


'''simple docstring'''


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 		lowerCAmelCase__	(	__snake_case			,				unittest.TestCase		):
			lowerCAmelCase       :		int				      =   RobertaTokenizer
			lowerCAmelCase       :		List[Any]				      =   RobertaTokenizerFast
			lowerCAmelCase       :		List[Any]				      =   True
			lowerCAmelCase       :		List[str]				      =   {"""cls_token""": """<s>"""}




			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->int:



					'''simple docstring'''







					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					_UpperCAmelCase						:				Dict	    =					[
					    "l",
					    "o",
					    "w",
					    "e",
					    "r",
					    "s",
					    "t",
					    "i",
					    "d",
					    "n",
					    "\u0120",
					    "\u0120l",
					    "\u0120n",
					    "\u0120lo",
					    "\u0120low",
					    "er",
					    "\u0120lowest",
					    "\u0120newer",
					    "\u0120wider",
					    "<unk>",
					]
					_UpperCAmelCase						:				List[str]	    =					dict(zip(UpperCamelCase__		,     range(len(UpperCamelCase__       )       )       )       )
					_UpperCAmelCase						:				Any	    =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
					_UpperCAmelCase						:				Any	    =					{"unk_token": "<unk>"}

					_UpperCAmelCase						:				Any	    =					os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["vocab_file"]       )
					_UpperCAmelCase						:				List[str]	    =					os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["merges_file"]       )
					with open(self.vocab_file		,     "w"		,     encoding="utf-8"       ) as fp:
							fp.write(json.dumps(UpperCamelCase__       ) + "\n"       )
					with open(self.merges_file		,     "w"		,     encoding="utf-8"       ) as fp:
							fp.write("\n".join(UpperCamelCase__       )       )




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     **lowerCamelCase__     :   Optional[Any]       )      ->str:



					'''simple docstring'''







					kwargs.update(self.special_tokens_map       )
					return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **UpperCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     **lowerCamelCase__     :   str       )      ->List[Any]:



					'''simple docstring'''







					kwargs.update(self.special_tokens_map       )
					return RobertaTokenizerFast.from_pretrained(self.tmpdirname		,     **UpperCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   Tuple       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					"lower newer"
					_UpperCAmelCase						:				Optional[Any]	    =					"lower newer"
					return input_text, output_text




			def 							lowerCAmelCase__			(						self     :   Any       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					self.tokenizer_class(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map       )
					_UpperCAmelCase						:				Optional[Any]	    =					"lower newer"
					_UpperCAmelCase						:				List[Any]	    =					["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
					_UpperCAmelCase						:				Dict	    =					tokenizer.tokenize(UpperCamelCase__       )  # , add_prefix_space=True)
					self.assertListEqual(UpperCamelCase__		,     UpperCamelCase__       )

					_UpperCAmelCase						:				Any	    =					tokens + [tokenizer.unk_token]
					_UpperCAmelCase						:				Tuple	    =					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__       )		,     UpperCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					self.get_tokenizer()

					self.assertListEqual(tokenizer.encode("Hello world!"		,     add_special_tokens=UpperCamelCase__       )		,     [0, 3_14_14, 2_32, 3_28, 2]       )
					self.assertListEqual(
					    tokenizer.encode("Hello world! cécé herlolip 418"		,     add_special_tokens=UpperCamelCase__       )		,     [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]		,     )




			@slow
			def 							lowerCAmelCase__			(						self     :   int       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.tokenizer_class.from_pretrained("roberta-base"       )

					_UpperCAmelCase						:				int	    =					tokenizer.encode("sequence builders"		,     add_special_tokens=UpperCamelCase__       )
					_UpperCAmelCase						:				List[Any]	    =					tokenizer.encode("multi-sequence build"		,     add_special_tokens=UpperCamelCase__       )

					_UpperCAmelCase						:				Optional[int]	    =					tokenizer.encode(
					    "sequence builders"		,     add_special_tokens=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__       )
					_UpperCAmelCase						:				Dict	    =					tokenizer.encode(
					    "sequence builders"		,     "multi-sequence build"		,     add_special_tokens=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__       )

