






"""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    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	Optional[Any]    ):

     '''simple docstring'''
     __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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	BatchEncoding,			_lowerCamelCase					:	Optional[Callable] = None    ):

     '''simple docstring'''
     __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 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE_    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     self.assertEqual(TensorType('''tf'''    ),			TensorType.TENSORFLOW    )
     self.assertEqual(TensorType('''pt'''    ),			TensorType.PYTORCH    )
     self.assertEqual(TensorType('''np'''    ),			TensorType.NUMPY    )


   @require_tokenizers
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     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 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     import tensorflow as tf

     def tf_array_equals(_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	int    ):
       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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     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 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __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 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __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 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __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 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __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 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     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 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     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 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					Tokenizer(WordPiece(unk_token='''[UNK]'''    )    )
     PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_    )






   @require_tokenizers
   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __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'''    )    )






'''simple docstring'''





import math
from timeit import timeit

def   A			(							A_				:       int							):
						snake_case							:  List[str]			=     0
						snake_case							:  Any			=     abs(A_							)
						while True:
												snake_case							:  List[Any]			=     n // 10
												digits += 1
												if n == 0:
																		break
						return digits

def   A			(							A_				:       int							):
						return 1 if n == 0 else math.floor(math.log(abs(A_							)     ,				10							) + 1							)

def   A			(							A_				:       int							):
						return len(str(abs(A_							)							)							)

def   A			(							):
						from collections.abc import Callable

						def benchmark_a_function(A_				:       Callable     ,				A_				:       int							) -> None:
												snake_case							:  Optional[int]			=     F"""{func.__name__}({value})"""
												snake_case							:  Optional[int]			=     timeit(F"""__main__.{call}"""     ,				setup='''import __main__'''							)
												print(F"""{call}: {func(A_							)} -- {timing} seconds"""							)

						for value in (262144, 1125899906842624, 1267650600228229401496703205376):
												for func in (num_digits, num_digits_fast, num_digits_faster):
																		benchmark_a_function(A_     ,				A_							)
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()

