


"""simple docstring"""







import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class  UpperCamelCase_	( UpperCamelCase			,  unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  Union[str, Any]    					=			FunnelTokenizer
	snake_case__				:  List[str]    					=			FunnelTokenizerFast
	snake_case__				:  str    					=			True
	snake_case__				:  Optional[Any]    					=			True




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	str:
						super().setUp()

						__SCREAMING_SNAKE_CASE						   =				[
						    "<unk>",
						    "<cls>",
						    "<sep>",
						    "want",
						    "##want",
						    "##ed",
						    "wa",
						    "un",
						    "runn",
						    "##ing",
						    ",",
						    "low",
						    "lowest",
						]
						__SCREAMING_SNAKE_CASE						   =				os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["vocab_file"]   )
						with open(self.vocab_file			,			"w"			,			encoding="utf-8"   ) as vocab_writer:
											vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )




	def   UpperCAmelCase_      ( self			:				List[str]			,			**UpperCAmelCase__			:				str   )       ->	Any:
						return FunnelTokenizer.from_pretrained(self.tmpdirname			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Dict			,			**UpperCAmelCase__			:				int   )       ->	List[str]:
						return FunnelTokenizerFast.from_pretrained(self.tmpdirname			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				int   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				"UNwant\u00E9d,running"
						__SCREAMING_SNAKE_CASE						   =				"unwanted, running"
						return input_text, output_text




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				self.tokenizer_class(self.vocab_file   )

						__SCREAMING_SNAKE_CASE						   =				tokenizer.tokenize("UNwant\u00E9d,running"   )
						self.assertListEqual(UpperCAmelCase__			,			["un", "##want", "##ed", ",", "runn", "##ing"]   )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )			,			[7, 4, 5, 1_0, 8, 9]   )




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				self.get_tokenizers(do_lower_case=UpperCAmelCase__   )
						for tokenizer in tokenizers:
											__SCREAMING_SNAKE_CASE						   =				tokenizer("UNwant\u00E9d,running"   )
											__SCREAMING_SNAKE_CASE						   =				len(inputs["input_ids"]   ) - 1
											self.assertListEqual(inputs["token_type_ids"]			,			[2] + [0] * sentence_len   )

											__SCREAMING_SNAKE_CASE						   =				tokenizer("UNwant\u00E9d,running"			,			"UNwant\u00E9d,running"   )
											self.assertListEqual(inputs["token_type_ids"]			,			[2] + [0] * sentence_len + [1] * sentence_len   )






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()






