





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


__A							:			Union[str, Any]         =			["gpt2"]
__A							:			Union[str, Any]         =			"gpt2"

if is_tf_available():







 class      lowerCamelCase(						tf.Module	):
      '''simple docstring'''


      def __init__(    self				,     snake_case_							):
          super().__init__()
          _A     =							tokenizer
          _A     =							AutoConfig.from_pretrained(snake_case_							)
          _A     =							TFGPTaLMHeadModel.from_config(snake_case_							)




      @tf.function(input_signature=(tf.TensorSpec((None,)				,     tf.string				,     name='text'							),)							)
      def        lowerCAmelCase__		(    self				,     snake_case_							):
          _A     =							self.tokenizer(snake_case_							)
          _A     =							tokenized['input_ids'].to_tensor()

          _A     =							tf.cast(input_ids_dense > 0				,     tf.intaa							)
          # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

          _A     =							self.model(input_ids=snake_case_				,     attention_mask=snake_case_							)['logits']

          return outputs







@require_tf
@require_keras_nlp
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         super().setUp()

         _A     =							[GPTaTokenizer.from_pretrained(snake_case_							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         _A     =							[TFGPTaTokenizer.from_pretrained(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        lowerCAmelCase__		(    self							):
         for tokenizer, tf_tokenizer in zip(self.tokenizers				,     self.tf_tokenizers							):
             for test_inputs in self.test_sentences:
                 _A     =							tokenizer([test_inputs]				,     return_tensors='tf'							)
                 _A     =							tf_tokenizer([test_inputs]							)

                 for key in python_outputs.keys():
                     # convert them to numpy to avoid messing with ragged tensors
                     _A     =							python_outputs[key].numpy()
                     _A     =							tf_outputs[key].numpy()

                     self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
                     self.assertTrue(tf.reduce_all(tf.cast(snake_case_				,     tf.intaa							) == tf_outputs_values							)							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for tf_tokenizer in self.tf_tokenizers:
             _A     =							tf.function(snake_case_							)
             for test_inputs in self.test_sentences:
                 _A     =							tf.constant(snake_case_							)
                 _A     =							compiled_tokenizer(snake_case_							)
                 _A     =							tf_tokenizer(snake_case_							)

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




     @slow
     def        lowerCAmelCase__		(    self							):
         for tf_tokenizer in self.tf_tokenizers:
             _A     =							ModelToSave(tokenizer=snake_case_							)
             _A     =							tf.convert_to_tensor([self.test_sentences[0]]							)
             _A     =							model.serving(snake_case_							)  # Build model with some sample inputs
             with TemporaryDirectory() as tempdir:
                 _A     =							Path(snake_case_							) / 'saved.model'
                 tf.saved_model.save(snake_case_				,     snake_case_				,     signatures={'serving_default': model.serving}							)
                 _A     =							tf.saved_model.load(snake_case_							)
             _A     =							loaded_model.signatures['serving_default'](snake_case_							)['output_0']
             # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
             self.assertTrue(tf.reduce_all(out == loaded_output							)							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for tf_tokenizer in self.tf_tokenizers:
             _A     =							tf.convert_to_tensor([self.test_sentences[0]]							)
             _A     =							tf_tokenizer(snake_case_							)  # Build model with some sample inputs

             _A     =							tf_tokenizer.get_config()
             _A     =							TFGPTaTokenizer.from_config(snake_case_							)
             _A     =							model_from_config(snake_case_							)

             for key in from_config_output.keys():
                 self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for tf_tokenizer in self.tf_tokenizers:
             # for the test to run
             _A     =							12_3123

             for max_length in [3, 5, 1024]:
                 _A     =							tf.convert_to_tensor([self.test_sentences[0]]							)
                 _A     =							tf_tokenizer(snake_case_				,     max_length=snake_case_							)

                 _A     =							out['input_ids'].numpy().shape[1]

                 assert out_length == max_length












from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> bool:




    """simple docstring"""




    _A     =							int(number**0.5				)
    return number == sq * sq





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> tuple[int, int]:




    """simple docstring"""




    _A     =							x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    _A     =							x_den * y_den * z_den
    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    top //= hcf
    bottom //= hcf
    return top, bottom





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 35				)							-> int:




    """simple docstring"""




    _A     =							set()
    _A     =							42
    _A     =							Fraction(0				)
    _A     =							42

    for x_num in range(1							,     order + 1				):
        for x_den in range(x_num + 1							,     order + 1				):
            for y_num in range(1							,     order + 1				):
                for y_den in range(y_num + 1							,     order + 1				):
                    # n=1
                    _A     =							x_num * y_den + x_den * y_num
                    _A     =							x_den * y_den
                    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _A     =							add_three(
                            _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=2
                    _A     =							(
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    _A     =							x_den * x_den * y_den * y_den
                    if is_sq(_SCREAMING_SNAKE_CASE				) and is_sq(_SCREAMING_SNAKE_CASE				):
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _A     =							add_three(
                                _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                            unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=-1
                    _A     =							x_num * y_num
                    _A     =							x_den * y_num + x_num * y_den
                    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _A     =							add_three(
                            _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=2
                    _A     =							x_num * x_num * y_num * y_num
                    _A     =							(
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(_SCREAMING_SNAKE_CASE				) and is_sq(_SCREAMING_SNAKE_CASE				):
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _A     =							add_three(
                                _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                            unique_s.add(_SCREAMING_SNAKE_CASE				)

    for num, den in unique_s:
        total += Fraction(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

    return total.denominator + total.numerator


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





