




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


_lowerCAmelCase						:   Union[str, Any]		=   logging.get_logger(__name__)

_lowerCAmelCase						:   Tuple		=   {
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		"""trajectory_transformer"""
      UpperCAmelCase_        =		["""past_key_values"""]
      UpperCAmelCase_        =		{
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(			self   :Union[str, Any] ,      lowerCamelCase   :Optional[int]=100 ,      lowerCamelCase   :Optional[int]=5 ,      lowerCamelCase   :Optional[Any]=1 ,      lowerCamelCase   :Any=1 ,      lowerCamelCase   :int=249 ,      lowerCamelCase   :Optional[Any]=6 ,      lowerCamelCase   :Optional[Any]=17 ,      lowerCamelCase   :Optional[int]=25 ,      lowerCamelCase   :Union[str, Any]=4 ,      lowerCamelCase   :Union[str, Any]=4 ,      lowerCamelCase   :Optional[int]=128 ,      lowerCamelCase   :List[Any]=0.1 ,      lowerCamelCase   :List[str]=0.1 ,      lowerCamelCase   :Tuple=0.1 ,      lowerCamelCase   :Dict=0.00_06 ,      lowerCamelCase   :Dict=512 ,      lowerCamelCase   :Optional[Any]=0.02 ,      lowerCamelCase   :Any=1e-12 ,      lowerCamelCase   :Optional[Any]=1 ,      lowerCamelCase   :List[Any]=True ,      lowerCamelCase   :int=1 ,      lowerCamelCase   :Dict=5_0256 ,      lowerCamelCase   :Union[str, Any]=5_0256 ,      **lowerCamelCase   :int ,      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						vocab_size
       UpperCAmelCase__							 =						action_weight
       UpperCAmelCase__							 =						reward_weight
       UpperCAmelCase__							 =						value_weight
       UpperCAmelCase__							 =						max_position_embeddings
       UpperCAmelCase__							 =						block_size
       UpperCAmelCase__							 =						action_dim
       UpperCAmelCase__							 =						observation_dim
       UpperCAmelCase__							 =						transition_dim
       UpperCAmelCase__							 =						learning_rate
       UpperCAmelCase__							 =						n_layer
       UpperCAmelCase__							 =						n_head
       UpperCAmelCase__							 =						n_embd
       UpperCAmelCase__							 =						embd_pdrop
       UpperCAmelCase__							 =						attn_pdrop
       UpperCAmelCase__							 =						resid_pdrop
       UpperCAmelCase__							 =						initializer_range
       UpperCAmelCase__							 =						layer_norm_eps
       UpperCAmelCase__							 =						kaiming_initializer_range
       UpperCAmelCase__							 =						use_cache
       super().__init__(pad_token_id=lowerCamelCase ,      bos_token_id=lowerCamelCase ,      eos_token_id=lowerCamelCase ,      **lowerCamelCase      )











from typing import TYPE_CHECKING

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


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[int]		=   ["MaskFormerFeatureExtractor"]
  _lowerCAmelCase						:   Dict		=   ["MaskFormerImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MaskFormerForInstanceSegmentation",
      "MaskFormerModel",
      "MaskFormerPreTrainedModel",
  ]
  _lowerCAmelCase						:   List[str]		=   [
      "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

  _lowerCAmelCase						:   str		=   _LazyModule(__name__, globals()["__file__"], _import_structure)





