

"""simple docstring"""


import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class 					snake_case			(       __snake_case,							unittest.TestCase						):
				SCREAMING_SNAKE_CASE_     :   Dict        =						BartTokenizer
				SCREAMING_SNAKE_CASE_     :   Optional[int]        =						BartTokenizerFast
				SCREAMING_SNAKE_CASE_     :   Optional[Any]        =						True
				SCREAMING_SNAKE_CASE_     :   List[Any]        =						filter_roberta_detectors

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



						'''simple docstring'''


						super().setUp()
						__lowerCAmelCase: List[str]					   =    [
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						__lowerCAmelCase: Optional[Any]					   =    dict(zip(UpperCamelCase__  ,						range(len(UpperCamelCase__))))
						__lowerCAmelCase: str					   =    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						__lowerCAmelCase: List[Any]					   =    {"unk_token": "<unk>"}

						__lowerCAmelCase: Optional[Any]					   =    os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["vocab_file"])
						__lowerCAmelCase: Tuple					   =    os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["merges_file"])
						with open(self.vocab_file  ,						"w"  ,						encoding="utf-8") as fp:
								fp.write(json.dumps(UpperCamelCase__) + "\n")
						with open(self.merges_file  ,						"w"  ,						encoding="utf-8") as fp:
								fp.write("\n".join(UpperCamelCase__))

				def 							lowercase_  (			self							:       List[str]  ,						**UpperCamelCase__							:       Optional[Any])->     str:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return self.tokenizer_class.from_pretrained(self.tmpdirname  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       Any  ,						**UpperCamelCase__							:       Dict)->     Any:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       str  ,						UpperCamelCase__							:       int)->     Dict:



						'''simple docstring'''


						return "lower newer", "lower newer"

				@cached_property
				def 							lowercase_  (			self							:       List[Any])->     Optional[int]:



						'''simple docstring'''


						return BartTokenizer.from_pretrained("facebook/bart-large")

				@cached_property
				def 							lowercase_  (			self							:       Dict)->     Optional[Any]:



						'''simple docstring'''


						return BartTokenizerFast.from_pretrained("facebook/bart-large")

				@require_torch
				def 							lowercase_  (			self							:       Optional[int])->     Dict:



						'''simple docstring'''


						__lowerCAmelCase: str					   =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
						__lowerCAmelCase: Dict					   =    [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__lowerCAmelCase: List[Any]					   =    tokenizer(UpperCamelCase__  ,						max_length=len(UpperCamelCase__)  ,						padding=UpperCamelCase__  ,						return_tensors="pt")
								self.assertIsInstance(UpperCamelCase__  ,						UpperCamelCase__)

								self.assertEqual((2, 9)  ,						batch.input_ids.shape)
								self.assertEqual((2, 9)  ,						batch.attention_mask.shape)
								__lowerCAmelCase: Optional[int]					   =    batch.input_ids.tolist()[0]
								self.assertListEqual(UpperCamelCase__  ,						UpperCamelCase__)
								# Test that special tokens are reset

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



						'''simple docstring'''


						__lowerCAmelCase: List[str]					   =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__lowerCAmelCase: Optional[int]					   =    tokenizer(UpperCamelCase__  ,						padding=UpperCamelCase__  ,						return_tensors="pt")
								# check if input_ids are returned and no labels
								self.assertIn("input_ids"  ,						UpperCamelCase__)
								self.assertIn("attention_mask"  ,						UpperCamelCase__)
								self.assertNotIn("labels"  ,						UpperCamelCase__)
								self.assertNotIn("decoder_attention_mask"  ,						UpperCamelCase__)

				@require_torch
				def 							lowercase_  (			self							:       Dict)->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: List[str]					   =    [
						    "Summary of the text.",
						    "Another summary.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__lowerCAmelCase: List[Any]					   =    tokenizer(text_target=UpperCamelCase__  ,						max_length=3_2  ,						padding="max_length"  ,						return_tensors="pt")
								self.assertEqual(3_2  ,						targets["input_ids"].shape[1])

