
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       ):
     SCREAMING_SNAKE_CASE			    =    '''trajectory_transformer'''
     SCREAMING_SNAKE_CASE			    =    ['''past_key_values''']
     SCREAMING_SNAKE_CASE			    =    {
         '''hidden_size''': '''n_embd''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }

     def __init__(	self					:     List[str]   ,					__UpperCamelCase					:     Optional[int]=1_0_0   ,					__UpperCamelCase					:     List[Any]=5   ,					__UpperCamelCase					:     Any=1   ,					__UpperCamelCase					:     Any=1   ,					__UpperCamelCase					:     Tuple=2_4_9   ,					__UpperCamelCase					:     Tuple=6   ,					__UpperCamelCase					:     str=1_7   ,					__UpperCamelCase					:     Any=2_5   ,					__UpperCamelCase					:     List[str]=4   ,					__UpperCamelCase					:     Dict=4   ,					__UpperCamelCase					:     Any=1_2_8   ,					__UpperCamelCase					:     List[str]=0.1   ,					__UpperCamelCase					:     Optional[Any]=0.1   ,					__UpperCamelCase					:     int=0.1   ,					__UpperCamelCase					:     Any=0.0006   ,					__UpperCamelCase					:     List[str]=5_1_2   ,					__UpperCamelCase					:     Dict=0.02   ,					__UpperCamelCase					:     Union[str, Any]=1E-12   ,					__UpperCamelCase					:     Any=1   ,					__UpperCamelCase					:     str=True   ,					__UpperCamelCase					:     Union[str, Any]=1   ,					__UpperCamelCase					:     Tuple=5_0_2_5_6   ,					__UpperCamelCase					:     str=5_0_2_5_6   ,					**__UpperCamelCase					:     List[Any]   ,					):
            lowerCamelCase_							  =					vocab_size
            lowerCamelCase_							  =					action_weight
            lowerCamelCase_							  =					reward_weight
            lowerCamelCase_							  =					value_weight
            lowerCamelCase_							  =					max_position_embeddings
            lowerCamelCase_							  =					block_size
            lowerCamelCase_							  =					action_dim
            lowerCamelCase_							  =					observation_dim
            lowerCamelCase_							  =					transition_dim
            lowerCamelCase_							  =					learning_rate
            lowerCamelCase_							  =					n_layer
            lowerCamelCase_							  =					n_head
            lowerCamelCase_							  =					n_embd
            lowerCamelCase_							  =					embd_pdrop
            lowerCamelCase_							  =					attn_pdrop
            lowerCamelCase_							  =					resid_pdrop
            lowerCamelCase_							  =					initializer_range
            lowerCamelCase_							  =					layer_norm_eps
            lowerCamelCase_							  =					kaiming_initializer_range
            lowerCamelCase_							  =					use_cache
            super().__init__(pad_token_id=__UpperCamelCase   ,					bos_token_id=__UpperCamelCase   ,					eos_token_id=__UpperCamelCase   ,					**__UpperCamelCase   )







from typing import TYPE_CHECKING

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


lowercase        =  {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''RagModel''',
		    '''RagPreTrainedModel''',
		    '''RagSequenceForGeneration''',
		    '''RagTokenForGeneration''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TFRagModel''',
		    '''TFRagPreTrainedModel''',
		    '''TFRagSequenceForGeneration''',
		    '''TFRagTokenForGeneration''',
		]


if TYPE_CHECKING:
		from .configuration_rag import RagConfig
		from .retrieval_rag import RagRetriever
		from .tokenization_rag import RagTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_rag import (
				    TFRagModel,
				    TFRagPreTrainedModel,
				    TFRagSequenceForGeneration,
				    TFRagTokenForGeneration,
				)

else:
		import sys

		lowercase        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





