



"""simple docstring"""






import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case							:   int      =    get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
	from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__snake_case							:   Dict      =    256_047
__snake_case							:   Optional[int]      =    256_145



@require_sentencepiece
@require_tokenizers
class 			A__   (  _UpperCamelCase  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	NllbTokenizer
	SCREAMING_SNAKE_CASE          =	NllbTokenizerFast
	SCREAMING_SNAKE_CASE          =	True
	SCREAMING_SNAKE_CASE          =	True
	SCREAMING_SNAKE_CASE          =	{}

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Union[str, Any]:

				"""simple docstring"""

				super().setUp()

				# We have a SentencePiece fixture for testing
				__lowerCAmelCase      :  int												=       NllbTokenizer(_UpperCAmelCase       ,			keep_accents=_UpperCAmelCase)
				tokenizer.save_pretrained(self.tmpdirname)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       NllbTokenizer(_UpperCAmelCase       ,			keep_accents=_UpperCAmelCase)

				__lowerCAmelCase      :  List[str]												=       tokenizer.tokenize("This is a test")
				self.assertListEqual(_UpperCAmelCase       ,			["▁This", "▁is", "▁a", "▁t", "est"])

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(_UpperCAmelCase)       ,			[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,			)

				__lowerCAmelCase      :  Optional[int]												=       tokenizer.tokenize("I was born in 92000, and this is falsé.")
				self.assertListEqual(
				    _UpperCAmelCase       ,			[
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "9",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "é",
				        ".",
				    ]       ,			)
				__lowerCAmelCase      :  Tuple												=       tokenizer.convert_tokens_to_ids(_UpperCAmelCase)
				self.assertListEqual(
				    _UpperCAmelCase       ,			[
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]       ,			)

				__lowerCAmelCase      :  Dict												=       tokenizer.convert_ids_to_tokens(_UpperCAmelCase)
				self.assertListEqual(
				    _UpperCAmelCase       ,			[
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "<unk>",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "<unk>",
				        ".",
				    ]       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										__lowerCAmelCase      :  List[str]												=       self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase       ,			**_UpperCAmelCase)
										__lowerCAmelCase      :  Tuple												=       self.tokenizer_class.from_pretrained(_UpperCAmelCase       ,			**_UpperCAmelCase)

										__lowerCAmelCase      :  Tuple												=       tempfile.mkdtemp()

										__lowerCAmelCase      :  str												=       tokenizer_r.save_pretrained(_UpperCAmelCase)
										__lowerCAmelCase      :  str												=       tokenizer_p.save_pretrained(_UpperCAmelCase)

										# Checks it save with the same files + the tokenizer.json file for the fast one
										self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
										__lowerCAmelCase      :  int												=       tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
										self.assertSequenceEqual(_UpperCAmelCase       ,			_UpperCAmelCase)

										# Checks everything loads correctly in the same way
										__lowerCAmelCase      :  Union[str, Any]												=       tokenizer_r.from_pretrained(_UpperCAmelCase)
										__lowerCAmelCase      :  Optional[Any]												=       tokenizer_p.from_pretrained(_UpperCAmelCase)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(_UpperCAmelCase       ,			_UpperCAmelCase))

										shutil.rmtree(_UpperCAmelCase)

										# Save tokenizer rust, legacy_format=True
										__lowerCAmelCase      :  Any												=       tempfile.mkdtemp()

										__lowerCAmelCase      :  Tuple												=       tokenizer_r.save_pretrained(_UpperCAmelCase       ,			legacy_format=_UpperCAmelCase)
										__lowerCAmelCase      :  Optional[int]												=       tokenizer_p.save_pretrained(_UpperCAmelCase)

										# Checks it save with the same files
										self.assertSequenceEqual(_UpperCAmelCase       ,			_UpperCAmelCase)

										# Checks everything loads correctly in the same way
										__lowerCAmelCase      :  Optional[Any]												=       tokenizer_r.from_pretrained(_UpperCAmelCase)
										__lowerCAmelCase      :  Optional[Any]												=       tokenizer_p.from_pretrained(_UpperCAmelCase)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(_UpperCAmelCase       ,			_UpperCAmelCase))

										shutil.rmtree(_UpperCAmelCase)

