
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase   =	logging.get_logger(__name__)

lowercase   =	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     """glpn"""

					def __init__(						self       :  int ,       __UpperCamelCase       :  Any=3 ,       __UpperCamelCase       :  List[Any]=4 ,       __UpperCamelCase       :  Union[str, Any]=[2, 2, 2, 2] ,       __UpperCamelCase       :  Any=[8, 4, 2, 1] ,       __UpperCamelCase       :  Dict=[3_2, 6_4, 1_6_0, 2_5_6] ,       __UpperCamelCase       :  Optional[Any]=[7, 3, 3, 3] ,       __UpperCamelCase       :  str=[4, 2, 2, 2] ,       __UpperCamelCase       :  List[Any]=[1, 2, 5, 8] ,       __UpperCamelCase       :  Optional[Any]=[4, 4, 4, 4] ,       __UpperCamelCase       :  int="gelu" ,       __UpperCamelCase       :  Any=0.0 ,       __UpperCamelCase       :  List[str]=0.0 ,       __UpperCamelCase       :  Dict=0.02 ,       __UpperCamelCase       :  int=0.1 ,       __UpperCamelCase       :  Dict=1E-6 ,       __UpperCamelCase       :  List[str]=6_4 ,       __UpperCamelCase       :  List[str]=1_0 ,       __UpperCamelCase       :  Any=-1 ,       **__UpperCamelCase       :  Optional[Any] ,       ):
												super().__init__(**_lowercase )

												lowerCamelCase_					=       num_channels
												lowerCamelCase_					=       num_encoder_blocks
												lowerCamelCase_					=       depths
												lowerCamelCase_					=       sr_ratios
												lowerCamelCase_					=       hidden_sizes
												lowerCamelCase_					=       patch_sizes
												lowerCamelCase_					=       strides
												lowerCamelCase_					=       mlp_ratios
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       initializer_range
												lowerCamelCase_					=       drop_path_rate
												lowerCamelCase_					=       layer_norm_eps
												lowerCamelCase_					=       decoder_hidden_size
												lowerCamelCase_					=       max_depth
												lowerCamelCase_					=       head_in_index








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str						=     ["""RemBertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     ["""RemBertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict						=     [
     """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RemBertForCausalLM""",
     """RemBertForMaskedLM""",
     """RemBertForMultipleChoice""",
     """RemBertForQuestionAnswering""",
     """RemBertForSequenceClassification""",
     """RemBertForTokenClassification""",
     """RemBertLayer""",
     """RemBertModel""",
     """RemBertPreTrainedModel""",
     """load_tf_weights_in_rembert""",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[int]						=     [
     """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFRemBertForCausalLM""",
     """TFRemBertForMaskedLM""",
     """TFRemBertForMultipleChoice""",
     """TFRemBertForQuestionAnswering""",
     """TFRemBertForSequenceClassification""",
     """TFRemBertForTokenClassification""",
     """TFRemBertLayer""",
     """TFRemBertModel""",
     """TFRemBertPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert import RemBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert_fast import RemBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rembert import (
      REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      RemBertForCausalLM,
      RemBertForMaskedLM,
      RemBertForMultipleChoice,
      RemBertForQuestionAnswering,
      RemBertForSequenceClassification,
      RemBertForTokenClassification,
      RemBertLayer,
      RemBertModel,
      RemBertPreTrainedModel,
      load_tf_weights_in_rembert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rembert import (
      TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRemBertForCausalLM,
      TFRemBertForMaskedLM,
      TFRemBertForMultipleChoice,
      TFRemBertForQuestionAnswering,
      TFRemBertForSequenceClassification,
      TFRemBertForTokenClassification,
      TFRemBertLayer,
      TFRemBertModel,
      TFRemBertPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