					_UpperCAmelCase						:				str	    =					tokenizer.build_inputs_with_special_tokens(UpperCamelCase__       )
					_UpperCAmelCase						:				List[Any]	    =					tokenizer.build_inputs_with_special_tokens(UpperCamelCase__		,     UpperCamelCase__       )

					assert encoded_sentence == encoded_text_from_decode
					assert encoded_pair == encoded_pair_from_decode




			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					self.get_tokenizer()

					_UpperCAmelCase						:				Optional[int]	    =					"Encode this sequence."
					_UpperCAmelCase						:				List[str]	    =					tokenizer.byte_encoder[" ".encode("utf-8"       )[0]]

					# Testing encoder arguments
					_UpperCAmelCase						:				Dict	    =					tokenizer.encode(UpperCamelCase__		,     add_special_tokens=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__       )
					_UpperCAmelCase						:				Tuple	    =					tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
					self.assertNotEqual(UpperCamelCase__		,     UpperCamelCase__       )

					_UpperCAmelCase						:				Any	    =					tokenizer.encode(UpperCamelCase__		,     add_special_tokens=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__       )
					_UpperCAmelCase						:				List[str]	    =					tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
					self.assertEqual(UpperCamelCase__		,     UpperCamelCase__       )

					tokenizer.add_special_tokens({"bos_token": "<s>"}       )
					_UpperCAmelCase						:				Any	    =					tokenizer.encode(UpperCamelCase__		,     add_special_tokens=UpperCamelCase__       )
					_UpperCAmelCase						:				Dict	    =					tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
					self.assertNotEqual(UpperCamelCase__		,     UpperCamelCase__       )

					# Testing spaces after special tokens
					_UpperCAmelCase						:				Union[str, Any]	    =					"<mask>"
					tokenizer.add_special_tokens(
					    {"mask_token": AddedToken(UpperCamelCase__		,     lstrip=UpperCamelCase__		,     rstrip=UpperCamelCase__       )}       )  # mask token has a left space
					_UpperCAmelCase						:				str	    =					tokenizer.convert_tokens_to_ids(UpperCamelCase__       )

					_UpperCAmelCase						:				Optional[int]	    =					"Encode <mask> sequence"
					_UpperCAmelCase						:				str	    =					"Encode <mask>sequence"

					_UpperCAmelCase						:				str	    =					tokenizer.encode(UpperCamelCase__       )
					_UpperCAmelCase						:				Optional[Any]	    =					encoded.index(UpperCamelCase__       )
					_UpperCAmelCase						:				Optional[Any]	    =					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
					self.assertEqual(UpperCamelCase__		,     UpperCamelCase__       )

					_UpperCAmelCase						:				Dict	    =					tokenizer.encode(UpperCamelCase__       )
					_UpperCAmelCase						:				List[str]	    =					encoded.index(UpperCamelCase__       )
					_UpperCAmelCase						:				Dict	    =					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
					self.assertNotEqual(UpperCamelCase__		,     UpperCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->Dict:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->List[str]:



					'''simple docstring'''







					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
									_UpperCAmelCase						:				Union[str, Any]	    =					self.rust_tokenizer_class.from_pretrained(UpperCamelCase__		,     **UpperCamelCase__       )
									_UpperCAmelCase						:				Any	    =					self.tokenizer_class.from_pretrained(UpperCamelCase__		,     **UpperCamelCase__       )
									_UpperCAmelCase						:				Any	    =					"A, <mask> AllenNLP sentence."
									_UpperCAmelCase						:				Dict	    =					tokenizer_r.encode_plus(UpperCamelCase__		,     add_special_tokens=UpperCamelCase__		,     return_token_type_ids=UpperCamelCase__       )
									_UpperCAmelCase						:				Optional[int]	    =					tokenizer_p.encode_plus(UpperCamelCase__		,     add_special_tokens=UpperCamelCase__		,     return_token_type_ids=UpperCamelCase__       )

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r["token_type_ids"]       )		,     sum(tokens_p["token_type_ids"]       )       )

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r["attention_mask"]       ) / len(tokens_r["attention_mask"]       )		,     sum(tokens_p["attention_mask"]       ) / len(tokens_p["attention_mask"]       )		,     )