										# Save tokenizer rust, legacy_format=False
										__lowerCAmelCase      :  Dict												=       tempfile.mkdtemp()

										__lowerCAmelCase      :  Any												=       tokenizer_r.save_pretrained(_UpperCAmelCase       ,			legacy_format=_UpperCAmelCase)
										__lowerCAmelCase      :  Union[str, Any]												=       tokenizer_p.save_pretrained(_UpperCAmelCase)

										# Checks it saved the tokenizer.json file
										self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))

										# Checks everything loads correctly in the same way
										__lowerCAmelCase      :  int												=       tokenizer_r.from_pretrained(_UpperCAmelCase)
										__lowerCAmelCase      :  List[str]												=       tokenizer_p.from_pretrained(_UpperCAmelCase)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(_UpperCAmelCase       ,			_UpperCAmelCase))

										shutil.rmtree(_UpperCAmelCase)

	@require_torch
	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       str:

				"""simple docstring"""

				if not self.test_seqaseq:
							return

				__lowerCAmelCase      :  List[Any]												=       self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""):
										# Longer text that will definitely require truncation.
										__lowerCAmelCase      :  Any												=       [
										    ' UN Chief Says There Is No Military Solution in Syria',
										    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
										    ' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
										    ' will only worsen the violence and misery for millions of people.',
										]
										__lowerCAmelCase      :  Optional[Any]												=       [
										    'Şeful ONU declară că nu există o soluţie militară în Siria',
										    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
										    ' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
										    ' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
										]
										try:
													__lowerCAmelCase      :  Optional[int]												=       tokenizer.prepare_seqaseq_batch(
													    src_texts=_UpperCAmelCase       ,			tgt_texts=_UpperCAmelCase       ,			max_length=3       ,			max_target_length=10       ,			return_tensors="pt"       ,			src_lang="eng_Latn"       ,			tgt_lang="ron_Latn"       ,			)
										except NotImplementedError:
													return
										self.assertEqual(batch.input_ids.shape[1]       ,			3)
										self.assertEqual(batch.labels.shape[1]       ,			10)
										# max_target_length will default to max_length if not specified
										__lowerCAmelCase      :  List[str]												=       tokenizer.prepare_seqaseq_batch(
										    _UpperCAmelCase       ,			tgt_texts=_UpperCAmelCase       ,			max_length=3       ,			return_tensors="pt")
										self.assertEqual(batch.input_ids.shape[1]       ,			3)
										self.assertEqual(batch.labels.shape[1]       ,			3)

										__lowerCAmelCase      :  Dict												=       tokenizer.prepare_seqaseq_batch(
										    src_texts=_UpperCAmelCase       ,			max_length=3       ,			max_target_length=10       ,			return_tensors="pt")
										self.assertEqual(batch_encoder_only.input_ids.shape[1]       ,			3)
										self.assertEqual(batch_encoder_only.attention_mask.shape[1]       ,			3)
										self.assertNotIn("decoder_input_ids"       ,			_UpperCAmelCase)

	@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       str:

				"""simple docstring"""

				pass







	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Tuple:

				"""simple docstring"""

				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										__lowerCAmelCase      :  Dict												=       [AddedToken("<special>"       ,			lstrip=_UpperCAmelCase)]

										__lowerCAmelCase      :  Dict												=       self.rust_tokenizer_class.from_pretrained(
										    _UpperCAmelCase       ,			additional_special_tokens=_UpperCAmelCase       ,			**_UpperCAmelCase)
										__lowerCAmelCase      :  Tuple												=       tokenizer_r.encode("Hey this is a <special> token")

										__lowerCAmelCase      :  Dict												=       tokenizer_r.encode("<special>"       ,			add_special_tokens=_UpperCAmelCase)[0]

										self.assertTrue(special_token_id in r_output)

										if self.test_slow_tokenizer:
													__lowerCAmelCase      :  Tuple												=       self.rust_tokenizer_class.from_pretrained(
													    _UpperCAmelCase       ,			additional_special_tokens=_UpperCAmelCase       ,			**_UpperCAmelCase       ,			)
													__lowerCAmelCase      :  Any												=       self.tokenizer_class.from_pretrained(
													    _UpperCAmelCase       ,			additional_special_tokens=_UpperCAmelCase       ,			**_UpperCAmelCase)

