





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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """edbeeching/decision-transformer-gym-hopper-medium""": (
        """https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Optional[int]			=			"decision_transformer"
     __snake_case    :	Any			=			["past_key_values"]
     __snake_case    :	Optional[int]			=			{
         "max_position_embeddings": "n_positions",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }






     def __init__(							self						:    Union[str, Any]						,lowerCamelCase__						:    int=17						,lowerCamelCase__						:    Dict=4						,lowerCamelCase__						:    int=128						,lowerCamelCase__						:    Tuple=4096						,lowerCamelCase__						:    Tuple=True						,lowerCamelCase__						:    List[Any]=1						,lowerCamelCase__						:    Optional[int]=1024						,lowerCamelCase__						:    Optional[Any]=3						,lowerCamelCase__						:    Union[str, Any]=1						,lowerCamelCase__						:    Optional[int]=None						,lowerCamelCase__						:    Dict="relu"						,lowerCamelCase__						:    Optional[int]=0.1						,lowerCamelCase__						:    Optional[Any]=0.1						,lowerCamelCase__						:    Any=0.1						,lowerCamelCase__						:    List[Any]=1e-5						,lowerCamelCase__						:    Optional[Any]=0.02						,lowerCamelCase__						:    Dict=True						,lowerCamelCase__						:    str=True						,lowerCamelCase__						:    Dict=50256						,lowerCamelCase__						:    str=50256						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    List[Any]=False						,**lowerCamelCase__						:    Optional[Any]						,)     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						state_dim
           SCREAMING_SNAKE_CASE		=						act_dim
           SCREAMING_SNAKE_CASE		=						hidden_size
           SCREAMING_SNAKE_CASE		=						max_ep_len
           SCREAMING_SNAKE_CASE		=						action_tanh
           SCREAMING_SNAKE_CASE		=						vocab_size
           SCREAMING_SNAKE_CASE		=						n_positions
           SCREAMING_SNAKE_CASE		=						n_layer
           SCREAMING_SNAKE_CASE		=						n_head
           SCREAMING_SNAKE_CASE		=						n_inner
           SCREAMING_SNAKE_CASE		=						activation_function
           SCREAMING_SNAKE_CASE		=						resid_pdrop
           SCREAMING_SNAKE_CASE		=						embd_pdrop
           SCREAMING_SNAKE_CASE		=						attn_pdrop
           SCREAMING_SNAKE_CASE		=						layer_norm_epsilon
           SCREAMING_SNAKE_CASE		=						initializer_range
           SCREAMING_SNAKE_CASE		=						scale_attn_weights
           SCREAMING_SNAKE_CASE		=						use_cache
           SCREAMING_SNAKE_CASE		=						scale_attn_by_inverse_layer_idx
           SCREAMING_SNAKE_CASE		=						reorder_and_upcast_attn

           SCREAMING_SNAKE_CASE		=						bos_token_id
           SCREAMING_SNAKE_CASE		=						eos_token_id

           super().__init__(bos_token_id=lowerCamelCase__						,eos_token_id=lowerCamelCase__						,**lowerCamelCase__       )









import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)



def    __lowercase   (      _SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   List[str]:


      '''simple docstring'''


      return field(default_factory=lambda: default ,						metadata=_SCREAMING_SNAKE_CASE	)



@dataclass
class 		UpperCamelCase__ :



     '''simple docstring'''

     __snake_case    :	List[str]			=			list_field(
         default=[]							,      metadata={
             "help": (
                 "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
                 " of all available models"
             )
         }							,      )

     __snake_case    :	List[int]			=			list_field(
         default=[8]							,      metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}   )

     __snake_case    :	List[int]			=			list_field(
         default=[8, 32, 128, 512]							,      metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}							,      )

     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Use FP16 to accelerate inference."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Benchmark training of model"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Verbose memory tracing"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
         }							,      )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Trace memory line by line"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save result to a CSV file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save all print statements in a log file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to print environment information"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
                 " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
                 " for debugging / testing and on TPU."
             )
         }							,      )
     __snake_case    :	str			=			field(
         default=F"inference_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"inference_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"env_info_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving environment information."}							,      )
     __snake_case    :	str			=			field(
         default=F"log_{round(time()   )}.csv"							,      metadata={"help": "Log filename used if print statements are saved in log."}							,      )
     __snake_case    :	int			=			field(default=3							,      metadata={"help": "Times an experiment will be run."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                 " model weights."
             )
         }							,      )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Dict:




           '''simple docstring'''


           warnings.warn(
               F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
               """ are deprecated in general and it is advised to use external Benchmarking libraries """
               """ to benchmark Transformer models."""						,lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Any:




           '''simple docstring'''


           return json.dumps(dataclasses.asdict(self       )						,indent=2       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           if len(self.models       ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""       )
           return self.models






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		int:




           '''simple docstring'''


           if not self.multi_process:
                 return False
           elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""       )
                 return False
           else:
                 return True