									_UpperCAmelCase						:				List[Any]	    =					tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]       )
									_UpperCAmelCase						:				Dict	    =					tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]       )

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p["input_ids"]		,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]       )
									self.assertSequenceEqual(tokens_r["input_ids"]		,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]       )

									self.assertSequenceEqual(
									    UpperCamelCase__		,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]       )
									self.assertSequenceEqual(
									    UpperCamelCase__		,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]       )




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



					'''simple docstring'''







					for trim_offsets, add_prefix_space in itertools.product([True, False]		,     repeat=2       ):
							_UpperCAmelCase						:				Any	    =					self.rust_tokenizer_class.from_pretrained(
							    self.tmpdirname		,     use_fast=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__		,     trim_offsets=UpperCamelCase__       )

							_UpperCAmelCase						:				List[Any]	    =					json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
							_UpperCAmelCase						:				Union[str, Any]	    =					json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

							self.assertEqual(pre_tokenizer_state["add_prefix_space"]		,     UpperCamelCase__       )

							self.assertEqual(post_processor_state["add_prefix_space"]		,     UpperCamelCase__       )
							self.assertEqual(post_processor_state["trim_offsets"]		,     UpperCamelCase__       )






			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->List[str]:



					'''simple docstring'''







					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
									_UpperCAmelCase						:				Any	    =					"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
									_UpperCAmelCase						:				str	    =					F"""{text_of_1_token} {text_of_1_token}"""

									_UpperCAmelCase						:				Union[str, Any]	    =					self.rust_tokenizer_class.from_pretrained(
									    UpperCamelCase__		,     use_fast=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__		,     trim_offsets=UpperCamelCase__       )
									_UpperCAmelCase						:				List[Any]	    =					tokenizer_r(UpperCamelCase__		,     return_offsets_mapping=UpperCamelCase__		,     add_special_tokens=UpperCamelCase__       )
									self.assertEqual(encoding.offset_mapping[0]		,     (0, len(UpperCamelCase__       ))       )
									self.assertEqual(
									    encoding.offset_mapping[1]		,     (len(UpperCamelCase__       ) + 1, len(UpperCamelCase__       ) + 1 + len(UpperCamelCase__       ))		,     )

									_UpperCAmelCase						:				Optional[int]	    =					self.rust_tokenizer_class.from_pretrained(
									    UpperCamelCase__		,     use_fast=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__		,     trim_offsets=UpperCamelCase__       )
									_UpperCAmelCase						:				Union[str, Any]	    =					tokenizer_r(UpperCamelCase__		,     return_offsets_mapping=UpperCamelCase__		,     add_special_tokens=UpperCamelCase__       )
									self.assertEqual(encoding.offset_mapping[0]		,     (0, len(UpperCamelCase__       ))       )
									self.assertEqual(
									    encoding.offset_mapping[1]		,     (len(UpperCamelCase__       ) + 1, len(UpperCamelCase__       ) + 1 + len(UpperCamelCase__       ))		,     )

									_UpperCAmelCase						:				List[str]	    =					self.rust_tokenizer_class.from_pretrained(
									    UpperCamelCase__		,     use_fast=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__		,     trim_offsets=UpperCamelCase__       )
									_UpperCAmelCase						:				Any	    =					tokenizer_r(UpperCamelCase__		,     return_offsets_mapping=UpperCamelCase__		,     add_special_tokens=UpperCamelCase__       )
									self.assertEqual(encoding.offset_mapping[0]		,     (0, len(UpperCamelCase__       ))       )
									self.assertEqual(
									    encoding.offset_mapping[1]		,     (len(UpperCamelCase__       ), len(UpperCamelCase__       ) + 1 + len(UpperCamelCase__       ))		,     )

									_UpperCAmelCase						:				int	    =					self.rust_tokenizer_class.from_pretrained(
									    UpperCamelCase__		,     use_fast=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__		,     trim_offsets=UpperCamelCase__       )
									_UpperCAmelCase						:				Optional[Any]	    =					tokenizer_r(UpperCamelCase__		,     return_offsets_mapping=UpperCamelCase__		,     add_special_tokens=UpperCamelCase__       )
									self.assertEqual(encoding.offset_mapping[0]		,     (0, len(UpperCamelCase__       ))       )
									self.assertEqual(
									    encoding.offset_mapping[1]		,     (len(UpperCamelCase__       ), len(UpperCamelCase__       ) + 1 + len(UpperCamelCase__       ))		,     )

