




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


UpperCamelCase__    			=			logging.get_logger(__name__)
@dataclass
class 			a__    :
						_a							:    str					=    field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()      )}      )
						_a							:    str					=    field(
						    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}      )
						_a							:    int					=    field(
						    default=1_2_8 ,				metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    } ,				)
						_a							:    bool					=    field(
						    default=snake_case__ ,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.task_name.lower()






class 			a__    (					snake_case__      ):
						_a							:    Union[str, Any]					=    """train"""
						_a							:    List[str]					=    """dev"""
						_a							:    int					=    """test"""






class 			a__    (					snake_case__      ):
						_a							:    GlueDataTrainingArguments
						_a							:    str
						_a							:    List[InputFeatures]






						def __init__(   self		,			_A		,			_A		,			_A = None		,			_A = Split.train		,			_A = None		,			):




											"""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		,			)
											__lowerCAmelCase				   =       args
											__lowerCAmelCase				   =       glue_processors[args.task_name]()
											__lowerCAmelCase				   =       glue_output_modes[args.task_name]
											if isinstance(_A		,			_A			):
																try:
																					__lowerCAmelCase				   =       Split[mode]
																except KeyError:
																					raise KeyError("mode is not a valid split name"			)
        # Load data features from cache or dataset file
											__lowerCAmelCase				   =       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}"""		,			)
											__lowerCAmelCase				   =       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)
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       label_list[2], label_list[1]
											__lowerCAmelCase				   =       label_list

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											__lowerCAmelCase				   =       cached_features_file + ".lock"
											with FileLock(_A			):
																if os.path.exists(_A			) and not args.overwrite_cache:
																					__lowerCAmelCase				   =       time.time()
																					__lowerCAmelCase				   =       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:
																										__lowerCAmelCase				   =       self.processor.get_dev_examples(args.data_dir			)
																					elif mode == Split.test:
																										__lowerCAmelCase				   =       self.processor.get_test_examples(args.data_dir			)
																					else:
																										__lowerCAmelCase				   =       self.processor.get_train_examples(args.data_dir			)
																					if limit_length is not None:
																										__lowerCAmelCase				   =       examples[:limit_length]
																					__lowerCAmelCase				   =       glue_convert_examples_to_features(
																					    _A		,			_A		,			max_length=args.max_seq_length		,			label_list=_A		,			output_mode=self.output_mode		,			)
																					__lowerCAmelCase				   =       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			):




											"""simple docstring"""



											return len(self.features			)






						def __getitem__(   self		,			_A			):




											"""simple docstring"""



											return self.features[i]






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.label_list










from queue import PriorityQueue
from typing import Any

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				PriorityQueue      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				float | int      ,      ):
					for nxt, d in graph[v]:
										if nxt in visited_forward:
															continue
										__lowerCAmelCase				   =       cst_fwd.get(SCREAMING_SNAKE_CASE_      ,      np.inf     )
										__lowerCAmelCase				   =       cst_fwd[v] + d
										if new_cost_f < old_cost_f:
															queue.put((new_cost_f, nxt)     )
															__lowerCAmelCase				   =       new_cost_f
															__lowerCAmelCase				   =       v
										if nxt in visited_backward:
															if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																				__lowerCAmelCase				   =       cst_fwd[v] + d + cst_bwd[nxt]
					return shortest_distance







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict     ):
					__lowerCAmelCase				   =       -1

					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       {source: 0}
					__lowerCAmelCase				   =       {destination: 0}
					__lowerCAmelCase				   =       {source: None}
					__lowerCAmelCase				   =       {destination: None}
					__lowerCAmelCase				   =       PriorityQueue()
					__lowerCAmelCase				   =       PriorityQueue()

					__lowerCAmelCase				   =       np.inf

					queue_forward.put((0, source)     )
					queue_backward.put((0, destination)     )

					if source == destination:
										return 0

					while not queue_forward.empty() and not queue_backward.empty():
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_forward.get()
										visited_forward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_backward.get()
										visited_backward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
															break

					if shortest_distance != np.inf:
										__lowerCAmelCase				   =       shortest_distance
					return shortest_path_distance


UpperCamelCase__    			=			{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
UpperCamelCase__    			=			{
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()




