"""simple docstring"""


import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer




class  UpperCamelCase					(				__a   ):







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					tempfile.mkdtemp()
            snake_case_					       =					5

            # Realm tok
            snake_case_					       =					[
                '[UNK]',
                '[CLS]',
                '[SEP]',
                '[PAD]',
                '[MASK]',
                'test',
                'question',
                'this',
                'is',
                'the',
                'first',
                'second',
                'third',
                'fourth',
                'fifth',
                'record',
                'want',
                '##want',
                '##ed',
                'wa',
                'un',
                'runn',
                '##ing',
                ',',
                'low',
                'lowest',
            ]
            snake_case_					       =					os.path.join(self.tmpdirname,							'realm_tokenizer')
            os.makedirs(lowerCAmelCase__,							exist_ok=lowerCAmelCase__)
            snake_case_					       =					os.path.join(lowerCAmelCase__,							VOCAB_FILES_NAMES['vocab_file'])
            with open(self.vocab_file,							'w',							encoding='utf-8') as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

            snake_case_					       =					os.path.join(self.tmpdirname,							'realm_block_records')
            os.makedirs(lowerCAmelCase__,							exist_ok=lowerCAmelCase__)







       def 		a_						( self)      ->      List[str]:
            return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname,							'realm_tokenizer'))







       def 		a_						( self)      ->      Optional[Any]:
            shutil.rmtree(self.tmpdirname)







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					RealmConfig(num_block_records=self.num_block_records)
            return config







       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					Dataset.from_dict(
                {
                    'id': ['0', '1'],
                    'question': ['foo', 'bar'],
                    'answers': [['Foo', 'Bar'], ['Bar']],
                })
            return dataset







       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					np.array(
                [
                    b'This is the first record',
                    b'This is the second record',
                    b'This is the third record',
                    b'This is the fourth record',
                    b'This is the fifth record',
                    b'This is a longer longer longer record',
                ],							dtype=lowerCAmelCase__,							)
            return block_records







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					RealmRetriever(
                block_records=self.get_dummy_block_records(),							tokenizer=self.get_tokenizer(),							)
            return retriever







       def 		a_						( self)      ->      List[Any]:
            snake_case_					       =					self.get_config()
            snake_case_					       =					self.get_dummy_retriever()
            snake_case_					       =					retriever.tokenizer

            snake_case_					       =					np.array([0, 3],							dtype='long')
            snake_case_					       =					tokenizer(['Test question']).input_ids
            snake_case_					       =					tokenizer(
                ['the fourth'],							add_special_tokens=lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__,							return_attention_mask=lowerCAmelCase__,							).input_ids
            snake_case_					       =					config.reader_seq_len

            snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_					       =					retriever(
                lowerCAmelCase__,							lowerCAmelCase__,							answer_ids=lowerCAmelCase__,							max_length=lowerCAmelCase__,							return_tensors='np')

            self.assertEqual(len(lowerCAmelCase__),							2)
            self.assertEqual(len(lowerCAmelCase__),							2)
            self.assertEqual(len(lowerCAmelCase__),							2)
            self.assertEqual(concat_inputs.input_ids.shape,							(2, 10))
            self.assertEqual(concat_inputs.attention_mask.shape,							(2, 10))
            self.assertEqual(concat_inputs.token_type_ids.shape,							(2, 10))
            self.assertEqual(concat_inputs.special_tokens_mask.shape,							(2, 10))
            self.assertEqual(
                tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]),							['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]'],							)
            self.assertEqual(
                tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]),							['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]'],							)







       def 		a_						( self)      ->      str:
            snake_case_					       =					self.get_config()
            snake_case_					       =					self.get_dummy_retriever()
            snake_case_					       =					retriever.tokenizer

            snake_case_					       =					np.array([0, 3, 5],							dtype='long')
            snake_case_					       =					tokenizer(['Test question']).input_ids
            snake_case_					       =					tokenizer(
                ['the fourth', 'longer longer'],							add_special_tokens=lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__,							return_attention_mask=lowerCAmelCase__,							).input_ids
            snake_case_					       =					config.reader_seq_len

            snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_					       =					retriever(
                lowerCAmelCase__,							lowerCAmelCase__,							answer_ids=lowerCAmelCase__,							max_length=lowerCAmelCase__,							return_tensors='np')

            self.assertEqual([False, True, True],							lowerCAmelCase__)
            self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]],							lowerCAmelCase__)
            self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]],							lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					self.get_dummy_retriever()
            retriever.save_pretrained(os.path.join(self.tmpdirname,							'realm_block_records'))

            # Test local path
            snake_case_					       =					retriever.from_pretrained(os.path.join(self.tmpdirname,							'realm_block_records'))
            self.assertEqual(retriever.block_records[0],							b'This is the first record')

            # Test mocked remote path
            with patch('transformers.models.realm.retrieval_realm.hf_hub_download') as mock_hf_hub_download:
                 snake_case_					       =					os.path.join(
                     os.path.join(self.tmpdirname,							'realm_block_records'),							_REALM_BLOCK_RECORDS_FILENAME)
                 snake_case_					       =					RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa')

            self.assertEqual(retriever.block_records[0],							b'This is the first record')







'''simple docstring'''







from queue import PriorityQueue
from typing import Any

import numpy as np





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





def 						snake_case_							(_a	:							str    ,   _a	:							str    ,   _a	:							dict    ,   _a	:							dict     ):
    UpperCAmelCase			       =		-1

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

    UpperCAmelCase			       =		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():
        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_forward.get()
        visited_forward.add(_a     )

        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_backward.get()
        visited_backward.add(_a     )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

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

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


A     ={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A     ={
    '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()




