


"""simple docstring"""







import random

from .binary_exp_mod import bin_exp_mod
def _A						(						lowercase	,   lowercase=10_00		):







    """simple docstring"""
    if n < 2:
        return False

    if n % 2 == 0:
        return n == 2

    # this means n is odd
    a					=n - 1
    a					=0
    while d % 2 == 0:
        d /= 2
        exp += 1

    # n - 1=d*(2**exp)
    a					=0
    while count < prec:
        a					=random.randint(2	,   n - 1		)
        a					=bin_exp_mod(lowercase	,   lowercase	,   lowercase		)
        if b != 1:
            a					=True
            for _ in range(lowercase		):
                if b == n - 1:
                    a					=False
                    break
                a					=b * b
                b %= n
            if flag:
                return False
            count += 1
    return True


if __name__ == "__main__":
       lowerCamelCase_       :       Optional[Any]      						=  abs(int(input("""Enter bound : """).strip()))
       print("""Here's the list of primes:""")
       print(""", """.join(str(i) for i in range(n + 1) if is_prime_big(i)))



"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
       import tensorflow as tf

if is_tensorflow_text_available():
       from transformers.models.bert import TFBertTokenizer


lowerCamelCase_       :       str      						=  ["""bert-base-uncased""", """bert-base-cased"""]
lowerCamelCase_       :       List[str]      						=  """hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():






       class 				__A    (	tf.keras.Model      ):






        """simple docstring"""




        def __init__(		self			,   __A						) ->					Dict:
            super().__init__()
            a					=tokenizer
            a					=AutoConfig.from_pretrained(__A						)
            a					=TFAutoModel.from_config(__A						)





        def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					int:
            a					=self.tokenizer(__A						)
            a					=self.bert(**__A						)
            return out["pooler_output"]






@require_tf
@require_tensorflow_text
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     super().setUp()

     a					=[
         BertTokenizer.from_pretrained(__A						) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     a					=[TFBertTokenizer.from_pretrained(__A						) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(__A			,   use_fast_bert_tokenizer=__A						)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers						) == len(self.tf_tokenizers						)

     a					=[
         '''This is a straightforward English test sentence.''',
         '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
         '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
         '''And some much more rare Chinese: 齉 堃 齉堃''',
         '''Je vais aussi écrire en français pour tester les accents''',
         '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
     ]
     a					=list(zip(self.test_sentences			,   self.test_sentences[::-1]						)						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     for tokenizer, tf_tokenizer in zip(self.tokenizers			,   self.tf_tokenizers						):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             a					=tokenizer(__A			,   return_tensors='''tf'''			,   padding='''longest'''						)
             a					=tf_tokenizer(__A						)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape						)						)
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]			,   tf.intaa						) == tf_outputs[key]						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     for tf_tokenizer in self.tf_tokenizers:
         a					=tf_tokenizer(self.paired_sentences						)
         a					=tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences]			,   text_pair=[sentence[1] for sentence in self.paired_sentences]			,   )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]			,   tf.intaa						) == separated_outputs[key]						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     for tf_tokenizer in self.tf_tokenizers:
         a					=tf.function(__A						)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             a					=tf.constant(__A						)
             a					=compiled_tokenizer(__A						)
             a					=tf_tokenizer(__A						)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     for tf_tokenizer in self.tf_tokenizers:
         a					=ModelToSave(tokenizer=__A						)
         a					=tf.convert_to_tensor(self.test_sentences						)
         a					=model(__A						)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             a					=Path(__A						) / '''saved.model'''
             model.save(__A						)
             a					=tf.keras.models.load_model(__A						)
         a					=loaded_model(__A						)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output						)						)			,   1E-5						)