





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE	 =							["gpt2"]
_SCREAMING_SNAKE_CASE	 =							"gpt2"

if is_tf_available():

       class    lowerCAmelCase_		(		tf.Module		):




        def __init__(	self			,     _lowerCAmelCase		)    ->							str:
            super().__init__()
            _lowerCAmelCase				=      tokenizer
            _lowerCAmelCase				=      AutoConfig.from_pretrained(_lowerCAmelCase		)
            _lowerCAmelCase				=      TFGPTaLMHeadModel.from_config(_lowerCAmelCase		)





        @tf.function(input_signature=(tf.TensorSpec((None,)			,     tf.string			,     name="text"		),)		)
        def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
            _lowerCAmelCase				=      self.tokenizer(_lowerCAmelCase		)
            _lowerCAmelCase				=      tokenized["input_ids"].to_tensor()

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

            _lowerCAmelCase				=      self.model(input_ids=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase		)["logits"]

            return outputs

@require_tf
@require_keras_nlp
class    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							List[Any]:
     super().setUp()

     _lowerCAmelCase				=      [GPTaTokenizer.from_pretrained(_lowerCAmelCase		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
     _lowerCAmelCase				=      [TFGPTaTokenizer.from_pretrained(_lowerCAmelCase		) for checkpoint in TOKENIZER_CHECKPOINTS]
     assert len(self.tokenizers		) == len(self.tf_tokenizers		)

     _lowerCAmelCase				=      [
         "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",
     ]
     _lowerCAmelCase				=      list(zip(self.test_sentences			,     self.test_sentences[::-1]		)		)




 def 							_snake_case   (	self		)    ->							Dict:
     for tokenizer, tf_tokenizer in zip(self.tokenizers			,     self.tf_tokenizers		):
         for test_inputs in self.test_sentences:
             _lowerCAmelCase				=      tokenizer([test_inputs]			,     return_tensors="tf"		)
             _lowerCAmelCase				=      tf_tokenizer([test_inputs]		)

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

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




 @slow
 def 							_snake_case   (	self		)    ->							Dict:
     for tf_tokenizer in self.tf_tokenizers:
         _lowerCAmelCase				=      tf.function(_lowerCAmelCase		)
         for test_inputs in self.test_sentences:
             _lowerCAmelCase				=      tf.constant(_lowerCAmelCase		)
             _lowerCAmelCase				=      compiled_tokenizer(_lowerCAmelCase		)
             _lowerCAmelCase				=      tf_tokenizer(_lowerCAmelCase		)

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




 @slow
 def 							_snake_case   (	self		)    ->							Any:
     for tf_tokenizer in self.tf_tokenizers:
         _lowerCAmelCase				=      ModelToSave(tokenizer=_lowerCAmelCase		)
         _lowerCAmelCase				=      tf.convert_to_tensor([self.test_sentences[0]]		)
         _lowerCAmelCase				=      model.serving(_lowerCAmelCase		)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             _lowerCAmelCase				=      Path(_lowerCAmelCase		) / "saved.model"
             tf.saved_model.save(_lowerCAmelCase			,     _lowerCAmelCase			,     signatures={"serving_default": model.serving}		)
             _lowerCAmelCase				=      tf.saved_model.load(_lowerCAmelCase		)
         _lowerCAmelCase				=      loaded_model.signatures["serving_default"](_lowerCAmelCase		)["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 							_snake_case   (	self		)    ->							Dict:
     for tf_tokenizer in self.tf_tokenizers:
         _lowerCAmelCase				=      tf.convert_to_tensor([self.test_sentences[0]]		)
         _lowerCAmelCase				=      tf_tokenizer(_lowerCAmelCase		)  # Build model with some sample inputs

         _lowerCAmelCase				=      tf_tokenizer.get_config()
         _lowerCAmelCase				=      TFGPTaTokenizer.from_config(_lowerCAmelCase		)
         _lowerCAmelCase				=      model_from_config(_lowerCAmelCase		)

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





 @slow
 def 							_snake_case   (	self		)    ->							str:
     for tf_tokenizer in self.tf_tokenizers:
         # for the test to run
         _lowerCAmelCase				=      123123

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

             _lowerCAmelCase				=      out["input_ids"].numpy().shape[1]

             assert out_length == max_length









'''simple docstring'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


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


