


"""simple docstring"""




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


__SCREAMING_SNAKE_CASE      		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      		=    {
    """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 __snake_case		(       _a   ):







   """simple docstring"""




   lowerCAmelCase_					: int  						=      '''trajectory_transformer'''
   lowerCAmelCase_					: Dict  						=      ['''past_key_values''']
   lowerCAmelCase_					: Any  						=      {
       '''hidden_size''': '''n_embd''',
       '''num_attention_heads''': '''n_head''',
       '''num_hidden_layers''': '''n_layer''',
   }




   def __init__(		self				:Optional[int]					,	UpperCamelCase__				:Optional[Any]=100					,	UpperCamelCase__				:List[str]=5					,	UpperCamelCase__				:Tuple=1					,	UpperCamelCase__				:str=1					,	UpperCamelCase__				:Optional[Any]=249					,	UpperCamelCase__				:List[str]=6					,	UpperCamelCase__				:Optional[int]=17					,	UpperCamelCase__				:Optional[Any]=25					,	UpperCamelCase__				:int=4					,	UpperCamelCase__				:List[Any]=4					,	UpperCamelCase__				:List[Any]=128					,	UpperCamelCase__				:Any=0.1					,	UpperCamelCase__				:Optional[int]=0.1					,	UpperCamelCase__				:Any=0.1					,	UpperCamelCase__				:List[Any]=0.0006					,	UpperCamelCase__				:List[str]=512					,	UpperCamelCase__				:Union[str, Any]=0.02					,	UpperCamelCase__				:Any=1E-12					,	UpperCamelCase__				:str=1					,	UpperCamelCase__				:Tuple=True					,	UpperCamelCase__				:Any=1					,	UpperCamelCase__				:Any=50_256					,	UpperCamelCase__				:Union[str, Any]=50_256					,	**UpperCamelCase__				:Tuple					,	):
          _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__							)








from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Union[List[PIL.Image.Image], np.ndarray]
       __lowerCAmelCase    :		Optional[List[bool]]


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


