



'''simple docstring'''

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


snake_case_     :   Any			=     logging.get_logger(__name__)

snake_case_     :   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 	a__     (				_lowercase	):
	__magic_name__		:    Dict	    =     "trajectory_transformer"
	__magic_name__		:    int	    =     ["past_key_values"]
	__magic_name__		:    List[Any]	    =     {
	    "hidden_size": "n_embd",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}


	def __init__(self : Union[str, Any],					__UpperCAmelCase : Dict=100,					__UpperCAmelCase : List[str]=5,					__UpperCAmelCase : List[Any]=1,					__UpperCAmelCase : Dict=1,					__UpperCAmelCase : int=249,					__UpperCAmelCase : Optional[Any]=6,					__UpperCAmelCase : List[str]=17,					__UpperCAmelCase : str=25,					__UpperCAmelCase : Dict=4,					__UpperCAmelCase : int=4,					__UpperCAmelCase : List[Any]=128,					__UpperCAmelCase : Any=0.1,					__UpperCAmelCase : List[Any]=0.1,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : List[str]=0.0006,					__UpperCAmelCase : Tuple=512,					__UpperCAmelCase : int=0.02,					__UpperCAmelCase : Tuple=1e-12,					__UpperCAmelCase : Dict=1,					__UpperCAmelCase : Tuple=True,					__UpperCAmelCase : int=1,					__UpperCAmelCase : Any=50256,					__UpperCAmelCase : Dict=50256,					**__UpperCAmelCase : List[str],					)     ->			Optional[int]:

					"""simple docstring"""

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









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :str       ):
				SCREAMING_SNAKE_CASE  :						int      					= 0
				if start < end:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= randint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						List[str]      					= a[end]
								SCREAMING_SNAKE_CASE  :						List[str]      					= a[pivot]
								SCREAMING_SNAKE_CASE  :						Dict      					= temp

								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= _in_place_partition(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								count += _in_place_quick_sort(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	p - 1       )
								count += _in_place_quick_sort(_SCREAMING_SNAKE_CASE    ,	p + 1    ,	_SCREAMING_SNAKE_CASE       )
				return count






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				SCREAMING_SNAKE_CASE  :						Any      					= 0
				SCREAMING_SNAKE_CASE  :						Tuple      					= randint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= a[end]
				SCREAMING_SNAKE_CASE  :						int      					= a[pivot]
				SCREAMING_SNAKE_CASE  :						Tuple      					= temp
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= start - 1
				for index in range(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ):
								count += 1
								if a[index] < a[end]:  # check if current val is less than pivot value
												SCREAMING_SNAKE_CASE  :						Tuple      					= new_pivot_index + 1
												SCREAMING_SNAKE_CASE  :						Dict      					= a[new_pivot_index]
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= a[index]
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= temp

				SCREAMING_SNAKE_CASE  :						List[Any]      					= a[new_pivot_index + 1]
				SCREAMING_SNAKE_CASE  :						Any      					= a[end]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= temp
				return new_pivot_index + 1, count


snake_case_			=     TemporaryFile()
snake_case_			=     100  # 1000 elements are to be sorted


snake_case_			,				snake_case_			=     0, 1  # mean and standard deviation
snake_case_			=     np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
snake_case_			=     np.load(outfile)
snake_case_			=     len(M) - 1
snake_case_			=     _in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)




print(z)




