

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


a_     =			get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
			from transformers.models.mbart.modeling_mbart import shift_tokens_right

a_     =			25_0004
a_     =			25_0020






@require_sentencepiece
@require_tokenizers
class _UpperCamelCase							(							__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=MBartaaTokenizer
			lowerCamelCase__							=MBartaaTokenizerFast
			lowerCamelCase__							=True
			lowerCamelCase__							=True
			def 							__UpperCamelCase       (	self							:       Any		)							->				int:

				"""simple docstring"""

				super().setUp()

				# We have a SentencePiece fixture for testing
				SCREAMING_SNAKE_CASE      :					Tuple    =				MBartaaTokenizer(a					,					src_lang="en_XX"					,					tgt_lang="ro_RO"					,					keep_accents=a		)
				tokenizer.save_pretrained(self.tmpdirname		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				"<s>"
				SCREAMING_SNAKE_CASE      :					Any    =				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							:       List[str]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				list(self.get_tokenizer().get_vocab().keys()		)

				self.assertEqual(vocab_keys[0]					,					"<s>"		)
				self.assertEqual(vocab_keys[1]					,					"<pad>"		)
				self.assertEqual(vocab_keys[-1]					,					"<mask>"		)
				self.assertEqual(len(a		)					,					1054		)
			def 							__UpperCamelCase       (	self							:       Any		)							->				Tuple:

				"""simple docstring"""

				self.assertEqual(self.get_tokenizer().vocab_size					,					1054		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				MBartaaTokenizer(a					,					src_lang="en_XX"					,					tgt_lang="ro_RO"					,					keep_accents=a		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				tokenizer.tokenize("This is a test"		)
				self.assertListEqual(a					,					["▁This", "▁is", "▁a", "▁t", "est"]		)

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

				SCREAMING_SNAKE_CASE      :					List[Any]    =				tokenizer.tokenize("I was born in 92000, and this is falsé."		)
				self.assertListEqual(
				    a					,					[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", "."]					,					)
				SCREAMING_SNAKE_CASE      :					Tuple    =				tokenizer.convert_tokens_to_ids(a		)
				self.assertListEqual(
				    a					,					[
				        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]
				    ]					,					)

				SCREAMING_SNAKE_CASE      :					Dict    =				tokenizer.convert_ids_to_tokens(a		)
				self.assertListEqual(
				    a					,					[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>", "."]					,					)
			@slow
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				{"input_ids": [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 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], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 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]], "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, 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], [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, 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="facebook/mbart-large-50"					,					revision="d3913889c59cd5c9e456b269c376325eabad57e2"					,					)






			def 							__UpperCamelCase       (	self							:       int		)							->				Optional[int]:

				"""simple docstring"""

				if not self.test_slow_tokenizer:
					# as we don't have a slow version, we can't compare the outputs between slow and fast versions
					return

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"		):
						SCREAMING_SNAKE_CASE      :					Dict    =				self.rust_tokenizer_class.from_pretrained(a					,					**a		)
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.tokenizer_class.from_pretrained(a					,					**a		)

						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				tempfile.mkdtemp()

						SCREAMING_SNAKE_CASE      :					str    =				tokenizer_r.save_pretrained(a		)
						SCREAMING_SNAKE_CASE      :					Tuple    =				tokenizer_p.save_pretrained(a		)

						# 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		)		)
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f		)
						self.assertSequenceEqual(a					,					a		)

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

						# Check special tokens are set accordingly on Rust and Python
						for key in tokenizer_pp.special_tokens_map:
							self.assertTrue(hasattr(a					,					a		)		)
							# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
							# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

						shutil.rmtree(a		)

						# Save tokenizer rust, legacy_format=True
						SCREAMING_SNAKE_CASE      :					int    =				tempfile.mkdtemp()

						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				tokenizer_r.save_pretrained(a					,					legacy_format=a		)
						SCREAMING_SNAKE_CASE      :					Any    =				tokenizer_p.save_pretrained(a		)

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

						# Checks everything loads correctly in the same way
						SCREAMING_SNAKE_CASE      :					Tuple    =				tokenizer_r.from_pretrained(a		)
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				tokenizer_p.from_pretrained(a		)

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

						shutil.rmtree(a		)

						# Save tokenizer rust, legacy_format=False
						SCREAMING_SNAKE_CASE      :					List[str]    =				tempfile.mkdtemp()

						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				tokenizer_r.save_pretrained(a					,					legacy_format=a		)
						SCREAMING_SNAKE_CASE      :					str    =				tokenizer_p.save_pretrained(a		)

						# 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
						SCREAMING_SNAKE_CASE      :					List[Any]    =				tokenizer_r.from_pretrained(a		)
						SCREAMING_SNAKE_CASE      :					Any    =				tokenizer_p.from_pretrained(a		)

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

						shutil.rmtree(a		)






@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							='facebook/mbart-large-50-one-to-many-mmt'
			lowerCamelCase__							=[
			    ' 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__							=[
			    '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.',
			]
			lowerCamelCase__							=[EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]
			@classmethod
			def 							__UpperCamelCase       (	cls							:       Any		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					MBartaaTokenizer    =				MBartaaTokenizer.from_pretrained(
				    cls.checkpoint_name					,					src_lang="en_XX"					,					tgt_lang="ro_RO"		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				1
				return cls
			def 							__UpperCamelCase       (	self							:       str		)							->				Optional[int]:

				"""simple docstring"""

