

'''simple docstring'''







import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
							from tokenizers import Tokenizer
							from tokenizers.models import WordPiece
class 					__a   (		unittest.TestCase  ):

						def 	_UpperCAmelCase							(		self	:						List[str]    ,			lowercase__	:						Optional[Any])						->Union[str, Any]:

							"""simple docstring"""



							_lowercase							=     list(tokenizer_class.max_model_input_sizes.keys())
							for model_name in sa_models[:1]:
								_lowercase							=     tokenizer_class.from_pretrained(lowercase__)
								self.assertIsNotNone(lowercase__)
								self.assertIsInstance(lowercase__    ,			lowercase__)
								self.assertIsInstance(lowercase__    ,			lowercase__)

								for special_tok in tokenizer.all_special_tokens:
									self.assertIsInstance(lowercase__    ,			lowercase__)
									_lowercase							=     tokenizer.convert_tokens_to_ids(lowercase__)
									self.assertIsInstance(lowercase__    ,			lowercase__)

						def 	_UpperCAmelCase							(		self	:						List[str]    ,			lowercase__	:						BatchEncoding    ,			lowercase__	:						Optional[Callable] = None)						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     pickle.dumps(lowercase__)
							self.assertIsNotNone(lowercase__)

							_lowercase							=     pickle.loads(lowercase__)

							# Ensure is_fast is correctly restored
							self.assertEqual(be_restored.is_fast    ,			be_original.is_fast)

							# Ensure encodings are potentially correctly restored
							if be_original.is_fast:
								self.assertIsNotNone(be_restored.encodings)
							else:
								self.assertIsNone(be_restored.encodings)

							# Ensure the keys are the same
							for original_v, restored_v in zip(be_original.values()    ,			be_restored.values()):
								if equal_op:
									self.assertTrue(equal_op(lowercase__    ,			lowercase__))
								else:
									self.assertEqual(lowercase__    ,			lowercase__)

						@slow
						def 	_UpperCAmelCase							(		self	:						Any)						->str:

							"""simple docstring"""



							self.check_tokenizer_from_pretrained(lowercase__)

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

							"""simple docstring"""



							self.assertEqual(TensorType("""tf""")    ,			TensorType.TENSORFLOW)
							self.assertEqual(TensorType("""pt""")    ,			TensorType.PYTORCH)
							self.assertEqual(TensorType("""np""")    ,			TensorType.NUMPY)

						@require_tokenizers
						def 	_UpperCAmelCase							(		self	:						Any)						->List[Any]:

							"""simple docstring"""



							import numpy as np

							_lowercase							=     BertTokenizer.from_pretrained("""bert-base-cased""")
							_lowercase							=     BertTokenizerFast.from_pretrained("""bert-base-cased""")

							# Python no tensor
							with self.subTest("""BatchEncoding (Python, return_tensors=None)"""):
								self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""))

							with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""):
								self.assert_dump_and_restore(
								    tokenizer_p("""Small example to encode"""    ,			return_tensors=TensorType.NUMPY)    ,			np.array_equal)

							with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""):
								self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""))

							with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""):
								self.assert_dump_and_restore(
								    tokenizer_r("""Small example to encode"""    ,			return_tensors=TensorType.NUMPY)    ,			np.array_equal)

						@require_tf
						@require_tokenizers
						def 	_UpperCAmelCase							(		self	:						List[Any])						->Dict:

							"""simple docstring"""



							import tensorflow as tf

							def tf_array_equals(lowercase__	:						Tuple    ,			lowercase__	:						Tuple):
								return tf.reduce_all(tf.equal(lowercase__    ,			lowercase__))

							_lowercase							=     BertTokenizer.from_pretrained("""bert-base-cased""")
							_lowercase							=     BertTokenizerFast.from_pretrained("""bert-base-cased""")

							with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""):
								self.assert_dump_and_restore(
								    tokenizer_p("""Small example to encode"""    ,			return_tensors=TensorType.TENSORFLOW)    ,			lowercase__)

							with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""):
								self.assert_dump_and_restore(
								    tokenizer_r("""Small example to encode"""    ,			return_tensors=TensorType.TENSORFLOW)    ,			lowercase__)

						@require_torch
						@require_tokenizers
						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->int:

							"""simple docstring"""



							import torch

							_lowercase							=     BertTokenizer.from_pretrained("""bert-base-cased""")
							_lowercase							=     BertTokenizerFast.from_pretrained("""bert-base-cased""")

							with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""):
								self.assert_dump_and_restore(
								    tokenizer_p("""Small example to encode"""    ,			return_tensors=TensorType.PYTORCH)    ,			torch.equal)

							with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""):
								self.assert_dump_and_restore(
								    tokenizer_r("""Small example to encode"""    ,			return_tensors=TensorType.PYTORCH)    ,			torch.equal)

