"""simple docstring"""



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


_UpperCamelCase								=		get_tests_dir("fixtures/test_sentencepiece.model")

_UpperCamelCase								=		{"target_lang": "fi", "source_lang": "en"}
_UpperCamelCase								=		">>zh<<"
_UpperCamelCase								=		"Helsinki-NLP/"

if is_torch_available():
				_UpperCamelCase								=		"pt"
elif is_tf_available():
				_UpperCamelCase								=		"tf"
else:
				_UpperCamelCase								=		"jax"
@require_sentencepiece
class 						__UpperCAmelCase    (lowerCAmelCase_							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				MarianTokenizer
			_UpperCamelCase  :						Optional[int]					     =				False
			_UpperCamelCase  :						Union[str, Any]					     =				True





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										super().setUp()
										A__       :     Optional[Any]								=			["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
										A__       :     Dict								=			dict(zip(__lowerCAmelCase   ,      range(len(__lowerCAmelCase							)							)							)							)
										A__       :     str								=			Path(self.tmpdirname							)
										save_json(__lowerCAmelCase   ,      save_dir / VOCAB_FILES_NAMES["""vocab"""]							)
										save_json(__lowerCAmelCase   ,      save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]							)
										if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
																	copyfile(__lowerCAmelCase   ,      save_dir / VOCAB_FILES_NAMES["""source_spm"""]							)
																	copyfile(__lowerCAmelCase   ,      save_dir / VOCAB_FILES_NAMES["""target_spm"""]							)

										A__       :     Tuple								=			MarianTokenizer.from_pretrained(self.tmpdirname							)
										tokenizer.save_pretrained(self.tmpdirname							)





			def 		lowerCamelCase					(			self   ,      **snake_case_							):







										'''simple docstring'''


										return MarianTokenizer.from_pretrained(self.tmpdirname   ,      **__lowerCAmelCase							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return (
										    "This is a test",
										    "This is a test",
										)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			"""</s>"""
										A__       :     Dict								=			0

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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			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(__lowerCAmelCase							)   ,      9							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self.assertEqual(self.get_tokenizer().vocab_size   ,      9							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''							)
										A__       :     int								=			en_de_tokenizer(["""I am a small frog"""]   ,      return_tensors=__lowerCAmelCase							)
										self.assertIsInstance(__lowerCAmelCase   ,      __lowerCAmelCase							)
										A__       :     Dict								=			[38, 121, 14, 697, 38_848, 0]
										self.assertListEqual(__lowerCAmelCase   ,      batch.input_ids[0]							)

										A__       :     Optional[int]								=			tempfile.mkdtemp()
										en_de_tokenizer.save_pretrained(__lowerCAmelCase							)
										A__       :     Dict								=			[x.name for x in Path(__lowerCAmelCase							).glob("""*"""							)]
										self.assertIn("""source.spm"""   ,      __lowerCAmelCase							)
										MarianTokenizer.from_pretrained(__lowerCAmelCase							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			self.get_tokenizer()

										A__       :     Union[str, Any]								=			tok(
										    ["""I am a small frog""" * 1_000, """I am a small frog"""]   ,      padding=__lowerCAmelCase   ,      truncation=__lowerCAmelCase   ,      return_tensors=__lowerCAmelCase							)
										self.assertIsInstance(__lowerCAmelCase   ,      __lowerCAmelCase							)
										self.assertEqual(batch.input_ids.shape   ,      (2, 512)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			self.get_tokenizer()
										A__       :     Optional[Any]								=			tok(["""I am a tiny frog""", """I am a small frog"""]   ,      padding=__lowerCAmelCase   ,      return_tensors=__lowerCAmelCase							)
										self.assertIsInstance(__lowerCAmelCase   ,      __lowerCAmelCase							)
										self.assertEqual(batch_smaller.input_ids.shape   ,      (2, 10)							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			{"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """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=__lowerCAmelCase   ,      model_name="""Helsinki-NLP/opus-mt-en-de"""   ,      revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""   ,      decode_kwargs={"""use_source_tokenizer""": True}   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""							)

										A__       :     Optional[Any]								=			"""Tämä on testi"""
										A__       :     Any								=			"""This is a test"""

										A__       :     Tuple								=			[76, 7, 2_047, 2]
										A__       :     Optional[Any]								=			[69, 12, 11, 940, 2]

										A__       :     Optional[int]								=			tokenizer(__lowerCAmelCase							).input_ids
										self.assertListEqual(__lowerCAmelCase   ,      __lowerCAmelCase							)

										A__       :     Optional[Any]								=			tokenizer(text_target=__lowerCAmelCase							).input_ids
										self.assertListEqual(__lowerCAmelCase   ,      __lowerCAmelCase							)

										A__       :     str								=			tokenizer.decode(__lowerCAmelCase   ,      skip_special_tokens=__lowerCAmelCase							)
										self.assertEqual(__lowerCAmelCase   ,      __lowerCAmelCase							)






"""simple docstring"""


import heapq






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   []

  # for each node and his adjacency list add them and the rank of the node to queue
  # using heapq module the queue will be filled like a Priority Queue
  # heapq works with a min priority queue, so I used -1*len(v) to build it
  for key, value in graph.items():
    # O(log(n))
    heapq.heappush(lowercase ,[-1 * len(lowercase		), (key, value)]		)

  # chosen_vertices = set of chosen vertices
  _UpperCAmelCase    			=   set()

  # while queue isn't empty and there are still edges
  #   (queue[0][0] is the rank of the node with max rank)
  while queue and queue[0][0] != 0:
    # extract vertex with max rank from queue and add it to chosen_vertices
    _UpperCAmelCase    			=   heapq.heappop(lowercase		)[1][0]
    chosen_vertices.add(lowercase		)

    # Remove all arcs adjacent to argmax
    for elem in queue:
      # if v haven't adjacent node, skip
      if elem[0] == 0:
        continue
      # if argmax is reachable from elem
      # remove argmax from elem's adjacent list and update his rank
      if argmax in elem[1][1]:
        _UpperCAmelCase    			=   elem[1][1].index(lowercase		)
        del elem[1][1][index]
        elem[0] += 1
        # re-order the queue
    heapq.heapify(lowercase		)
  return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCAmelCase__			=    {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    print(F'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')