				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]					,					25_0001		)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]					,					25_0004		)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]					,					25_0020		)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]					,					25_0038		)
			def 							__UpperCamelCase       (	self							:       Dict		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
				self.assertListEqual(self.expected_src_tokens					,					a		)
			def 							__UpperCamelCase       (	self							:       Any		)							->				Optional[int]:

				"""simple docstring"""

				self.assertIn(a					,					self.tokenizer.all_special_ids		)
				SCREAMING_SNAKE_CASE      :					Dict    =				[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
				SCREAMING_SNAKE_CASE      :					str    =				self.tokenizer.decode(a					,					skip_special_tokens=a		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.tokenizer.decode(generated_ids[1:]					,					skip_special_tokens=a		)
				self.assertEqual(a					,					a		)
				self.assertNotIn(self.tokenizer.eos_token					,					a		)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				List[str]:

				"""simple docstring"""

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

				"""simple docstring"""

				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]		)					,					[25_0053, 25_0001]		)
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				tempfile.mkdtemp()
				SCREAMING_SNAKE_CASE      :					Tuple    =				self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(a		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				MBartaaTokenizer.from_pretrained(a		)
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,					a		)
			@require_torch
			def 							__UpperCamelCase       (	self							:       Dict		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.tokenizer(self.src_text					,					text_target=self.tgt_text					,					padding=a					,					return_tensors="pt"		)
				SCREAMING_SNAKE_CASE      :					int    =				shift_tokens_right(batch["labels"]					,					self.tokenizer.pad_token_id		)

				# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
				assert batch.input_ids[1][0] == EN_CODE
				assert batch.input_ids[1][-1] == 2
				assert batch.labels[1][0] == RO_CODE
				assert batch.labels[1][-1] == 2
				assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
			@require_torch
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.tokenizer(
				    self.src_text					,					text_target=self.tgt_text					,					padding=a					,					truncation=a					,					max_length=len(self.expected_src_tokens		)					,					return_tensors="pt"					,					)
				SCREAMING_SNAKE_CASE      :					int    =				shift_tokens_right(batch["labels"]					,					self.tokenizer.pad_token_id		)

				self.assertIsInstance(a					,					a		)

				self.assertEqual((2, 14)					,					batch.input_ids.shape		)
				self.assertEqual((2, 14)					,					batch.attention_mask.shape		)
				SCREAMING_SNAKE_CASE      :					Dict    =				batch.input_ids.tolist()[0]
				self.assertListEqual(self.expected_src_tokens					,					a		)
				self.assertEqual(2					,					batch.decoder_input_ids[0, 0]		)  # decoder_start_token_id
				# 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 							__UpperCamelCase       (	self							:       int		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				self.tokenizer(self.src_text					,					padding=a					,					truncation=a					,					max_length=3					,					return_tensors="pt"		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.tokenizer(
				    text_target=self.tgt_text					,					padding=a					,					truncation=a					,					max_length=10					,					return_tensors="pt"		)
				SCREAMING_SNAKE_CASE      :					str    =				targets["input_ids"]
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				shift_tokens_right(a					,					self.tokenizer.pad_token_id		)

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






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				self.tokenizer._build_translation_inputs(
				    "A test"					,					return_tensors="pt"					,					src_lang="en_XX"					,					tgt_lang="ar_AR"		)

				self.assertEqual(
				    nested_simplify(a		)					,					{
				        # en_XX, A, test, EOS
				        "input_ids": [[25_0004, 62, 3034, 2]],
				        "attention_mask": [[1, 1, 1, 1]],
				        # ar_AR
				        "forced_bos_token_id": 25_0001,
				    }					,					)





from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray




def 			__magic_name__				( A		:					NDArray[floataa],  A		:					NDArray[floataa],  A		:					list[int],  A		:					int,  ):






			'''simple docstring'''







			a    ,		a    =			coefficient_matrix.shape
			a    ,		a    =			constant_matrix.shape

			if rowsa != colsa:
						a    =			F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if colsa != 1:
						a    =			F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if rowsa != rowsa:
						a    =			(
						    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
						    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
						)
						raise ValueError(A   )

			if len(A   ) != rowsa:
						a    =			(
						    "Number of initial values must be equal to number of rows in coefficient "
						    F"""matrix but received {len(A   )} and {rowsa}"""
						)
						raise ValueError(A   )

			if iterations <= 0:
						raise ValueError("Iterations must be at least 1"   )

			a    =			np.concatenate(
			    (coefficient_matrix, constant_matrix),  axis=1   )

			a    ,		a    =			table.shape

			strictly_diagonally_dominant(A   )

			# Iterates the whole matrix for given number of times
			for _ in range(A   ):
						a    =			[]
						for row in range(A   ):
									a    =			0
									for col in range(A   ):
												if col == row:
															a    =			table[row][col]
												elif col == cols - 1:
															a    =			table[row][col]
												else:
															temp += (-1) * table[row][col] * init_val[col]
									a    =			(temp + val) / denom
									new_val.append(A   )
						a    =			new_val

			return [float(A   ) for i in new_val]




def 			__magic_name__				( A		:					NDArray[floataa]   ):






			'''simple docstring'''







			a    ,		a    =			table.shape

			a    =			True

			for i in range(0,  A   ):
						a    =			0
						for j in range(0,  cols - 1   ):
									if i == j:
												continue
									else:
												total += table[i][j]

						if table[i][i] <= total:
									raise ValueError("Coefficient matrix is not strictly diagonally dominant"   )

			return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()






