



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 		lowercase						( unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(	self	:						str						,  __UpperCAmelCase	:						Optional[Any]     )			-> Optional[Any]:
							UpperCAmelCase_=   list(tokenizer_class.max_model_input_sizes.keys()     )
							for model_name in sa_models[:1]:
													UpperCAmelCase_=   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     )
																			UpperCAmelCase_=   tokenizer.convert_tokens_to_ids(__UpperCAmelCase     )
																			self.assertIsInstance(__UpperCAmelCase						,  __UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]						,  __UpperCAmelCase	:						BatchEncoding						,  __UpperCAmelCase	:						Optional[Callable] = None     )			-> Tuple:
							UpperCAmelCase_=   pickle.dumps(__UpperCAmelCase     )
							self.assertIsNotNone(__UpperCAmelCase     )

							UpperCAmelCase_=   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   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Optional[int]:
							self.check_tokenizer_from_pretrained(__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> Tuple:
							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	:						List[str]     )			-> Tuple:
							import numpy as np

							UpperCAmelCase_=   BertTokenizer.from_pretrained("""bert-base-cased"""     )
							UpperCAmelCase_=   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[str]     )			-> Union[str, Any]:
							import tensorflow as tf

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

							UpperCAmelCase_=   BertTokenizer.from_pretrained("""bert-base-cased"""     )
							UpperCAmelCase_=   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   _SCREAMING_SNAKE_CASE		(	self	:						int     )			-> Optional[Any]:
							import torch

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

							UpperCAmelCase_=   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}     )
							UpperCAmelCase_=   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   _SCREAMING_SNAKE_CASE		(	self	:						Dict     )			-> Any:
							UpperCAmelCase_=   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}     )
							UpperCAmelCase_=   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:
													UpperCAmelCase_=   batch.convert_to_tensors(tensor_type="""pt"""     )
							self.assertFalse(len(cs.err     )						,  msg=F"""should have no warning, but got {cs.err}"""     )

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

							UpperCAmelCase_=   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}     )
							UpperCAmelCase_=   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   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Any:
							UpperCAmelCase_=   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}     )
							UpperCAmelCase_=   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:
													UpperCAmelCase_=   batch.convert_to_tensors(tensor_type="""jax"""     )
							self.assertFalse(len(cs.err     )						,  msg=F"""should have no warning, but got {cs.err}"""     )

							UpperCAmelCase_=   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}     )
							UpperCAmelCase_=   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   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> Union[str, Any]:
							UpperCAmelCase_=   [{"""input_ids""": np.array([0, 1, 2]     )}, {"""input_ids""": np.array([0, 1, 2, 3]     )}]
							UpperCAmelCase_=   BertTokenizer.from_pretrained("""bert-base-cased"""     )

							UpperCAmelCase_=   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]]     )
							UpperCAmelCase_=   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   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Dict:
							import torch

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

							UpperCAmelCase_=   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]]     )
							UpperCAmelCase_=   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   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> Any:
							import tensorflow as tf

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

							UpperCAmelCase_=   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]]     )
							UpperCAmelCase_=   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   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> str:
							UpperCAmelCase_=   Tokenizer(WordPiece(unk_token="""[UNK]"""     )     )
							PreTrainedTokenizerFast(tokenizer_object=__UpperCAmelCase     )




	@require_tokenizers
	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> List[str]:
							UpperCAmelCase_=   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"""     )     )









def        __a     (    lowerCAmelCase_   :					int				)      -> int:






						'''simple docstring'''



						if number != int(lowerCAmelCase_				):
												raise ValueError("""factorial() only accepts integral values"""				)
						if number < 0:
												raise ValueError("""factorial() not defined for negative values"""				)
						UpperCAmelCase_=   1
						for i in range(1			,number + 1				):
												value *= i
						return value



def        __a     (    lowerCAmelCase_   :					int				)      -> int:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""factorial() only accepts integral values"""				)
						if n < 0:
												raise ValueError("""factorial() not defined for negative values"""				)
						return 1 if n == 0 or n == 1 else n * factorial(n - 1				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__A				    =						int(input('''Enter a positive integer: ''').strip() or 0)
	print(f'factorial{n} is {factorial(n)}')