						@require_tokenizers
						def 	_UpperCAmelCase							(		self	:						int)						->Any:

							"""simple docstring"""



							_lowercase							=     BertTokenizer.from_pretrained("""bert-base-cased""")
							_lowercase							=     BertTokenizerFast.from_pretrained("""bert-base-cased""")

							with self.subTest("""Python Tokenizer"""):
								self.assertFalse(tokenizer_p("""Small example to_encode""").is_fast)

							with self.subTest("""Rust Tokenizer"""):
								self.assertTrue(tokenizer_r("""Small example to_encode""").is_fast)

						@require_tokenizers
						def 	_UpperCAmelCase							(		self	:						Tuple)						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     BertTokenizerFast.from_pretrained("""bert-base-cased""")
							_lowercase							=     tokenizer_r(["""Test""", """\xad""", """test"""]    ,			is_split_into_words=lowercase__)

							self.assertEqual(encoded.word_to_tokens(0)    ,			TokenSpan(start=1    ,			end=2))
							self.assertEqual(encoded.word_to_tokens(1)    ,			lowercase__)
							self.assertEqual(encoded.word_to_tokens(2)    ,			TokenSpan(start=2    ,			end=3))

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->Tuple:

							"""simple docstring"""



							_lowercase							=     BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
							_lowercase							=     batch.convert_to_tensors(tensor_type="""np""")
							self.assertEqual(tensor_batch["""inputs"""].shape    ,			(2, 3))
							self.assertEqual(tensor_batch["""labels"""].shape    ,			(2,))
							# test converting the converted
							with CaptureStderr() as cs:
								_lowercase							=     batch.convert_to_tensors(tensor_type="""np""")
							self.assertFalse(len(cs.err)    ,			msg=f"""should have no warning, but got {cs.err}""")

							_lowercase							=     BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
							_lowercase							=     batch.convert_to_tensors(tensor_type="""np"""    ,			prepend_batch_axis=lowercase__)
							self.assertEqual(tensor_batch["""inputs"""].shape    ,			(1, 3))
							self.assertEqual(tensor_batch["""labels"""].shape    ,			(1,))

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

							"""simple docstring"""



							_lowercase							=     BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
							_lowercase							=     batch.convert_to_tensors(tensor_type="""pt""")
							self.assertEqual(tensor_batch["""inputs"""].shape    ,			(2, 3))
							self.assertEqual(tensor_batch["""labels"""].shape    ,			(2,))
							# test converting the converted
							with CaptureStderr() as cs:
								_lowercase							=     batch.convert_to_tensors(tensor_type="""pt""")
							self.assertFalse(len(cs.err)    ,			msg=f"""should have no warning, but got {cs.err}""")

							_lowercase							=     BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
							_lowercase							=     batch.convert_to_tensors(tensor_type="""pt"""    ,			prepend_batch_axis=lowercase__)
							self.assertEqual(tensor_batch["""inputs"""].shape    ,			(1, 3))
							self.assertEqual(tensor_batch["""labels"""].shape    ,			(1,))

						@require_tf
						def 	_UpperCAmelCase							(		self	:						str)						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
							_lowercase							=     batch.convert_to_tensors(tensor_type="""tf""")
							self.assertEqual(tensor_batch["""inputs"""].shape    ,			(2, 3))
							self.assertEqual(tensor_batch["""labels"""].shape    ,			(2,))
							# test converting the converted
							with CaptureStderr() as cs:
								_lowercase							=     batch.convert_to_tensors(tensor_type="""tf""")
							self.assertFalse(len(cs.err)    ,			msg=f"""should have no warning, but got {cs.err}""")

							_lowercase							=     BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
							_lowercase							=     batch.convert_to_tensors(tensor_type="""tf"""    ,			prepend_batch_axis=lowercase__)
							self.assertEqual(tensor_batch["""inputs"""].shape    ,			(1, 3))
							self.assertEqual(tensor_batch["""labels"""].shape    ,			(1,))

						@require_flax
						def 	_UpperCAmelCase							(		self	:						List[str])						->int:

							"""simple docstring"""



