
'''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 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Optional[Any]   ):
										snake_case							:  Optional[int]			=     list(tokenizer_class.max_model_input_sizes.keys()   )
										for model_name in sa_models[:1]:
																snake_case							:  str			=     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_   )
																						snake_case							:  Optional[Any]			=     tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_   )
																						self.assertIsInstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					BatchEncoding,     SCREAMING_SNAKE_CASE_						:					Optional[Callable] = None   ):
										snake_case							:  Dict			=     pickle.dumps(SCREAMING_SNAKE_CASE_   )
										self.assertIsNotNone(SCREAMING_SNAKE_CASE_   )

										snake_case							:  Union[str, Any]			=     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 						__snake_case				(				self						:					Dict   ):
										self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE_   )




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




				@require_tokenizers
				def 						__snake_case				(				self						:					Dict   ):
										import numpy as np

										snake_case							:  str			=     BertTokenizer.from_pretrained('''bert-base-cased'''   )
										snake_case							:  Tuple			=     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 						__snake_case				(				self						:					List[Any]   ):
										import tensorflow as tf

										def tf_array_equals(SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int   ):
																return tf.reduce_all(tf.equal(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )   )

										snake_case							:  int			=     BertTokenizer.from_pretrained('''bert-base-cased'''   )
										snake_case							:  Optional[int]			=     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 						__snake_case				(				self						:					Optional[int]   ):
										import torch

										snake_case							:  List[str]			=     BertTokenizer.from_pretrained('''bert-base-cased'''   )
										snake_case							:  Dict			=     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 						__snake_case				(				self						:					Tuple   ):
										snake_case							:  List[Any]			=     BertTokenizer.from_pretrained('''bert-base-cased'''   )
										snake_case							:  List[Any]			=     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 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  Tuple			=     BertTokenizerFast.from_pretrained('''bert-base-cased'''   )
										snake_case							:  Union[str, Any]			=     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 						__snake_case				(				self						:					Tuple   ):
										snake_case							:  int			=     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}   )
										snake_case							:  Any			=     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:
																snake_case							:  str			=     batch.convert_to_tensors(tensor_type='''np'''   )
										self.assertFalse(len(cs.err   ),     msg=F"""should have no warning, but got {cs.err}"""   )

										snake_case							:  int			=     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}   )
										snake_case							:  List[Any]			=     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 						__snake_case				(				self						:					Optional[int]   ):
										snake_case							:  int			=     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}   )
										snake_case							:  str			=     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:
																snake_case							:  List[Any]			=     batch.convert_to_tensors(tensor_type='''pt'''   )
										self.assertFalse(len(cs.err   ),     msg=F"""should have no warning, but got {cs.err}"""   )

										snake_case							:  str			=     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}   )
										snake_case							:  Optional[Any]			=     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 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  Dict			=     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}   )
										snake_case							:  Any			=     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:
																snake_case							:  Optional[Any]			=     batch.convert_to_tensors(tensor_type='''tf'''   )
										self.assertFalse(len(cs.err   ),     msg=F"""should have no warning, but got {cs.err}"""   )

										snake_case							:  int			=     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}   )
										snake_case							:  Any			=     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 						__snake_case				(				self						:					Any   ):
										snake_case							:  Optional[Any]			=     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}   )
										snake_case							:  Optional[Any]			=     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:
																snake_case							:  Union[str, Any]			=     batch.convert_to_tensors(tensor_type='''jax'''   )
										self.assertFalse(len(cs.err   ),     msg=F"""should have no warning, but got {cs.err}"""   )

										snake_case							:  List[Any]			=     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}   )
										snake_case							:  List[str]			=     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 						__snake_case				(				self						:					Dict   ):
										snake_case							:  Union[str, Any]			=     [{'''input_ids''': np.array([0, 1, 2]   )}, {'''input_ids''': np.array([0, 1, 2, 3]   )}]
										snake_case							:  Dict			=     BertTokenizer.from_pretrained('''bert-base-cased'''   )

										snake_case							:  Optional[Any]			=     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]]   )
										snake_case							:  List[Any]			=     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 						__snake_case				(				self						:					Dict   ):
										import torch

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

										snake_case							:  Optional[int]			=     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]]   )
										snake_case							:  Optional[int]			=     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 						__snake_case				(				self						:					Any   ):
										import tensorflow as tf

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

										snake_case							:  List[str]			=     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]]   )
										snake_case							:  Optional[Any]			=     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 						__snake_case				(				self						:					List[str]   ):
										snake_case							:  Union[str, Any]			=     Tokenizer(WordPiece(unk_token='''[UNK]'''   )   )
										PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_   )




				@require_tokenizers
				def 						__snake_case				(				self						:					List[str]   ):
										snake_case							:  Tuple			=     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'''   )   )




def      UpperCAmelCase__							(     _A :					int      , _A :					int				):





  '''simple docstring'''







  a__    =1.0
  for i in range(1      , r + 1				):
    ret *= (n + 1 - i) / i
  return round(_A				)




def      UpperCAmelCase__							(     _A :					int				):





  '''simple docstring'''







  return choose(8 + n      , _A				) + choose(9 + n      , _A				) - 10




def      UpperCAmelCase__							(     _A :					int				):





  '''simple docstring'''







  return sum(non_bouncy_exact(_A				) for i in range(1      , n + 1				)				)




def      UpperCAmelCase__							(     _A :					int = 1_00				):





  '''simple docstring'''







  return non_bouncy_upto(_A				)


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