													__lowerCAmelCase      :  Tuple												=       tokenizer_p.encode("Hey this is a <special> token")

													__lowerCAmelCase      :  Optional[Any]												=       tokenizer_cr.encode("Hey this is a <special> token")

													self.assertEqual(_UpperCAmelCase       ,			_UpperCAmelCase)
													self.assertEqual(_UpperCAmelCase       ,			_UpperCAmelCase)
													self.assertTrue(special_token_id in p_output)
													self.assertTrue(special_token_id in cr_output)






@require_torch
@require_sentencepiece
@require_tokenizers
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'facebook/nllb-200-distilled-600M'
	SCREAMING_SNAKE_CASE          =	[
	    ' UN Chief Says There Is No Military Solution in Syria',
	    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
	]
	SCREAMING_SNAKE_CASE          =	[
	    'Şeful ONU declară că nu există o soluţie militară în Siria',
	    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
	    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
	    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
	]
	SCREAMING_SNAKE_CASE          =	[
	    2_5_6_0_4_7,
	    1_6_2_9_7,
	    1_3_4_4_0_8,
	    8_1_6_5,
	    2_4_8_0_6_6,
	    1_4_7_3_4,
	    9_5_0,
	    1_1_3_5,
	    1_0_5_7_2_1,
	    3_5_7_3,
	    8_3,
	    2_7_3_5_2,
	    1_0_8,
	    4_9_4_8_6,
	    2,
	]

	@classmethod
	def 							_SCREAMING_SNAKE_CASE   (     cls:		Union[str, Any])				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  NllbTokenizer												=       NllbTokenizer.from_pretrained(
				    cls.checkpoint_name       ,			src_lang="eng_Latn"       ,			tgt_lang="ron_Latn")
				__lowerCAmelCase      :  Optional[Any]												=       1
				return cls

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       str:

				"""simple docstring"""

				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]       ,			25_6001)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]       ,			25_6002)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]       ,			25_6057)

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
				self.assertListEqual(self.expected_src_tokens       ,			_UpperCAmelCase)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       int:

				"""simple docstring"""

				self.assertIn(_UpperCAmelCase       ,			self.tokenizer.all_special_ids)
				# fmt: off
				__lowerCAmelCase      :  Dict												=       [RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
				# fmt: on

				__lowerCAmelCase      :  Tuple												=       self.tokenizer.decode(_UpperCAmelCase       ,			skip_special_tokens=_UpperCAmelCase)
				__lowerCAmelCase      :  List[str]												=       self.tokenizer.decode(generated_ids[1:]       ,			skip_special_tokens=_UpperCAmelCase)
				self.assertEqual(_UpperCAmelCase       ,			_UpperCAmelCase)
				self.assertNotIn(self.tokenizer.eos_token       ,			_UpperCAmelCase)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       ['this is gunna be a long sentence ' * 20]
				assert isinstance(src_text[0]       ,			_UpperCAmelCase)
				__lowerCAmelCase      :  List[str]												=       10
				__lowerCAmelCase      :  List[Any]												=       self.tokenizer(_UpperCAmelCase       ,			max_length=_UpperCAmelCase       ,			truncation=_UpperCAmelCase).input_ids[0]
				self.assertEqual(ids[-1]       ,			2)
				self.assertEqual(ids[0]       ,			_UpperCAmelCase)
				self.assertEqual(len(_UpperCAmelCase)       ,			_UpperCAmelCase)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       str:

				"""simple docstring"""

				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"])       ,			[25_6203, 3])

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

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       tempfile.mkdtemp()
				__lowerCAmelCase      :  Tuple												=       self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(_UpperCAmelCase)
				__lowerCAmelCase      :  Tuple												=       NllbTokenizer.from_pretrained(_UpperCAmelCase)
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,			_UpperCAmelCase)

