


import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


_snake_case  :	str			      =       logging.getLogger(__name__)

@dataclass(frozen=_lowerCAmelCase   )
class        a	:




							"""simple docstring"""


							__UpperCAmelCase					:							Tuple  						=						42
							__UpperCAmelCase					:							Dict  						=						42
							__UpperCAmelCase					:							Dict  						=						None
							__UpperCAmelCase					:							str  						=						None
							__UpperCAmelCase					:							int  						=						None

@dataclass(frozen=_lowerCAmelCase   )
class        a	:




							"""simple docstring"""


							__UpperCAmelCase					:							List[Any]  						=						42
							__UpperCAmelCase					:							Tuple  						=						None
							__UpperCAmelCase					:							Union[str, Any]  						=						None
							__UpperCAmelCase					:							Dict  						=						None
							__UpperCAmelCase					:							Union[str, Any]  						=						None


if is_torch_available():
			import torch
			from torch.utils.data import Dataset

			class        a	(_lowerCAmelCase   ):




										"""simple docstring"""


										__UpperCAmelCase					:							Tuple  						=						42

										def __init__(							self :						Optional[int]					,			lowerCamelCase :						str					,			lowerCamelCase :						PreTrainedTokenizer					,			lowerCamelCase :						str					,			lowerCamelCase :						Optional[int] = None					,			lowerCamelCase :						str=False					,			lowerCamelCase :						bool = False					,			)			->					Dict:
																	__snake_case	:    Union[str, Any]											=     hans_processors[task]()

																	__snake_case	:    int											=     os.path.join(
																	    __A					,			"cached_{}_{}_{}_{}".format(
																	        "dev" if evaluate else "train"					,			tokenizer.__class__.__name__					,			str(__A       )					,			__A					,			)					,			)
																	__snake_case	:    Dict											=     processor.get_labels()
																	if tokenizer.__class__ in (
																	    RobertaTokenizer,
																	    RobertaTokenizerFast,
																	    XLMRobertaTokenizer,
																	    BartTokenizer,
																	    BartTokenizerFast,
																	):
																								# HACK(label indices are swapped in RoBERTa pretrained model)
																								__snake_case				,    __snake_case	:    Tuple											=     label_list[2], label_list[1]
																	__snake_case	:    Any											=     label_list

																	# Make sure only the first process in distributed training processes the dataset,
																	# and the others will use the cache.
																	__snake_case	:    Any											=     cached_features_file + ".lock"
																	with FileLock(__A       ):
																								if os.path.exists(__A       ) and not overwrite_cache:
																															logger.info(F'Loading features from cached file {cached_features_file}'       )
																															__snake_case	:    List[str]											=     torch.load(__A       )
																								else:
																															logger.info(F'Creating features from dataset file at {data_dir}'       )

																															__snake_case	:    int											=     (
																															    processor.get_dev_examples(__A       ) if evaluate else processor.get_train_examples(__A       )
																															)

																															logger.info("Training examples: %s"					,			len(__A       )       )
																															__snake_case	:    Any											=     hans_convert_examples_to_features(__A					,			__A					,			__A					,			__A       )
																															logger.info("Saving features into cached file %s"					,			__A       )
																															torch.save(self.features					,			__A       )

										def __len__(							self :						Dict       )			->					Any:
																	return len(self.features       )

										def __getitem__(							self :						Any					,			lowerCamelCase :						List[Any]       )			->					InputFeatures:
																	return self.features[i]






										def 					__snake_case      (							self :						Tuple       )			->					Optional[Any]:
																	return self.label_list


if is_tf_available():
			import tensorflow as tf

			class        a	:




										"""simple docstring"""


										__UpperCAmelCase					:							Optional[int]  						=						42

										def __init__(							self :						Tuple					,			lowerCamelCase :						str					,			lowerCamelCase :						PreTrainedTokenizer					,			lowerCamelCase :						str					,			lowerCamelCase :						Optional[int] = 128					,			lowerCamelCase :						Dict=False					,			lowerCamelCase :						bool = False					,			)			->					Optional[int]:
																	__snake_case	:    int											=     hans_processors[task]()
																	__snake_case	:    List[Any]											=     processor.get_labels()
																	if tokenizer.__class__ in (
																	    RobertaTokenizer,
																	    RobertaTokenizerFast,
																	    XLMRobertaTokenizer,
																	    BartTokenizer,
																	    BartTokenizerFast,
																	):
																								# HACK(label indices are swapped in RoBERTa pretrained model)
																								__snake_case				,    __snake_case	:    List[Any]											=     label_list[2], label_list[1]
																	__snake_case	:    str											=     label_list

																	__snake_case	:    int											=     processor.get_dev_examples(__A       ) if evaluate else processor.get_train_examples(__A       )
																	__snake_case	:    List[Any]											=     hans_convert_examples_to_features(__A					,			__A					,			__A					,			__A       )

																	def gen():
																								for ex_index, ex in tqdm.tqdm(enumerate(self.features       )					,			desc="convert examples to features"       ):
																															if ex_index % 10000 == 0:
																																						logger.info("Writing example %d of %d" % (ex_index, len(__A       ))       )

																															yield (
																															    {
																															        "example_id": 0,
																															        "input_ids": ex.input_ids,
																															        "attention_mask": ex.attention_mask,
																															        "token_type_ids": ex.token_type_ids,
																															    },
																															    ex.label,
																															)

