

"""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


_a				:		int	   =       ['''gpt2''']
_a				:		List[str]	   =       '''gpt2'''

if is_tf_available():
     class 						__A							(			tf.Module		):






          def __init__(		self    ,					a__  ):
            super().__init__()
            _lowerCAmelCase						:	List[str]        =					tokenizer
            _lowerCAmelCase						:	Union[str, Any]        =					AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
            _lowerCAmelCase						:	Optional[int]        =					TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE_  )






          @tf.function(input_signature=(tf.TensorSpec((None,)    ,					tf.string    ,					name="""text"""  ),)  )
          def    __A  (		self    ,					a__  ):
            _lowerCAmelCase						:	Optional[Any]        =					self.tokenizer(SCREAMING_SNAKE_CASE_  )
            _lowerCAmelCase						:	List[str]        =					tokenized["""input_ids"""].to_tensor()

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

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

            return outputs
@require_tf
@require_keras_nlp
class 						__A							(			unittest.TestCase		):






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

       _lowerCAmelCase						:	Union[str, Any]        =					[GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
       _lowerCAmelCase						:	Optional[Any]        =					[TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  ) for checkpoint in TOKENIZER_CHECKPOINTS]
       assert len(self.tokenizers  ) == len(self.tf_tokenizers  )

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






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

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

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






     @slow
     def    __A  (		self  ):
       for tf_tokenizer in self.tf_tokenizers:
         _lowerCAmelCase						:	str        =					tf.function(SCREAMING_SNAKE_CASE_  )
         for test_inputs in self.test_sentences:
           _lowerCAmelCase						:	int        =					tf.constant(SCREAMING_SNAKE_CASE_  )
           _lowerCAmelCase						:	Any        =					compiled_tokenizer(SCREAMING_SNAKE_CASE_  )
           _lowerCAmelCase						:	Optional[int]        =					tf_tokenizer(SCREAMING_SNAKE_CASE_  )

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






     @slow
     def    __A  (		self  ):
       for tf_tokenizer in self.tf_tokenizers:
         _lowerCAmelCase						:	Optional[Any]        =					ModelToSave(tokenizer=SCREAMING_SNAKE_CASE_  )
         _lowerCAmelCase						:	int        =					tf.convert_to_tensor([self.test_sentences[0]]  )
         _lowerCAmelCase						:	Union[str, Any]        =					model.serving(SCREAMING_SNAKE_CASE_  )  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
           _lowerCAmelCase						:	Any        =					Path(SCREAMING_SNAKE_CASE_  ) / """saved.model"""
           tf.saved_model.save(SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_    ,					signatures={"""serving_default""": model.serving}  )
           _lowerCAmelCase						:	Optional[Any]        =					tf.saved_model.load(SCREAMING_SNAKE_CASE_  )
         _lowerCAmelCase						:	List[str]        =					loaded_model.signatures["""serving_default"""](SCREAMING_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    __A  (		self  ):
       for tf_tokenizer in self.tf_tokenizers:
         _lowerCAmelCase						:	int        =					tf.convert_to_tensor([self.test_sentences[0]]  )
         _lowerCAmelCase						:	int        =					tf_tokenizer(SCREAMING_SNAKE_CASE_  )  # Build model with some sample inputs

         _lowerCAmelCase						:	Tuple        =					tf_tokenizer.get_config()
         _lowerCAmelCase						:	Tuple        =					TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE_  )
         _lowerCAmelCase						:	Union[str, Any]        =					model_from_config(SCREAMING_SNAKE_CASE_  )

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






     @slow
     def    __A  (		self  ):
       for tf_tokenizer in self.tf_tokenizers:
         # for the test to run
         _lowerCAmelCase						:	int        =					123123

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

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

           assert out_length == max_length





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int = 100     )	->				int:
  _lowerCAmelCase						:	Optional[Any]        =					n * (n + 1) * (2 * n + 1) / 6
  _lowerCAmelCase						:	Tuple        =					(n * (n + 1) / 2) ** 2
  return int(square_of_sum - sum_of_squares     )


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