							_lowercase							=     BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
							_lowercase							=     batch.convert_to_tensors(tensor_type="""jax""")
							self.assertEqual(tensor_batch["""inputs"""].shape    ,			(2, 3))
							self.assertEqual(tensor_batch["""labels"""].shape    ,			(2,))
							# test converting the converted
							with CaptureStderr() as cs:
								_lowercase							=     batch.convert_to_tensors(tensor_type="""jax""")
							self.assertFalse(len(cs.err)    ,			msg=f"""should have no warning, but got {cs.err}""")

							_lowercase							=     BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
							_lowercase							=     batch.convert_to_tensors(tensor_type="""jax"""    ,			prepend_batch_axis=lowercase__)
							self.assertEqual(tensor_batch["""inputs"""].shape    ,			(1, 3))
							self.assertEqual(tensor_batch["""labels"""].shape    ,			(1,))

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

							"""simple docstring"""



							_lowercase							=     [{"""input_ids""": np.array([0, 1, 2])}, {"""input_ids""": np.array([0, 1, 2, 3])}]
							_lowercase							=     BertTokenizer.from_pretrained("""bert-base-cased""")

							_lowercase							=     tokenizer.pad(lowercase__    ,			padding=lowercase__)
							self.assertTrue(isinstance(batch["""input_ids"""]    ,			np.ndarray))
							self.assertEqual(batch["""input_ids"""].tolist()    ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
							_lowercase							=     tokenizer.pad(lowercase__    ,			padding=lowercase__    ,			return_tensors="""np""")
							self.assertTrue(isinstance(batch["""input_ids"""]    ,			np.ndarray))
							self.assertEqual(batch["""input_ids"""].tolist()    ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])

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

							"""simple docstring"""



							import torch

							_lowercase							=     [{"""input_ids""": torch.tensor([0, 1, 2])}, {"""input_ids""": torch.tensor([0, 1, 2, 3])}]
							_lowercase							=     BertTokenizer.from_pretrained("""bert-base-cased""")

							_lowercase							=     tokenizer.pad(lowercase__    ,			padding=lowercase__)
							self.assertTrue(isinstance(batch["""input_ids"""]    ,			torch.Tensor))
							self.assertEqual(batch["""input_ids"""].tolist()    ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
							_lowercase							=     tokenizer.pad(lowercase__    ,			padding=lowercase__    ,			return_tensors="""pt""")
							self.assertTrue(isinstance(batch["""input_ids"""]    ,			torch.Tensor))
							self.assertEqual(batch["""input_ids"""].tolist()    ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])

						@require_tf
						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->str:

							"""simple docstring"""



							import tensorflow as tf

							_lowercase							=     [{"""input_ids""": tf.constant([0, 1, 2])}, {"""input_ids""": tf.constant([0, 1, 2, 3])}]
							_lowercase							=     BertTokenizer.from_pretrained("""bert-base-cased""")

							_lowercase							=     tokenizer.pad(lowercase__    ,			padding=lowercase__)
							self.assertTrue(isinstance(batch["""input_ids"""]    ,			tf.Tensor))
							self.assertEqual(batch["""input_ids"""].numpy().tolist()    ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
							_lowercase							=     tokenizer.pad(lowercase__    ,			padding=lowercase__    ,			return_tensors="""tf""")
							self.assertTrue(isinstance(batch["""input_ids"""]    ,			tf.Tensor))
							self.assertEqual(batch["""input_ids"""].numpy().tolist()    ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])

						@require_tokenizers
						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Union[str, Any]:

							"""simple docstring"""



							_lowercase							=     Tokenizer(WordPiece(unk_token="""[UNK]"""))
							PreTrainedTokenizerFast(tokenizer_object=lowercase__)


						@require_tokenizers
						def 	_UpperCAmelCase							(		self	:						List[Any])						->int:

							"""simple docstring"""



							_lowercase							=     Tokenizer(WordPiece(unk_token="""[UNK]"""))
							with tempfile.TemporaryDirectory() as tmpdirname:
								bert_tokenizer.save(os.path.join(lowercase__    ,			"""tokenizer.json"""))
								PreTrainedTokenizerFast(tokenizer_file=os.path.join(lowercase__    ,			"""tokenizer.json"""))







'''simple docstring'''







from __future__ import annotations

import math
import random






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     d * e - 1
	_lowercase							=     0
	_lowercase							=     0
	while p == 0:
		_lowercase							=     random.randint(2							,       n - 1	)
		_lowercase							=     k
		while True:
			if t % 2 == 0:
				_lowercase							=     t // 2
				_lowercase							=     (g**t) % n
				_lowercase							=     math.gcd(x - 1							,       snake_case_	)
				if x > 1 and y > 1:
					_lowercase							=     y
					_lowercase							=     n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