																	__snake_case	:    Any											=     tf.data.Dataset.from_generator(
																	    __A					,			(
																	        {
																	            "example_id": tf.intaa,
																	            "input_ids": tf.intaa,
																	            "attention_mask": tf.intaa,
																	            "token_type_ids": tf.intaa,
																	        },
																	        tf.intaa,
																	    )					,			(
																	        {
																	            "example_id": tf.TensorShape([]       ),
																	            "input_ids": tf.TensorShape([None, None]       ),
																	            "attention_mask": tf.TensorShape([None, None]       ),
																	            "token_type_ids": tf.TensorShape([None, None]       ),
																	        },
																	        tf.TensorShape([]       ),
																	    )					,			)

										def 					__snake_case      (							self :						int       )			->					Dict:
																	return self.dataset

										def __len__(							self :						List[str]       )			->					str:
																	return len(self.features       )

										def __getitem__(							self :						int					,			lowerCamelCase :						Optional[Any]       )			->					InputFeatures:
																	return self.features[i]






										def 					__snake_case      (							self :						List[str]       )			->					int:
																	return self.label_list

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Tuple					,			lowerCamelCase :						List[str]       )			->					str:
														return self._create_examples(self._read_tsv(os.path.join(__A					,			"heuristics_train_set.txt"       )       )					,			"train"       )

							def 					__snake_case      (							self :						Any					,			lowerCamelCase :						str       )			->					int:
														return self._create_examples(self._read_tsv(os.path.join(__A					,			"heuristics_evaluation_set.txt"       )       )					,			"dev"       )

							def 					__snake_case      (							self :						Tuple       )			->					Any:
														return ["contradiction", "entailment", "neutral"]






							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						str					,			lowerCamelCase :						Any       )			->					Any:
														__snake_case	:    List[Any]											=     []
														for i, line in enumerate(__A       ):
																					if i == 0:
																												continue
																					__snake_case	:    int											=     "%s-%s" % (set_type, line[0])
																					__snake_case	:    List[str]											=     line[5]
																					__snake_case	:    int											=     line[6]
																					__snake_case	:    List[Any]											=     line[7][2:] if line[7].startswith("ex"       ) else line[7]
																					__snake_case	:    Union[str, Any]											=     line[0]
																					examples.append(InputExample(guid=__A					,			text_a=__A					,			text_b=__A					,			label=__A					,			pairID=__A       )       )
														return examples







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			):
							__snake_case	:    Optional[Any]											=     {label: i for i, label in enumerate(_lowercase			)}

							__snake_case	:    Union[str, Any]											=     []
							for ex_index, example in tqdm.tqdm(enumerate(_lowercase			)							,			desc="convert examples to features"			):
														if ex_index % 1_0_0_0_0 == 0:
																					logger.info("Writing example %d" % (ex_index)			)

														__snake_case	:    List[Any]											=     tokenizer(
														    example.text_a							,			example.text_b							,			add_special_tokens=_lowercase							,			max_length=_lowercase							,			padding="max_length"							,			truncation=_lowercase							,			return_overflowing_tokens=_lowercase							,			)

														__snake_case	:    Union[str, Any]											=     label_map[example.label] if example.label in label_map else 0

														__snake_case	:    str											=     int(example.pairID			)

														features.append(InputFeatures(**_lowercase							,			label=_lowercase							,			pairID=_lowercase			)			)

							for i, example in enumerate(examples[:5]			):
														logger.info("*** Example ***"			)
														logger.info(F'guid: {example}'			)
														logger.info(F'features: {features[i]}'			)

							return features


_snake_case  :	Tuple			      =       {
    "hans": 3,
}

_snake_case  :	Dict			      =       {
    "hans": HansProcessor,
}





from __future__ import annotations

_snake_case  :	Union[str, Any]			      =       {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}

class        a	:




							"""simple docstring"""


							def __init__(							self :						Tuple					,			lowerCamelCase :						dict[str, list[str]]					,			lowerCamelCase :						str       )			->					None:
														__snake_case	:    Tuple											=     graph
														# mapping node to its parent in resulting breadth first tree
														__snake_case	:    dict[str, str | None]											=     {}
														__snake_case	:    Dict											=     source_vertex

							def 					__snake_case      (							self :						Optional[int]       )			->					None:
														__snake_case	:    Dict											=     {self.source_vertex}
														__snake_case	:    List[str]											=     None
														__snake_case	:    Optional[Any]											=     [self.source_vertex]  # first in first out queue

														while queue:
																					__snake_case	:    List[Any]											=     queue.pop(0       )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(lowerCamelCase       )
																																			__snake_case	:    Any											=     vertex
																																			queue.append(lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					str:
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														__snake_case	:    Optional[Any]											=     self.parent.get(lowerCamelCase       )
														if target_vertex_parent is None:
																					__snake_case	:    Optional[Any]											=     (
																					    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
																					)
																					raise ValueError(lowerCamelCase       )

														return self.shortest_path(lowerCamelCase       ) + F'->{target_vertex}'


if __name__ == "__main__":
			_snake_case  :	Optional[Any]			      =       Graph(graph, "G")
			g.breath_first_search()
			print(g.shortest_path("D"))
			print(g.shortest_path("G"))
			print(g.shortest_path("Foo"))

