





import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			GPTSanJapaneseTokenizer
 lowercase_		=			False
 lowercase_		=			{"do_clean_text": False, "add_prefix_space": False}

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



   '''simple docstring'''



   super().setUp()

   # fmt: off
   lowerCamelCase__:      List[str]			  =["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
   # fmt: on
   lowerCamelCase__:      Dict			  ={"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
   lowerCamelCase__:      List[str]			  ={"unk_token": "<unk>"}

   lowerCamelCase__:      int			  =os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["vocab_file"])
   lowerCamelCase__:      str			  =os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["emoji_file"])
   with open(self.vocab_file       , "w"       , encoding="utf-8") as vocab_writer:
     vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
   with open(self.emoji_file       , "w") as emoji_writer:
     emoji_writer.write(json.dumps(UpperCAmelCase_))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , **UpperCAmelCase_				:   Union[str, Any])       ->Union[str, Any]:



   '''simple docstring'''



   kwargs.update(self.special_tokens_map)
   return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname       , **UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   Optional[int])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Any			  ="こんにちは、世界。 \nこんばんは、㔺界。😀"
   lowerCamelCase__:      List[Any]			  ="こんにちは、世界。 \nこんばんは、世界。😀"
   return input_text, output_text

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[Any])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__:      Optional[Any]			  =self.get_input_output_texts(UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =tokenizer.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =tokenizer.decode(UpperCAmelCase_       , clean_up_tokenization_spaces=UpperCAmelCase_)
   return text, ids

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



   '''simple docstring'''



   pass  # TODO add if relevant

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



   '''simple docstring'''



   pass  # TODO add if relevant

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Optional[int]:



   '''simple docstring'''



   pass  # TODO add if relevant

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



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.get_tokenizer()

   # Testing tokenization
   lowerCamelCase__:      int			  ="こんにちは、世界。　こんばんは、㔺界。"
   lowerCamelCase__:      Optional[Any]			  =["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
   lowerCamelCase__:      Optional[Any]			  =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   # Testing conversion to ids without special tokens
   lowerCamelCase__:      Optional[Any]			  =[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
   lowerCamelCase__:      Tuple			  =tokenizer.convert_tokens_to_ids(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   # Testing conversion to ids with special tokens
   lowerCamelCase__:      Union[str, Any]			  =tokens + [tokenizer.unk_token]
   lowerCamelCase__:      Optional[int]			  =[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
   lowerCamelCase__:      Tuple			  =tokenizer.convert_tokens_to_ids(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =self.get_tokenizer()

   # Testing tokenization
   lowerCamelCase__:      List[Any]			  ="こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
   lowerCamelCase__:      List[str]			  ="こんにちは、、、、世界。こんばんは、、、、世界。"
   lowerCamelCase__:      Union[str, Any]			  =tokenizer.encode(UpperCAmelCase_)
   lowerCamelCase__:      Dict			  =tokenizer.decode(UpperCAmelCase_)
   self.assertEqual(UpperCAmelCase_       , UpperCAmelCase_)

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")

   # Testing tokenization
   lowerCamelCase__:      Any			  ="こんにちは、世界。"
   lowerCamelCase__:      Tuple			  ="こんばんは、㔺界。😀"
   lowerCamelCase__:      List[str]			  ="こんにちは、世界。こんばんは、世界。😀"
   lowerCamelCase__:      Dict			  =tokenizer.encode(prefix_text + input_text)
   lowerCamelCase__:      List[Any]			  =tokenizer.encode(""       , prefix_text=prefix_text + input_text)
   lowerCamelCase__:      List[str]			  =tokenizer.encode(UpperCAmelCase_       , prefix_text=UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =tokenizer.decode(UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =tokenizer.decode(UpperCAmelCase_)
   lowerCamelCase__:      Any			  =tokenizer.decode(UpperCAmelCase_)
   self.assertEqual(UpperCAmelCase_       , UpperCAmelCase_)
   self.assertEqual(UpperCAmelCase_       , UpperCAmelCase_)
   self.assertEqual(UpperCAmelCase_       , UpperCAmelCase_)

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")

   # Testing tokenization
   lowerCamelCase__:      List[str]			  ="こんにちは、世界。"
   lowerCamelCase__:      Dict			  ="こんばんは、㔺界。😀"

   lowerCamelCase__:      List[str]			  =len(tokenizer.encode(UpperCAmelCase_)) - 2
   lowerCamelCase__:      int			  =len(tokenizer.encode(UpperCAmelCase_)) - 2

   lowerCamelCase__:      List[Any]			  =[1] + [0] * (len_prefix + len_text + 1)
   lowerCamelCase__:      int			  =[1] * (len_prefix + len_text + 1) + [0]
   lowerCamelCase__:      Union[str, Any]			  =[1] + [1] * (len_prefix) + [0] * (len_text + 1)

   lowerCamelCase__:      Optional[int]			  =tokenizer(prefix_text + input_text).token_type_ids
   lowerCamelCase__:      List[str]			  =tokenizer(""       , prefix_text=prefix_text + input_text).token_type_ids
   lowerCamelCase__:      List[Any]			  =tokenizer(UpperCAmelCase_       , prefix_text=UpperCAmelCase_).token_type_ids
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")

   lowerCamelCase__:      List[Any]			  =tokenizer.encode("あンいワ")
   lowerCamelCase__:      Optional[int]			  =tokenizer.encode(""       , prefix_text="あンいワ")
   lowerCamelCase__:      List[Any]			  =tokenizer.encode("いワ"       , prefix_text="あン")

   self.assertEqual(tokenizer.decode(UpperCAmelCase_)       , tokenizer.decode(UpperCAmelCase_))
   self.assertEqual(tokenizer.decode(UpperCAmelCase_)       , tokenizer.decode(UpperCAmelCase_))
   self.assertNotEqual(UpperCAmelCase_       , UpperCAmelCase_)
   self.assertNotEqual(UpperCAmelCase_       , UpperCAmelCase_)
   self.assertEqual(x_token_a[1]       , x_token_a[-1])  # SEG token
   self.assertEqual(x_token_a[1]       , x_token_a[3])  # SEG token

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")

   lowerCamelCase__:      List[Any]			  =[["武田信玄", "は、"], ["織田信長", "の配下の、"]]
   lowerCamelCase__:      Union[str, Any]			  =tokenizer(UpperCAmelCase_       , padding=UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =tokenizer.batch_encode_plus(UpperCAmelCase_       , padding=UpperCAmelCase_)

   # fmt: off
   lowerCamelCase__:      Any			  =[[35_993, 8_640, 25_948, 35_998, 30_647, 35_675, 35_999, 35_999], [35_993, 10_382, 9_868, 35_998, 30_646, 9_459, 30_646, 35_675]]
   lowerCamelCase__:      Optional[Any]			  =[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
   lowerCamelCase__:      List[Any]			  =[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
   # fmt: on
   self.assertListEqual(x_token.input_ids       , UpperCAmelCase_)
   self.assertListEqual(x_token.token_type_ids       , UpperCAmelCase_)
   self.assertListEqual(x_token.attention_mask       , UpperCAmelCase_)
   self.assertListEqual(x_token_a.input_ids       , UpperCAmelCase_)
   self.assertListEqual(x_token_a.token_type_ids       , UpperCAmelCase_)
   self.assertListEqual(x_token_a.attention_mask       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->str:



   '''simple docstring'''



   pass







 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Dict:



   '''simple docstring'''



   pass







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()






