2, 6_4, 1_6_0, 2_5_6]    ,   _A				:   Tuple=[7, 3, 3, 3]    ,   _A				:   Optional[int]=[4, 2, 2, 2]    ,   _A				:   Dict=[1, 2, 5, 8]    ,   _A				:   int=[4, 4, 4, 4]    ,   _A				:   Dict="gelu"    ,   _A				:   Tuple=0.0    ,   _A				:   Optional[Any]=0.0    ,   _A				:   List[Any]=0.1    ,   _A				:   Union[str, Any]=0.02    ,   _A				:   Dict=0.1    ,   _A				:   List[Any]=1E-6    ,   _A				:   List[str]=2_5_6    ,   _A				:   Optional[Any]=2_5_5    ,   **_A				:   str    ,   )						->						Tuple:
         super().__init__(**_A       )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
             warnings.warn(
                 "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
                 " removed, as the behaviour will default to that of reshape_last_stage = True."    ,   _A    ,   )

         snake_case								= num_channels
         snake_case								= num_encoder_blocks
         snake_case								= depths
         snake_case								= sr_ratios
         snake_case								= hidden_sizes
         snake_case								= patch_sizes
         snake_case								= strides
         snake_case								= mlp_ratios
         snake_case								= num_attention_heads
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= classifier_dropout_prob
         snake_case								= initializer_range
         snake_case								= drop_path_rate
         snake_case								= layer_norm_eps
         snake_case								= decoder_hidden_size
         snake_case								= kwargs.get("reshape_last_stage"    ,   _A       )
         snake_case								= semantic_loss_ignore_index







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		version.parse("1.11"  )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						float:
         return 1E-4





     @property
     def 		UpperCAmelCase(self				:   List[str]       )						->						int:
         return 1_2

