


'''simple docstring'''

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


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "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 						__SCREAMING_SNAKE_CASE					(      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     ,							__UpperCamelCase=100     ,							__UpperCamelCase=5     ,							__UpperCamelCase=1     ,							__UpperCamelCase=1     ,							__UpperCamelCase=249     ,							__UpperCamelCase=6     ,							__UpperCamelCase=17     ,							__UpperCamelCase=25     ,							__UpperCamelCase=4     ,							__UpperCamelCase=4     ,							__UpperCamelCase=128     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.00_06     ,							__UpperCamelCase=512     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-12     ,							__UpperCamelCase=1     ,							__UpperCamelCase=True     ,							__UpperCamelCase=1     ,							__UpperCamelCase=50_256     ,							__UpperCamelCase=50_256     ,							**__UpperCamelCase     ,							)       ->    List[Any]:
          _a						=		vocab_size
          _a						=		action_weight
          _a						=		reward_weight
          _a						=		value_weight
          _a						=		max_position_embeddings
          _a						=		block_size
          _a						=		action_dim
          _a						=		observation_dim
          _a						=		transition_dim
          _a						=		learning_rate
          _a						=		n_layer
          _a						=		n_head
          _a						=		n_embd
          _a						=		embd_pdrop
          _a						=		attn_pdrop
          _a						=		resid_pdrop
          _a						=		initializer_range
          _a						=		layer_norm_eps
          _a						=		kaiming_initializer_range
          _a						=		use_cache
          super().__init__(pad_token_id=__UpperCamelCase     ,							bos_token_id=__UpperCamelCase     ,							eos_token_id=__UpperCamelCase     ,							**__UpperCamelCase		)





'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__				     =		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				     =		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

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

