




'''simple docstring'''





import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_SCREAMING_SNAKE_CASE							:       Dict						=			logging.get_logger(__name__)




@dataclass
class 			_snake_case		:
			lowerCAmelCase_					:				str							=   field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()							)}							)
			lowerCAmelCase_					:				str							=   field(
			    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}							)
			lowerCAmelCase_					:				int							=   field(
			    default=128						,						metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }						,						)
			lowerCAmelCase_					:				bool							=   field(
			    default=lowercase_						,						metadata={"help": "Overwrite the cached training and evaluation sets"}							)




			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				self.task_name.lower()




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Dict							=   "train"
			lowerCAmelCase_					:				Union[str, Any]							=   "dev"
			lowerCAmelCase_					:				Any							=   "test"




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				GlueDataTrainingArguments
			lowerCAmelCase_					:				str
			lowerCAmelCase_					:				List[InputFeatures]




			def __init__( self    ,							a__    ,							a__    ,							a__ = None    ,							a__ = Split.train    ,							a__ = None    ,							)    ->    Any:

										'''simple docstring'''




										warnings.warn(
										    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
										    "library. You can have a look at this example script for pointers: "
										    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"    ,							a__    ,							)
										snake_case_      =				args
										snake_case_      =				glue_processors[args.task_name]()
										snake_case_      =				glue_output_modes[args.task_name]
										if isinstance(a__    ,							a__							):
																	try:
																								snake_case_      =				Split[mode]
																	except KeyError:
																								raise KeyError("mode is not a valid split name"							)
        # Load data features from cache or dataset file
										snake_case_      =				os.path.join(
										    cache_dir if cache_dir is not None else args.data_dir    ,							F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'    ,							)
										snake_case_      =				self.processor.get_labels()
										if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
										    "RobertaTokenizer",
										    "RobertaTokenizerFast",
										    "XLMRobertaTokenizer",
										    "BartTokenizer",
										    "BartTokenizerFast",
										):
																	# HACK(label indices are swapped in RoBERTa pretrained model)
																	snake_case_ ,				snake_case_      =				label_list[2], label_list[1]
										snake_case_      =				label_list

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										snake_case_      =				cached_features_file + ".lock"
										with FileLock(a__							):
																	if os.path.exists(a__							) and not args.overwrite_cache:
																								snake_case_      =				time.time()
																								snake_case_      =				torch.load(a__							)
																								logger.info(
																								    F'Loading features from cached file {cached_features_file} [took %.3f s]'    ,							time.time() - start							)
																	else:
																								logger.info(F'Creating features from dataset file at {args.data_dir}'							)

																								if mode == Split.dev:
																															snake_case_      =				self.processor.get_dev_examples(args.data_dir							)
																								elif mode == Split.test:
																															snake_case_      =				self.processor.get_test_examples(args.data_dir							)
																								else:
																															snake_case_      =				self.processor.get_train_examples(args.data_dir							)
																								if limit_length is not None:
																															snake_case_      =				examples[:limit_length]
																								snake_case_      =				glue_convert_examples_to_features(
																								    a__    ,							a__    ,							max_length=args.max_seq_length    ,							label_list=a__    ,							output_mode=self.output_mode    ,							)
																								snake_case_      =				time.time()
																								torch.save(self.features    ,							a__							)
																								# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																								logger.info(
																								    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'							)




			def __len__( self							)    ->    int:

										'''simple docstring'''




										return len(self.features							)




			def __getitem__( self    ,							a__							)    ->    InputFeatures:

										'''simple docstring'''




										return self.features[i]




			def        lowerCAmelCase__    ( self							)    ->    Dict:

										'''simple docstring'''




										return self.label_list










'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       List[Any]						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "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 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Dict							=   "decision_transformer"
			lowerCAmelCase_					:				List[Any]							=   ["past_key_values"]
			lowerCAmelCase_					:				Tuple							=   {
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}




			def __init__( self    ,							a__=17    ,							a__=4    ,							a__=128    ,							a__=4_096    ,							a__=True    ,							a__=1    ,							a__=1_024    ,							a__=3    ,							a__=1    ,							a__=None    ,							a__="relu"    ,							a__=0.1    ,							a__=0.1    ,							a__=0.1    ,							a__=1e-5    ,							a__=0.0_2    ,							a__=True    ,							a__=True    ,							a__=50_256    ,							a__=50_256    ,							a__=False    ,							a__=False    ,							**a__    ,							)    ->    Optional[int]:

										'''simple docstring'''




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

										snake_case_      =				bos_token_id
										snake_case_      =				eos_token_id

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




