


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


a : Optional[Any]		=				logging.get_logger(__name__)

a : Optional[int]		=				{
    '''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 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__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 ,       snake_case_=1_0_0 ,       snake_case_=5 ,       snake_case_=1 ,       snake_case_=1 ,       snake_case_=2_4_9 ,       snake_case_=6 ,       snake_case_=1_7 ,       snake_case_=2_5 ,       snake_case_=4 ,       snake_case_=4 ,       snake_case_=1_2_8 ,       snake_case_=0.1 ,       snake_case_=0.1 ,       snake_case_=0.1 ,       snake_case_=0.0_0_0_6 ,       snake_case_=5_1_2 ,       snake_case_=0.0_2 ,       snake_case_=1e-1_2 ,       snake_case_=1 ,       snake_case_=True ,       snake_case_=1 ,       snake_case_=5_0_2_5_6 ,       snake_case_=5_0_2_5_6 ,       **snake_case_ ,       )   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   vocab_size
								__lowercase 					=   action_weight
								__lowercase 					=   reward_weight
								__lowercase 					=   value_weight
								__lowercase 					=   max_position_embeddings
								__lowercase 					=   block_size
								__lowercase 					=   action_dim
								__lowercase 					=   observation_dim
								__lowercase 					=   transition_dim
								__lowercase 					=   learning_rate
								__lowercase 					=   n_layer
								__lowercase 					=   n_head
								__lowercase 					=   n_embd
								__lowercase 					=   embd_pdrop
								__lowercase 					=   attn_pdrop
								__lowercase 					=   resid_pdrop
								__lowercase 					=   initializer_range
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   kaiming_initializer_range
								__lowercase 					=   use_cache
								super().__init__(pad_token_id=snake_case_ ,       bos_token_id=snake_case_ ,       eos_token_id=snake_case_ ,       **snake_case_						)






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def 		lowercase_  (     *_UpperCamelCase  ):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):
						__lowercase 					=   list(_UpperCamelCase  )
			for i in range(len(_UpperCamelCase  )  ):
						__lowercase 					=   None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   [
			    '''CUDA out of memory.''',  # CUDA OOM
			    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
			    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
			]
			if isinstance(_UpperCamelCase       ,			_UpperCamelCase  ) and len(exception.args  ) == 1:
						return any(err in exception.args[0] for err in _statements  )
			return False





def 		lowercase_  (     _UpperCamelCase = None       ,			_UpperCamelCase = 1_28  ):


			'''simple docstring'''







			if function is None:
						return functools.partial(_UpperCamelCase       ,			starting_batch_size=_UpperCamelCase  )

			__lowercase 					=   starting_batch_size

			def decorator(*_UpperCamelCase       ,			**_UpperCamelCase  ):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						__lowercase 					=   list(inspect.signature(_UpperCamelCase  ).parameters.keys()  )
						# Guard against user error
						if len(_UpperCamelCase  ) < (len(_UpperCamelCase  ) + 1):
									__lowercase 					=   ''', '''.join([F'{arg}={value}' for arg, value in zip(params[1:]       ,			args[1:]  )]  )
									raise TypeError(
									    F'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'  )
						while True:
									if batch_size == 0:
												raise RuntimeError('''No executable batch size found, reached zero.'''  )
									try:
												return function(_UpperCamelCase       ,			*_UpperCamelCase       ,			**_UpperCamelCase  )
									except Exception as e:
												if should_reduce_batch_size(_UpperCamelCase  ):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator


