


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 					_UpperCamelCase (      unittest.TestCase	):







							'''simple docstring'''







							def 		_UpperCAmelCase	(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Any		):
													_a     =			list(tokenizer_class.max_model_input_sizes.keys()		)
													for model_name in sa_models[:1]:
																			_a     =			tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_		)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE_		)
																			self.assertIsInstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
																			self.assertIsInstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)

																			for special_tok in tokenizer.all_special_tokens:
																									self.assertIsInstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
																									_a     =			tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_		)
																									self.assertIsInstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)







							def 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				BatchEncoding		,				SCREAMING_SNAKE_CASE_  :				Optional[Callable] = None		):
													_a     =			pickle.dumps(SCREAMING_SNAKE_CASE_		)
													self.assertIsNotNone(SCREAMING_SNAKE_CASE_		)

													_a     =			pickle.loads(SCREAMING_SNAKE_CASE_		)

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







							@slow
							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
													self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE_		)







							def 		_UpperCAmelCase	(		self  :				str		):
													self.assertEqual(TensorType('tf'		)		,				TensorType.TENSORFLOW		)
													self.assertEqual(TensorType('pt'		)		,				TensorType.PYTORCH		)
													self.assertEqual(TensorType('np'		)		,				TensorType.NUMPY		)







							@require_tokenizers
							def 		_UpperCAmelCase	(		self  :				int		):
													import numpy as np

													_a     =			BertTokenizer.from_pretrained('bert-base-cased'		)
													_a     =			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  :				Optional[Any]		):
													import tensorflow as tf

													def tf_array_equals(SCREAMING_SNAKE_CASE_  :				Any		,				SCREAMING_SNAKE_CASE_  :				str		):
																			return tf.reduce_all(tf.equal(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)		)

													_a     =			BertTokenizer.from_pretrained('bert-base-cased'		)
													_a     =			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		)		,				SCREAMING_SNAKE_CASE_		)

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







							@require_torch
							@require_tokenizers
							def 		_UpperCAmelCase	(		self  :				List[str]		):
													import torch

													_a     =			BertTokenizer.from_pretrained('bert-base-cased'		)
													_a     =			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  :				Any		):
													_a     =			BertTokenizer.from_pretrained('bert-base-cased'		)
													_a     =			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  :				Any		):
													_a     =			BertTokenizerFast.from_pretrained('bert-base-cased'		)
													_a     =			tokenizer_r(['Test', '\xad', 'test']		,				is_split_into_words=SCREAMING_SNAKE_CASE_		)

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







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

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







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

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







							@require_tf
							def 		_UpperCAmelCase	(		self  :				List[str]		):
													_a     =			BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}		)
													_a     =			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:
																			_a     =			batch.convert_to_tensors(tensor_type='tf'		)
													self.assertFalse(len(cs.err		)		,				msg=f"""should have no warning, but got {cs.err}"""		)

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







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

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







							def 		_UpperCAmelCase	(		self  :				Tuple		):
													_a     =			[{'input_ids': np.array([0, 1, 2]		)}, {'input_ids': np.array([0, 1, 2, 3]		)}]
													_a     =			BertTokenizer.from_pretrained('bert-base-cased'		)

													_a     =			tokenizer.pad(SCREAMING_SNAKE_CASE_		,				padding=SCREAMING_SNAKE_CASE_		)
													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]]		)
													_a     =			tokenizer.pad(SCREAMING_SNAKE_CASE_		,				padding=SCREAMING_SNAKE_CASE_		,				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  :				Union[str, Any]		):
													import torch

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

													_a     =			tokenizer.pad(SCREAMING_SNAKE_CASE_		,				padding=SCREAMING_SNAKE_CASE_		)
													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]]		)
													_a     =			tokenizer.pad(SCREAMING_SNAKE_CASE_		,				padding=SCREAMING_SNAKE_CASE_		,				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  :				Dict		):
													import tensorflow as tf

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

													_a     =			tokenizer.pad(SCREAMING_SNAKE_CASE_		,				padding=SCREAMING_SNAKE_CASE_		)
													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]]		)
													_a     =			tokenizer.pad(SCREAMING_SNAKE_CASE_		,				padding=SCREAMING_SNAKE_CASE_		,				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  :				int		):
													_a     =			Tokenizer(WordPiece(unk_token='[UNK]'		)		)
													PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_		)







							@require_tokenizers
							def 		_UpperCAmelCase	(		self  :				Dict		):
													_a     =			Tokenizer(WordPiece(unk_token='[UNK]'		)		)
													with tempfile.TemporaryDirectory() as tmpdirname:
																			bert_tokenizer.save(os.path.join(SCREAMING_SNAKE_CASE_		,				'tokenizer.json'		)		)
																			PreTrainedTokenizerFast(tokenizer_file=os.path.join(SCREAMING_SNAKE_CASE_		,				'tokenizer.json'		)		)






import math


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
						_a     =			math.log(2							,     10				) * number
						_a     =			round((log_number - int(_UpperCAmelCase				))							,     15				)

						return difference


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase = 678910				)    ->  int:
						_a     =			90
						_a     =			0

						_a     =			math.log(1.23							,     10				)
						_a     =			math.log(1.24							,     10				)
						_a     =			0

						while position < number:
												_a     =			log_difference(_UpperCAmelCase				)

												if difference >= upper_limit:
																		power_iterator += 93

												elif difference < lower_limit:
																		power_iterator += 196

												else:
																		_a     =			power_iterator
																		power_iterator += 196
																		position += 1

						return previous_power


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		print(f"""{solution() = }""")


