
"""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


a		:       Optional[int]     						=       ['bert-base-uncased', 'bert-base-cased']
a		:       int     						=       'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():







    class     lowercase(tf.keras.Model       ):



         def __init__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     Dict:







             """simple docstring"""



             super().__init__()
             a__		=			tokenizer
             a__		=			AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
             a__		=			TFAutoModel.from_config(__SCREAMING_SNAKE_CASE   )




         def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     List[str]:







             """simple docstring"""



             a__		=			self.tokenizer(__SCREAMING_SNAKE_CASE   )
             a__		=			self.bert(**__SCREAMING_SNAKE_CASE   )
             return out["pooler_output"]







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



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         super().setUp()

         a__		=			[
             BertTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
         ]  # repeat for when fast_bert_tokenizer=false
         a__		=			[TFBertTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
             TFBertTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE     ,				use_fast_bert_tokenizer=__SCREAMING_SNAKE_CASE   )
             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 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         for tokenizer, tf_tokenizer in zip(self.tokenizers     ,				self.tf_tokenizers   ):
             for test_inputs in (self.test_sentences, self.paired_sentences):
                 a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='tf'     ,				padding='longest'   )
                 a__		=			tf_tokenizer(__SCREAMING_SNAKE_CASE   )

                 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 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         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 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         for tf_tokenizer in self.tf_tokenizers:
             a__		=			tf.function(__SCREAMING_SNAKE_CASE   )
             for test_inputs in (self.test_sentences, self.paired_sentences):
                 a__		=			tf.constant(__SCREAMING_SNAKE_CASE   )
                 a__		=			compiled_tokenizer(__SCREAMING_SNAKE_CASE   )
                 a__		=			tf_tokenizer(__SCREAMING_SNAKE_CASE   )

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




     @slow
     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         for tf_tokenizer in self.tf_tokenizers:
             a__		=			ModelToSave(tokenizer=__SCREAMING_SNAKE_CASE   )
             a__		=			tf.convert_to_tensor(self.test_sentences   )
             a__		=			model(__SCREAMING_SNAKE_CASE   )  # Build model with some sample inputs
             with TemporaryDirectory() as tempdir:
                 a__		=			Path(__SCREAMING_SNAKE_CASE   ) / 'saved.model'
                 model.save(__SCREAMING_SNAKE_CASE   )
                 a__		=			tf.keras.models.load_model(__SCREAMING_SNAKE_CASE   )
             a__		=			loaded_model(__SCREAMING_SNAKE_CASE   )
             # 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   )




"""simple docstring"""




from itertools import product



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		int     )     ->						list[int]:
    a__		=			sides_number
    a__		=			max_face_number * dice_number
    a__		=			[0] * (max_total + 1)

    a__		=			1
    a__		=			range(UpperCamelCase							,		max_face_number + 1     )
    for dice_numbers in product(UpperCamelCase							,		repeat=UpperCamelCase     ):
        a__		=			sum(UpperCamelCase     )
        totals_frequencies[total] += 1

    return totals_frequencies



def     __magic_name__		(					)     ->						float:
    a__		=			total_frequency_distribution(
        sides_number=4							,		dice_number=9     )
    a__		=			total_frequency_distribution(
        sides_number=6							,		dice_number=6     )

    a__		=			0
    a__		=			9
    a__		=			4 * 9
    a__		=			6
    for peter_total in range(UpperCamelCase							,		max_peter_total + 1     ):
        peter_wins_count += peter_totals_frequencies[peter_total] * sum(
            colin_totals_frequencies[min_colin_total:peter_total]     )

    a__		=			(4**9) * (6**6)
    a__		=			peter_wins_count / total_games_number

    a__		=			round(UpperCamelCase							,		ndigits=7     )

    return rounded_peter_win_probability


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


