
'''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  ):







   """simple docstring"""




   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		list(tokenizer_class.max_model_input_sizes.keys()      )
    for model_name in sa_models[:1]:
     SCREAMING_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      )
      SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )
      self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = None      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		pickle.dumps(__SCREAMING_SNAKE_CASE      )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  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 					__a			(       self      ):
    self.check_tokenizer_from_pretrained(__SCREAMING_SNAKE_CASE      )






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






   @require_tokenizers
   def 					__a			(       self      ):
    import numpy as np

    SCREAMING_SNAKE_CASE  :  Dict								=		BertTokenizer.from_pretrained('bert-base-cased'      )
    SCREAMING_SNAKE_CASE  :  Dict								=		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      ):
    import tensorflow as tf

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

    SCREAMING_SNAKE_CASE  :  Any								=		BertTokenizer.from_pretrained('bert-base-cased'      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		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 					__a			(       self      ):
    import torch

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

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

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

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

    SCREAMING_SNAKE_CASE  :  Any								=		BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		[{'input_ids': np.array([0, 1, 2]      )}, {'input_ids': np.array([0, 1, 2, 3]      )}]
    SCREAMING_SNAKE_CASE  :  Any								=		BertTokenizer.from_pretrained('bert-base-cased'      )

    SCREAMING_SNAKE_CASE  :  List[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]]      )
    SCREAMING_SNAKE_CASE  :  int								=		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 					__a			(       self      ):
    import torch

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

    SCREAMING_SNAKE_CASE  :  str								=		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]]      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		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 					__a			(       self      ):
    import tensorflow as tf

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

    SCREAMING_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]]      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		Tokenizer(WordPiece(unk_token='[UNK]'      )      )
    PreTrainedTokenizerFast(tokenizer_object=__SCREAMING_SNAKE_CASE      )






   @require_tokenizers
   def 					__a			(       self      ):
    SCREAMING_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'      )      )




'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int							)       ->	int:
 if number != int(snake_case_							):
  raise ValueError('factorial() only accepts integral values'							)
 if number < 0:
  raise ValueError('factorial() not defined for negative values'							)
 SCREAMING_SNAKE_CASE  :  str								=		1
 for i in range(1   ,     number + 1							):
  value *= i
 return value



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int							)       ->	int:
 if not isinstance(snake_case_   ,     snake_case_							):
  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()

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