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



						'''simple docstring'''


						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__lowerCAmelCase: Any					   =    tokenizer(
								    ["I am a small frog" * 1_0_2_4, "I am a small frog"]  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						return_tensors="pt")
								self.assertIsInstance(UpperCamelCase__  ,						UpperCamelCase__)
								self.assertEqual(batch.input_ids.shape  ,						(2, 1_0_2_4))

				@require_torch
				def 							lowercase_  (			self							:       Any)->     Any:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    ["A long paragraph for summarization."]
						__lowerCAmelCase: int					   =    [
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__lowerCAmelCase: Any					   =    tokenizer(UpperCamelCase__  ,						return_tensors="pt")
								__lowerCAmelCase: str					   =    tokenizer(text_target=UpperCamelCase__  ,						return_tensors="pt")
								__lowerCAmelCase: Any					   =    inputs["input_ids"]
								__lowerCAmelCase: Dict					   =    targets["input_ids"]
								self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
								self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
								self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
								self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())

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



						'''simple docstring'''


						pass




				def 							lowercase_  (			self							:       Optional[Any])->     List[str]:



						'''simple docstring'''


						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: str					   =    self.tokenizer_class.from_pretrained(UpperCamelCase__  ,						**UpperCamelCase__)
										__lowerCAmelCase: List[Any]					   =    "A, <mask> AllenNLP sentence."
										__lowerCAmelCase: Union[str, Any]					   =    tokenizer_r.encode_plus(UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ,						return_token_type_ids=UpperCamelCase__)
										__lowerCAmelCase: int					   =    tokenizer_p.encode_plus(UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ,						return_token_type_ids=UpperCamelCase__)

										# token_type_ids should put 0 everywhere
										self.assertEqual(sum(tokens_r["token_type_ids"])  ,						sum(tokens_p["token_type_ids"]))

										# attention_mask should put 1 everywhere, so sum over length should be 1
										self.assertEqual(
										    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])  ,						sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])  ,						)

										__lowerCAmelCase: Optional[int]					   =    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
										__lowerCAmelCase: List[Any]					   =    tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

										# Rust correctly handles the space before the mask while python doesnt
										self.assertSequenceEqual(tokens_p["input_ids"]  ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])
										self.assertSequenceEqual(tokens_r["input_ids"]  ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])

										self.assertSequenceEqual(
										    UpperCamelCase__  ,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
										self.assertSequenceEqual(
										    UpperCamelCase__  ,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])





"""simple docstring"""


from __future__ import annotations






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[float, float]:
		assert (
		    c % greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				) == 0
		)  # greatest_common_divisor(a,b) function implemented below
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # extended_gcd(a,b) function implemented below
		__lowerCAmelCase: Optional[int]					   =    c / d
		return (r * x, r * y)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = 2				)			->			None:
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)): Optional[Any]					   =    diophantine(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # Initial value
		__lowerCAmelCase: Optional[Any]					   =    greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Any					   =    a // d
		__lowerCAmelCase: str					   =    b // d

		for i in range(__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase: Dict					   =    xa + i * q
				__lowerCAmelCase: List[str]					   =    ya - i * p
				print(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			int:
		if a < b:
				__lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    b, a

		while a % b != 0:
				__lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    b, a % b

		return b






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[int, int, int]:
		assert a >= 0 and b >= 0

		if b == 0:
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    a, 1, 0
		else:
				((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				a % b				)
				__lowerCAmelCase: Dict					   =    q
				__lowerCAmelCase: int					   =    p - q * (a // b)

		assert a % d == 0 and b % d == 0
		assert d == a * x + b * y

		return (d, x, y)


if __name__ == "__main__":
			from doctest import testmod

			testmod(name="diophantine", verbose=True)
			testmod(name="diophantine_all_soln", verbose=True)
			testmod(name="extended_gcd", verbose=True)
			testmod(name="greatest_common_divisor", verbose=True)


