

"""simple docstring"""


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


a       :int     =			logging.get_logger(__name__)

a       :Optional[Any]     =			{
    "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'''
     _SCREAMING_SNAKE_CASE    :Dict							=			"""trajectory_transformer"""
     _SCREAMING_SNAKE_CASE    :Dict							=			["""past_key_values"""]
     _SCREAMING_SNAKE_CASE    :Union[str, Any]							=			{
         """hidden_size""": """n_embd""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }

     def __init__(    self						,     _a=100						,     _a=5						,     _a=1						,     _a=1						,     _a=249						,     _a=6						,     _a=17						,     _a=25						,     _a=4						,     _a=4						,     _a=128						,     _a=0.1						,     _a=0.1						,     _a=0.1						,     _a=0.0_006						,     _a=512						,     _a=0.02						,     _a=1E-1_2						,     _a=1						,     _a=True						,     _a=1						,     _a=50_256						,     _a=50_256						,     **_a						,     )	->   Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Dict   		=						vocab_size
           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						action_weight
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						reward_weight
           SCREAMING_SNAKE_CASE__ :   int   		=						value_weight
           SCREAMING_SNAKE_CASE__ :   Dict   		=						max_position_embeddings
           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						block_size
           SCREAMING_SNAKE_CASE__ :   Any   		=						action_dim
           SCREAMING_SNAKE_CASE__ :   Tuple   		=						observation_dim
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						transition_dim
           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						learning_rate
           SCREAMING_SNAKE_CASE__ :   Tuple   		=						n_layer
           SCREAMING_SNAKE_CASE__ :   str   		=						n_head
           SCREAMING_SNAKE_CASE__ :   int   		=						n_embd
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						embd_pdrop
           SCREAMING_SNAKE_CASE__ :   Any   		=						attn_pdrop
           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						resid_pdrop
           SCREAMING_SNAKE_CASE__ :   int   		=						initializer_range
           SCREAMING_SNAKE_CASE__ :   int   		=						layer_norm_eps
           SCREAMING_SNAKE_CASE__ :   str   		=						kaiming_initializer_range
           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						use_cache
           super().__init__(pad_token_id=UpperCamelCase_						,     bos_token_id=UpperCamelCase_						,     eos_token_id=UpperCamelCase_						,     **UpperCamelCase_	)






"""simple docstring"""



import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase__          =	'path-to-your-trained-model'
UpperCamelCase__          =	StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')

UpperCamelCase__          =	'A photo of sks dog in a bucket'
UpperCamelCase__          =	pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('dog-bucket.png')

