
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_lowerCamelCase					   ={"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase					   =["""GPTNeoXTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase					   =[
          """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPTNeoXForCausalLM""",
          """GPTNeoXForQuestionAnswering""",
          """GPTNeoXForSequenceClassification""",
          """GPTNeoXForTokenClassification""",
          """GPTNeoXLayer""",
          """GPTNeoXModel""",
          """GPTNeoXPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_neox import (
                GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTNeoXForCausalLM,
                GPTNeoXForQuestionAnswering,
                GPTNeoXForSequenceClassification,
                GPTNeoXForTokenClassification,
                GPTNeoXLayer,
                GPTNeoXModel,
                GPTNeoXPreTrainedModel,
            )


else:
      import sys

      _lowerCamelCase					   =_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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """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 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""segformer"""
      def __init__(      self    ,  __magic_name__=3    ,  __magic_name__=4    ,  __magic_name__=[2, 2, 2, 2]    ,  __magic_name__=[8, 4, 2, 1]    ,  __magic_name__=[3_2, 6_4, 1_6_0, 2_5_6]    ,  __magic_name__=[7, 3, 3, 3]    ,  __magic_name__=[4, 2, 2, 2]    ,  __magic_name__=[1, 2, 5, 8]    ,  __magic_name__=[4, 4, 4, 4]    ,  __magic_name__="gelu"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=0.02    ,  __magic_name__=0.1    ,  __magic_name__=1e-6    ,  __magic_name__=2_5_6    ,  __magic_name__=2_5_5    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       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."""    ,  __magic_name__    ,  )

       lowerCamelCase		:	Optional[Any]							=      num_channels
       lowerCamelCase		:	str							=      num_encoder_blocks
       lowerCamelCase		:	Any							=      depths
       lowerCamelCase		:	List[Any]							=      sr_ratios
       lowerCamelCase		:	int							=      hidden_sizes
       lowerCamelCase		:	Union[str, Any]							=      patch_sizes
       lowerCamelCase		:	Optional[Any]							=      strides
       lowerCamelCase		:	Dict							=      mlp_ratios
       lowerCamelCase		:	str							=      num_attention_heads
       lowerCamelCase		:	Any							=      hidden_act
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Union[str, Any]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Dict							=      classifier_dropout_prob
       lowerCamelCase		:	Tuple							=      initializer_range
       lowerCamelCase		:	Dict							=      drop_path_rate
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	Any							=      decoder_hidden_size
       lowerCamelCase		:	str							=      kwargs.get("""reshape_last_stage"""    ,  __magic_name__      )
       lowerCamelCase		:	Dict							=      semantic_loss_ignore_index





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    str           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-4






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