	@require_torch
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.tokenizer(
				    self.src_text       ,			text_target=self.tgt_text       ,			padding=_UpperCAmelCase       ,			truncation=_UpperCAmelCase       ,			max_length=len(self.expected_src_tokens)       ,			return_tensors="pt"       ,			)
				__lowerCAmelCase      :  Tuple												=       shift_tokens_right(
				    batch["labels"]       ,			self.tokenizer.pad_token_id       ,			self.tokenizer.lang_code_to_id["ron_Latn"])

				self.assertIsInstance(_UpperCAmelCase       ,			_UpperCAmelCase)

				self.assertEqual((2, 15)       ,			batch.input_ids.shape)
				self.assertEqual((2, 15)       ,			batch.attention_mask.shape)
				__lowerCAmelCase      :  Optional[int]												=       batch.input_ids.tolist()[0]
				self.assertListEqual(self.expected_src_tokens       ,			_UpperCAmelCase)
				self.assertEqual(_UpperCAmelCase       ,			batch.decoder_input_ids[0, 0])  # EOS
				# Test that special tokens are reset
				self.assertEqual(self.tokenizer.prefix_tokens       ,			[EN_CODE])
				self.assertEqual(self.tokenizer.suffix_tokens       ,			[self.tokenizer.eos_token_id])

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       self.tokenizer(self.src_text       ,			padding=_UpperCAmelCase       ,			truncation=_UpperCAmelCase       ,			max_length=3       ,			return_tensors="pt")
				__lowerCAmelCase      :  str												=       self.tokenizer(
				    text_target=self.tgt_text       ,			padding=_UpperCAmelCase       ,			truncation=_UpperCAmelCase       ,			max_length=10       ,			return_tensors="pt")
				__lowerCAmelCase      :  Union[str, Any]												=       targets['input_ids']
				__lowerCAmelCase      :  Dict												=       shift_tokens_right(
				    _UpperCAmelCase       ,			self.tokenizer.pad_token_id       ,			decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]       ,			)

				self.assertEqual(batch.input_ids.shape[1]       ,			3)
				self.assertEqual(batch.decoder_input_ids.shape[1]       ,			10)

	@require_torch
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       self.tokenizer._build_translation_inputs(
				    "A test"       ,			return_tensors="pt"       ,			src_lang="eng_Latn"       ,			tgt_lang="fra_Latn")

				self.assertEqual(
				    nested_simplify(_UpperCAmelCase)       ,			{
				        # A, test, EOS, en_XX
				        "input_ids": [[25_6047, 70, 7356, 2]],
				        "attention_mask": [[1, 1, 1, 1]],
				        # ar_AR
				        "forced_bos_token_id": 25_6057,
				    }       ,			)







	@require_torch
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       True
				__lowerCAmelCase      :  str												=       self.tokenizer(
				    "UN Chief says there is no military solution in Syria"       ,			src_lang="eng_Latn"       ,			tgt_lang="fra_Latn")
				self.assertEqual(
				    inputs.input_ids       ,			[1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047])

				__lowerCAmelCase      :  Tuple												=       False
				__lowerCAmelCase      :  str												=       self.tokenizer(
				    "UN Chief says there is no military solution in Syria"       ,			src_lang="eng_Latn"       ,			tgt_lang="fra_Latn")
				self.assertEqual(
				    inputs.input_ids       ,			[25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2])




'''simple docstring'''

