
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
					from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE							:str							 =				get_tests_dir('fixtures/test_sentencepiece.model')

SCREAMING_SNAKE_CASE							:Optional[Any]							 =				{'target_lang': 'fi', 'source_lang': 'en'}
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				'>>zh<<'
SCREAMING_SNAKE_CASE							:int							 =				'Helsinki-NLP/'

if is_torch_available():
					SCREAMING_SNAKE_CASE							:int							 =				'pt'
elif is_tf_available():
					SCREAMING_SNAKE_CASE							:List[str]							 =				'tf'
else:
					SCREAMING_SNAKE_CASE							:Tuple							 =				'jax'





@require_sentencepiece
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     MarianTokenizer
	snake_case_					    =     False
	snake_case_					    =     True






	def 				UpperCamelCase_  (       self					: Tuple ):
					super().setUp()
					__A   			= ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
					__A   			= dict(zip(A							,range(len(A ) ) ) )
					__A   			= Path(self.tmpdirname )
					save_json(A							,save_dir / VOCAB_FILES_NAMES["vocab"] )
					save_json(A							,save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"] )
					if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
									copyfile(A							,save_dir / VOCAB_FILES_NAMES["source_spm"] )
									copyfile(A							,save_dir / VOCAB_FILES_NAMES["target_spm"] )

					__A   			= MarianTokenizer.from_pretrained(self.tmpdirname )
					tokenizer.save_pretrained(self.tmpdirname )






	def 				UpperCamelCase_  (       self					: Dict							,**A					: Tuple ):
					return MarianTokenizer.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: int							,A					: Dict ):
					return (
					    "This is a test",
					    "This is a test",
					)






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= "</s>"
					__A   			= 0

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(A )							,A )
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(A )							,A )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= list(self.get_tokenizer().get_vocab().keys() )

					self.assertEqual(vocab_keys[0]							,"</s>" )
					self.assertEqual(vocab_keys[1]							,"<unk>" )
					self.assertEqual(vocab_keys[-1]							,"<pad>" )
					self.assertEqual(len(A )							,9 )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					self.assertEqual(self.get_tokenizer().vocab_size							,9 )






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de''' )
					__A   			= en_de_tokenizer(["I am a small frog"]							,return_tensors=A )
					self.assertIsInstance(A							,A )
					__A   			= [38, 1_21, 14, 6_97, 3_88_48, 0]
					self.assertListEqual(A							,batch.input_ids[0] )

					__A   			= tempfile.mkdtemp()
					en_de_tokenizer.save_pretrained(A )
					__A   			= [x.name for x in Path(A ).glob("*" )]
					self.assertIn("source.spm"							,A )
					MarianTokenizer.from_pretrained(A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.get_tokenizer()

					__A   			= tok(
					    ["I am a small frog" * 10_00, "I am a small frog"]							,padding=A							,truncation=A							,return_tensors=A )
					self.assertIsInstance(A							,A )
					self.assertEqual(batch.input_ids.shape							,(2, 5_12) )






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= self.get_tokenizer()
					__A   			= tok(["I am a tiny frog", "I am a small frog"]							,padding=A							,return_tensors=A )
					self.assertIsInstance(A							,A )
					self.assertEqual(batch_smaller.input_ids.shape							,(2, 10) )






	@slow
	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					# fmt: off
					__A   			= {"input_ids": [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=A							,model_name="Helsinki-NLP/opus-mt-en-de"							,revision="1a8c2263da11e68e50938f97e10cd57820bd504c"							,decode_kwargs={"use_source_tokenizer": True}							,)







	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs" )

					__A   			= "Tämä on testi"
					__A   			= "This is a test"

					__A   			= [76, 7, 20_47, 2]
					__A   			= [69, 12, 11, 9_40, 2]

					__A   			= tokenizer(A ).input_ids
					self.assertListEqual(A							,A )

					__A   			= tokenizer(text_target=A ).input_ids
					self.assertListEqual(A							,A )

					__A   			= tokenizer.decode(A							,skip_special_tokens=A )
					self.assertEqual(A							,A )


def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= [0] * len(a_						)
				__A   			= []
				__A   			= [1] * len(a_						)

				for values in graph.values():
								for i in values:
												indegree[i] += 1

				for i in range(len(a_						)						):
								if indegree[i] == 0:
												queue.append(a_						)

				while queue:
								__A   			= queue.pop(0						)
								for x in graph[vertex]:
												indegree[x] -= 1

												if long_dist[vertex] + 1 > long_dist[x]:
																__A   			= long_dist[vertex] + 1

												if indegree[x] == 0:
																queue.append(a_						)

				print(max(a_						)						)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE							:List[Any]							 =				{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
