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


lowercase_     :   str 				=				logging.get_logger(__name__)

lowercase_     :   int 				=				{
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}






class 		_lowerCamelCase		(   UpperCamelCase_    ):
				__a  =      "transfo-xl"
				__a  =      ["mems"]
				__a  =      {
				    "n_token": "vocab_size",
				    "hidden_size": "d_model",
				    "num_attention_heads": "n_head",
				    "num_hidden_layers": "n_layer",
				}
				def __init__(  self , lowerCAmelCase=267735 , lowerCAmelCase=[20000, 40000, 200000] , lowerCAmelCase=1024 , lowerCAmelCase=1024 , lowerCAmelCase=16 , lowerCAmelCase=64 , lowerCAmelCase=4096 , lowerCAmelCase=4 , lowerCAmelCase=False , lowerCAmelCase=18 , lowerCAmelCase=1600 , lowerCAmelCase=1000 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=0 , lowerCAmelCase=-1 , lowerCAmelCase=True , lowerCAmelCase=0.1 , lowerCAmelCase=0.0 , lowerCAmelCase=True , lowerCAmelCase="normal" , lowerCAmelCase=0.01 , lowerCAmelCase=0.01 , lowerCAmelCase=0.02 , lowerCAmelCase=1e-5 , lowerCAmelCase=0 , **lowerCAmelCase , )       ->					List[str]:
					SCREAMING_SNAKE_CASE__:   str=      vocab_size
					SCREAMING_SNAKE_CASE__:   int=      []
					self.cutoffs.extend(lowerCAmelCase   )
					if proj_share_all_but_first:
						SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [False] + [True] * len(self.cutoffs   )
					else:
						SCREAMING_SNAKE_CASE__:   str=      [False] + [False] * len(self.cutoffs   )
					SCREAMING_SNAKE_CASE__:   Tuple=      d_model
					SCREAMING_SNAKE_CASE__:   Any=      d_embed
					SCREAMING_SNAKE_CASE__:   Optional[Any]=      d_head
					SCREAMING_SNAKE_CASE__:   Any=      d_inner
					SCREAMING_SNAKE_CASE__:   List[Any]=      div_val
					SCREAMING_SNAKE_CASE__:   List[Any]=      pre_lnorm
					SCREAMING_SNAKE_CASE__:   Optional[int]=      n_layer
					SCREAMING_SNAKE_CASE__:   int=      n_head
					SCREAMING_SNAKE_CASE__:   Optional[Any]=      mem_len
					SCREAMING_SNAKE_CASE__:   str=      same_length
					SCREAMING_SNAKE_CASE__:   str=      attn_type
					SCREAMING_SNAKE_CASE__:   Tuple=      clamp_len
					SCREAMING_SNAKE_CASE__:   int=      sample_softmax
					SCREAMING_SNAKE_CASE__:   List[str]=      adaptive
					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      dropout
					SCREAMING_SNAKE_CASE__:   Optional[Any]=      dropatt
					SCREAMING_SNAKE_CASE__:   str=      untie_r
					SCREAMING_SNAKE_CASE__:   Any=      init
					SCREAMING_SNAKE_CASE__:   str=      init_range
					SCREAMING_SNAKE_CASE__:   Any=      proj_init_std
					SCREAMING_SNAKE_CASE__:   Tuple=      init_std
					SCREAMING_SNAKE_CASE__:   int=      layer_norm_epsilon
					super().__init__(eos_token_id=lowerCAmelCase , **lowerCAmelCase   )
				@property
				def 						UpperCamelCase_				(  self   )       ->					Any:
					# Message copied from Transformer-XL documentation
					logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.'   )
					return -1



				@max_position_embeddings.setter
				def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Optional[Any]:
					# Message copied from Transformer-XL documentation
					raise NotImplementedError(
					    f'The model {self.model_type} is one of the few models that has no sequence length limit.'   )


from typing import TYPE_CHECKING

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


lowercase_     :   List[str] 				=				{
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   Optional[Any] 				=				['MaskFormerFeatureExtractor']
      lowercase_     :   Dict 				=				['MaskFormerImageProcessor']


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MaskFormerForInstanceSegmentation',
          'MaskFormerModel',
          'MaskFormerPreTrainedModel',
      ]
      lowercase_     :   Optional[Any] 				=				[
          'MaskFormerSwinBackbone',
          'MaskFormerSwinModel',
          'MaskFormerSwinPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
      from .configuration_maskformer_swin import MaskFormerSwinConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_maskformer import MaskFormerFeatureExtractor
            from .image_processing_maskformer import MaskFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_maskformer import (
                MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaskFormerForInstanceSegmentation,
                MaskFormerModel,
                MaskFormerPreTrainedModel,
            )
            from .modeling_maskformer_swin import (
                MaskFormerSwinBackbone,
                MaskFormerSwinModel,
                MaskFormerSwinPreTrainedModel,
            )


else:
      import sys

      lowercase_     :   Any 				=				_LazyModule(__name__, globals()['__file__'], _import_structure)

