
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      _lowerCAmelCase (     unittest.TestCase	):




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     list(tokenizer_class.max_model_input_sizes.keys()					)
							for model_name in sa_models[:1]:
										lowerCAmelCase_      =     tokenizer_class.from_pretrained(_UpperCamelCase					)
										self.assertIsNotNone(_UpperCamelCase					)
										self.assertIsInstance(_UpperCamelCase    ,    _UpperCamelCase					)
										self.assertIsInstance(_UpperCamelCase    ,    _UpperCamelCase					)

										for special_tok in tokenizer.all_special_tokens:
													self.assertIsInstance(_UpperCamelCase    ,    _UpperCamelCase					)
													lowerCAmelCase_      =     tokenizer.convert_tokens_to_ids(_UpperCamelCase					)
													self.assertIsInstance(_UpperCamelCase    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase = None					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     pickle.dumps(_UpperCamelCase					)
							self.assertIsNotNone(_UpperCamelCase					)

							lowerCAmelCase_      =     pickle.loads(_UpperCamelCase					)

							# 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(_UpperCamelCase    ,    _UpperCamelCase					)					)
										else:
													self.assertEqual(_UpperCamelCase    ,    _UpperCamelCase					)




				@slow
				def 					__a						( self					)    ->				List[Any]:
							self.check_tokenizer_from_pretrained(_UpperCamelCase					)




				def 					__a						( self					)    ->				str:
							self.assertEqual(TensorType("tf"					)    ,    TensorType.TENSORFLOW					)
							self.assertEqual(TensorType("pt"					)    ,    TensorType.PYTORCH					)
							self.assertEqual(TensorType("np"					)    ,    TensorType.NUMPY					)




				@require_tokenizers
				def 					__a						( self					)    ->				List[str]:
							import numpy as np

							lowerCAmelCase_      =     BertTokenizer.from_pretrained("bert-base-cased"					)
							lowerCAmelCase_      =     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 					__a						( self					)    ->				int:
							import tensorflow as tf

							def tf_array_equals(_UpperCamelCase    ,    _UpperCamelCase					):
										return tf.reduce_all(tf.equal(_UpperCamelCase    ,    _UpperCamelCase					)					)

							lowerCAmelCase_      =     BertTokenizer.from_pretrained("bert-base-cased"					)
							lowerCAmelCase_      =     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					)    ,    _UpperCamelCase					)

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




				@require_torch
				@require_tokenizers
				def 					__a						( self					)    ->				str:
							import torch

							lowerCAmelCase_      =     BertTokenizer.from_pretrained("bert-base-cased"					)
							lowerCAmelCase_      =     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 					__a						( self					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     BertTokenizer.from_pretrained("bert-base-cased"					)
							lowerCAmelCase_      =     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 					__a						( self					)    ->				Optional[int]:
							lowerCAmelCase_      =     BertTokenizerFast.from_pretrained("bert-base-cased"					)
							lowerCAmelCase_      =     tokenizer_r(["Test", "\xad", "test"]    ,    is_split_into_words=_UpperCamelCase					)

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




				def 					__a						( self					)    ->				str:
							lowerCAmelCase_      =     BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
							lowerCAmelCase_      =     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:
										lowerCAmelCase_      =     batch.convert_to_tensors(tensor_type="np"					)
							self.assertFalse(len(cs.err					)    ,    msg=f"""should have no warning, but got {cs.err}"""					)

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




				@require_torch
				def 					__a						( self					)    ->				Tuple:
							lowerCAmelCase_      =     BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
							lowerCAmelCase_      =     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:
										lowerCAmelCase_      =     batch.convert_to_tensors(tensor_type="pt"					)
							self.assertFalse(len(cs.err					)    ,    msg=f"""should have no warning, but got {cs.err}"""					)

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




				@require_tf
				def 					__a						( self					)    ->				Optional[Any]:
							lowerCAmelCase_      =     BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
							lowerCAmelCase_      =     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:
										lowerCAmelCase_      =     batch.convert_to_tensors(tensor_type="tf"					)
							self.assertFalse(len(cs.err					)    ,    msg=f"""should have no warning, but got {cs.err}"""					)

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




				@require_flax
				def 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
							lowerCAmelCase_      =     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:
										lowerCAmelCase_      =     batch.convert_to_tensors(tensor_type="jax"					)
							self.assertFalse(len(cs.err					)    ,    msg=f"""should have no warning, but got {cs.err}"""					)

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




				def 					__a						( self					)    ->				Optional[Any]:
							lowerCAmelCase_      =     [{"input_ids": np.array([0, 1, 2]					)}, {"input_ids": np.array([0, 1, 2, 3]					)}]
							lowerCAmelCase_      =     BertTokenizer.from_pretrained("bert-base-cased"					)

							lowerCAmelCase_      =     tokenizer.pad(_UpperCamelCase    ,    padding=_UpperCamelCase					)
							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]]					)
							lowerCAmelCase_      =     tokenizer.pad(_UpperCamelCase    ,    padding=_UpperCamelCase    ,    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 					__a						( self					)    ->				Optional[Any]:
							import torch

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

							lowerCAmelCase_      =     tokenizer.pad(_UpperCamelCase    ,    padding=_UpperCamelCase					)
							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]]					)
							lowerCAmelCase_      =     tokenizer.pad(_UpperCamelCase    ,    padding=_UpperCamelCase    ,    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 					__a						( self					)    ->				Union[str, Any]:
							import tensorflow as tf

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

							lowerCAmelCase_      =     tokenizer.pad(_UpperCamelCase    ,    padding=_UpperCamelCase					)
							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]]					)
							lowerCAmelCase_      =     tokenizer.pad(_UpperCamelCase    ,    padding=_UpperCamelCase    ,    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 					__a						( self					)    ->				Any:
							lowerCAmelCase_      =     Tokenizer(WordPiece(unk_token="[UNK]"					)					)
							PreTrainedTokenizerFast(tokenizer_object=_UpperCamelCase					)




				@require_tokenizers
				def 					__a						( self					)    ->				Optional[Any]:
							lowerCAmelCase_      =     Tokenizer(WordPiece(unk_token="[UNK]"					)					)
							with tempfile.TemporaryDirectory() as tmpdirname:
										bert_tokenizer.save(os.path.join(_UpperCamelCase    ,    "tokenizer.json"					)					)
										PreTrainedTokenizerFast(tokenizer_file=os.path.join(_UpperCamelCase    ,    "tokenizer.json"					)					)





import math






def        lowerCamelCase__    (    __lowerCAmelCase  :  int    ):




			"""simple docstring"""


			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5   ,							int(math.sqrt(__lowerCAmelCase    ) + 1    )   ,							6    ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True






def        lowerCamelCase__    (    __lowerCAmelCase  :  int = 600851475143    ):




			"""simple docstring"""


			try:
						lowerCAmelCase_      =     int(__lowerCAmelCase    )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."    )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."    )
			lowerCAmelCase_      =     0
			if is_prime(__lowerCAmelCase    ):
						return n
			while n % 2 == 0:
						n //= 2
			if is_prime(__lowerCAmelCase    ):
						return n
			for i in range(3   ,							int(math.sqrt(__lowerCAmelCase    )    ) + 1   ,							2    ):
						if n % i == 0:
									if is_prime(n // i    ):
												lowerCAmelCase_      =     n // i
												break
									elif is_prime(__lowerCAmelCase    ):
												lowerCAmelCase_      =     i
			return max_number


if __name__ == "__main__":
			print(f"""{solution() = }""")