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      str | Literal[False]:
		_a     :      Optional[int]        =							list(lowerCAmelCase_  )
		_a     :      Optional[Any]        =							list(lowerCAmelCase_  )
		_a     :      Union[str, Any]        =							0
		for i in range(len(lowerCAmelCase_  )  ):
				if lista[i] != lista[i]:
						count += 1
						_a     :      Optional[int]        =							'_'
		if count > 1:
				return False
		else:
				return "".join(lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      list[str]:
		_a     :      Optional[int]        =							[]
		while True:
				_a     :      Any        =							['$'] * len(lowerCAmelCase_  )
				_a     :      List[str]        =							[]
				for i in range(len(lowerCAmelCase_  )  ):
						for j in range(i + 1						,			len(lowerCAmelCase_  )  ):
								_a     :      Optional[int]        =							compare_string(binary[i]						,			binary[j]  )
								if k is False:
										_a     :      Optional[Any]        =							'*'
										_a     :      Optional[Any]        =							'*'
										temp.append('X'  )
				for i in range(len(lowerCAmelCase_  )  ):
						if checka[i] == "$":
								pi.append(binary[i]  )
				if len(lowerCAmelCase_  ) == 0:
						return pi
				_a     :      Any        =							list(set(lowerCAmelCase_  )  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      int        =							[]
		for minterm in minterms:
				_a     :      Optional[int]        =							''
				for _ in range(lowerCAmelCase_  ):
						_a     :      Union[str, Any]        =							str(minterm % 2  ) + string
						minterm //= 2
				temp.append(lowerCAmelCase_  )
		return temp



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      bool:
		_a     :      int        =							list(lowerCAmelCase_  )
		_a     :      Union[str, Any]        =							list(lowerCAmelCase_  )
		_a     :      str        =							0
		for i in range(len(lowerCAmelCase_  )  ):
				if lista[i] != lista[i]:
						count_n += 1
		return count_n == count



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      List[Any]        =							[]
		_a     :      Optional[Any]        =							[0] * len(lowerCAmelCase_  )
		for i in range(len(chart[0]  )  ):
				_a     :      Union[str, Any]        =							0
				_a     :      int        =							-1
				for j in range(len(lowerCAmelCase_  )  ):
						if chart[j][i] == 1:
								count += 1
								_a     :      int        =							j
				if count == 1:
						_a     :      List[Any]        =							1
		for i in range(len(lowerCAmelCase_  )  ):
				if select[i] == 1:
						for j in range(len(chart[0]  )  ):
								if chart[i][j] == 1:
										for k in range(len(lowerCAmelCase_  )  ):
												_a     :      Any        =							0
						temp.append(prime_implicants[i]  )
		while True:
				_a     :      Union[str, Any]        =							0
				_a     :      List[Any]        =							-1
				_a     :      str        =							0
				for i in range(len(lowerCAmelCase_  )  ):
						_a     :      Union[str, Any]        =							chart[i].count(1  )
						if count_n > max_n:
								_a     :      Any        =							count_n
								_a     :      int        =							i

				if max_n == 0:
						return temp

				temp.append(prime_implicants[rem]  )

				for i in range(len(chart[0]  )  ):
						if chart[rem][i] == 1:
								for j in range(len(lowerCAmelCase_  )  ):
										_a     :      List[str]        =							0



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[list[int]]:
		_a     :      int        =							[[0 for x in range(len(lowerCAmelCase_  )  )] for x in range(len(lowerCAmelCase_  )  )]
		for i in range(len(lowerCAmelCase_  )  ):
				_a     :      str        =							prime_implicants[i].count('_'  )
				for j in range(len(lowerCAmelCase_  )  ):
						if is_for_table(prime_implicants[i]						,			binary[j]						,			lowerCAmelCase_  ):
								_a     :      Optional[Any]        =							1

		return chart



def      __lowerCamelCase     (      )			->      None:
		_a     :      Optional[int]        =							int(input('Enter the no. of variables\n'  )  )
		_a     :      List[Any]        =							[
		    float(lowerCAmelCase_  )
		    for x in input(
		        'Enter the decimal representation of Minterms \'Spaces Separated\'\n'  ).split()
		]
		_a     :      List[str]        =							decimal_to_binary(lowerCAmelCase_						,			lowerCAmelCase_  )

		_a     :      Dict        =							check(lowerCAmelCase_  )
		print('Prime Implicants are:'  )
		print(lowerCAmelCase_  )
		_a     :      List[Any]        =							prime_implicant_chart(lowerCAmelCase_						,			lowerCAmelCase_  )

		_a     :      int        =							selection(lowerCAmelCase_						,			lowerCAmelCase_  )
		print('Essential Prime Implicants are:'  )
		print(lowerCAmelCase_  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()