									_UpperCAmelCase						:				str	    =					F""" {text}"""

									# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
									#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
									# )
									# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
									# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
									# self.assertEqual(
									#     encoding.offset_mapping[1],
									#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
									# )

									_UpperCAmelCase						:				Tuple	    =					self.rust_tokenizer_class.from_pretrained(
									    UpperCamelCase__		,     use_fast=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__		,     trim_offsets=UpperCamelCase__       )
									_UpperCAmelCase						:				str	    =					tokenizer_r(UpperCamelCase__		,     return_offsets_mapping=UpperCamelCase__		,     add_special_tokens=UpperCamelCase__       )
									self.assertEqual(encoding.offset_mapping[0]		,     (1, 1 + len(UpperCamelCase__       ))       )
									self.assertEqual(
									    encoding.offset_mapping[1]		,     (1 + len(UpperCamelCase__       ) + 1, 1 + len(UpperCamelCase__       ) + 1 + len(UpperCamelCase__       ))		,     )

									_UpperCAmelCase						:				Union[str, Any]	    =					self.rust_tokenizer_class.from_pretrained(
									    UpperCamelCase__		,     use_fast=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__		,     trim_offsets=UpperCamelCase__       )
									_UpperCAmelCase						:				str	    =					tokenizer_r(UpperCamelCase__		,     return_offsets_mapping=UpperCamelCase__		,     add_special_tokens=UpperCamelCase__       )
									self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(UpperCamelCase__       ))       )
									self.assertEqual(
									    encoding.offset_mapping[1]		,     (1 + len(UpperCamelCase__       ), 1 + len(UpperCamelCase__       ) + 1 + len(UpperCamelCase__       ))		,     )

									_UpperCAmelCase						:				List[str]	    =					self.rust_tokenizer_class.from_pretrained(
									    UpperCamelCase__		,     use_fast=UpperCamelCase__		,     add_prefix_space=UpperCamelCase__		,     trim_offsets=UpperCamelCase__       )
									_UpperCAmelCase						:				List[Any]	    =					tokenizer_r(UpperCamelCase__		,     return_offsets_mapping=UpperCamelCase__		,     add_special_tokens=UpperCamelCase__       )
									self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(UpperCamelCase__       ))       )
									self.assertEqual(
									    encoding.offset_mapping[1]		,     (1 + len(UpperCamelCase__       ), 1 + len(UpperCamelCase__       ) + 1 + len(UpperCamelCase__       ))		,     )






"""simple docstring"""


__A						   =			{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	list[str]:
		__lowerCAmelCase:       Tuple          =			set()
		# keep track of all the paths to be checked
		__lowerCAmelCase:       Optional[Any]          =			[[start]]

		# return path if start is goal
		if start == goal:
				return [start]

		# keeps looping until all possible paths have been checked
		while queue:
				# pop the first path from the queue
				__lowerCAmelCase:       str          =			queue.pop(0 )
				# get the last node from the path
				__lowerCAmelCase:       List[Any]          =			path[-1]
				if node not in explored:
						__lowerCAmelCase:       str          =			graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								__lowerCAmelCase:       Optional[int]          =			list(__SCREAMING_SNAKE_CASE )
								new_path.append(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(__SCREAMING_SNAKE_CASE )

    # in case there's no path between the 2 nodes
		return []






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		__lowerCAmelCase:       Dict          =			[start]
		__lowerCAmelCase:       Any          =			set(__SCREAMING_SNAKE_CASE )
		# Keep tab on distances from `start` node.
		__lowerCAmelCase:       int          =			{start: 0, target: -1}
		while queue:
				__lowerCAmelCase:       Optional[Any]          =			queue.pop(0 )
				if node == target:
						__lowerCAmelCase:       Dict          =			(
						    dist[node] if dist[target] == -1 else min(dist[target]					, dist[node] )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								__lowerCAmelCase:       List[str]          =			dist[node] + 1
		return dist[target]


if __name__ == "__main__":
			print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
			print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4


