


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 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """trajectory_transformer"""
      __snake_case 	=       ["""past_key_values"""]
      __snake_case 	=       {
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }




      def __init__(    self:	int						,				a:	Dict=100						,				a:	int=5						,				a:	Union[str, Any]=1						,				a:	Optional[int]=1						,				a:	int=249						,				a:	Optional[int]=6						,				a:	str=17						,				a:	Optional[Any]=25						,				a:	int=4						,				a:	Union[str, Any]=4						,				a:	Tuple=128						,				a:	List[str]=0.1						,				a:	List[Any]=0.1						,				a:	Optional[Any]=0.1						,				a:	Any=0.0_0_0_6						,				a:	Optional[int]=512						,				a:	Any=0.0_2						,				a:	Tuple=1e-12						,				a:	Optional[int]=1						,				a:	List[str]=True						,				a:	Any=1						,				a:	Optional[Any]=5_0256						,				a:	Tuple=5_0256						,				**a:	str						,				):
       __lowerCamelCase		:							Tuple		 =						vocab_size
       __lowerCamelCase		:							Optional[int]		 =						action_weight
       __lowerCamelCase		:							Optional[Any]		 =						reward_weight
       __lowerCamelCase		:							Dict		 =						value_weight
       __lowerCamelCase		:							List[str]		 =						max_position_embeddings
       __lowerCamelCase		:							str		 =						block_size
       __lowerCamelCase		:							str		 =						action_dim
       __lowerCamelCase		:							Any		 =						observation_dim
       __lowerCamelCase		:							str		 =						transition_dim
       __lowerCamelCase		:							Optional[int]		 =						learning_rate
       __lowerCamelCase		:							Any		 =						n_layer
       __lowerCamelCase		:							Any		 =						n_head
       __lowerCamelCase		:							Tuple		 =						n_embd
       __lowerCamelCase		:							List[str]		 =						embd_pdrop
       __lowerCamelCase		:							List[Any]		 =						attn_pdrop
       __lowerCamelCase		:							Any		 =						resid_pdrop
       __lowerCamelCase		:							List[str]		 =						initializer_range
       __lowerCamelCase		:							Dict		 =						layer_norm_eps
       __lowerCamelCase		:							Dict		 =						kaiming_initializer_range
       __lowerCamelCase		:							List[Any]		 =						use_cache
       super().__init__(pad_token_id=a						,				bos_token_id=a						,				eos_token_id=a						,				**a    )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_	   =     {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     ['ConvNextFeatureExtractor']
       lowercase_	   =     ['ConvNextImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'ConvNextForImageClassification',
           'ConvNextModel',
           'ConvNextPreTrainedModel',
           'ConvNextBackbone',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'TFConvNextForImageClassification',
           'TFConvNextModel',
           'TFConvNextPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_convnext import ConvNextFeatureExtractor
              from .image_processing_convnext import ConvNextImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_convnext import (
                  CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ConvNextBackbone,
                  ConvNextForImageClassification,
                  ConvNextModel,
                  ConvNextPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
       import sys

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

