





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									=				["gpt2"]
__A									=				"gpt2"

if is_tf_available():



     class    _SCREAMING_SNAKE_CASE						(	tf.Module    ):







      '''simple docstring'''

      def __init__(self				:   List[str]       , UpperCAmelCase_				:   List[Any])       ->Optional[int]:



        '''simple docstring'''



        super().__init__()
        lowerCamelCase__:      Optional[Any]			  =tokenizer
        lowerCamelCase__:      List[Any]			  =AutoConfig.from_pretrained(UpperCAmelCase_)
        lowerCamelCase__:      Tuple			  =TFGPTaLMHeadModel.from_config(UpperCAmelCase_)







      @tf.function(input_signature=(tf.TensorSpec((None,)       , tf.string       , name="text"),))
      def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , UpperCAmelCase_				:   str)       ->Any:



        '''simple docstring'''



        lowerCamelCase__:      Optional[int]			  =self.tokenizer(UpperCAmelCase_)
        lowerCamelCase__:      str			  =tokenized["input_ids"].to_tensor()

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

        lowerCamelCase__:      Union[str, Any]			  =self.model(input_ids=UpperCAmelCase_       , attention_mask=UpperCAmelCase_)["logits"]

        return outputs



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







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->int:



   '''simple docstring'''



   super().setUp()

   lowerCamelCase__:      Tuple			  =[GPTaTokenizer.from_pretrained(UpperCAmelCase_) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   lowerCamelCase__:      Union[str, Any]			  =[TFGPTaTokenizer.from_pretrained(UpperCAmelCase_) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers) == len(self.tf_tokenizers)

   lowerCamelCase__:      Union[str, Any]			  =[
       "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__:      Optional[Any]			  =list(zip(self.test_sentences       , self.test_sentences[::-1]))

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->List[Any]:



   '''simple docstring'''



   for tokenizer, tf_tokenizer in zip(self.tokenizers       , self.tf_tokenizers):
     for test_inputs in self.test_sentences:
       lowerCamelCase__:      Any			  =tokenizer([test_inputs]       , return_tensors="tf")
       lowerCamelCase__:      List[Any]			  =tf_tokenizer([test_inputs])

       for key in python_outputs.keys():
         # convert them to numpy to avoid messing with ragged tensors
         lowerCamelCase__:      Tuple			  =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(UpperCAmelCase_       , tf.intaa) == tf_outputs_values))

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



   '''simple docstring'''



   for tf_tokenizer in self.tf_tokenizers:
     lowerCamelCase__:      Union[str, Any]			  =tf.function(UpperCAmelCase_)
     for test_inputs in self.test_sentences:
       lowerCamelCase__:      List[str]			  =tf.constant(UpperCAmelCase_)
       lowerCamelCase__:      Any			  =compiled_tokenizer(UpperCAmelCase_)
       lowerCamelCase__:      Optional[int]			  =tf_tokenizer(UpperCAmelCase_)

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

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Optional[Any]:



   '''simple docstring'''



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



   '''simple docstring'''



   for tf_tokenizer in self.tf_tokenizers:
     lowerCamelCase__:      int			  =tf.convert_to_tensor([self.test_sentences[0]])
     lowerCamelCase__:      Optional[int]			  =tf_tokenizer(UpperCAmelCase_)  # Build model with some sample inputs

     lowerCamelCase__:      Union[str, Any]			  =tf_tokenizer.get_config()
     lowerCamelCase__:      str			  =TFGPTaTokenizer.from_config(UpperCAmelCase_)
     lowerCamelCase__:      Any			  =model_from_config(UpperCAmelCase_)

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







 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Optional[Any]:



   '''simple docstring'''



   for tf_tokenizer in self.tf_tokenizers:
     # for the test to run
     lowerCamelCase__:      List[Any]			  =123_123

     for max_length in [3, 5, 1_024]:
       lowerCamelCase__:      Tuple			  =tf.convert_to_tensor([self.test_sentences[0]])
       lowerCamelCase__:      List[str]			  =tf_tokenizer(UpperCAmelCase_       , max_length=UpperCAmelCase_)

       lowerCamelCase__:      List[Any]			  =out["input_ids"].numpy().shape[1]

       assert out_length == max_length








from math import ceil, sqrt





def      lowerCAmelCase_ (       __a = 1000000		)     ->		int:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =0

  for outer_width in range(3	,    (limit // 4) + 2		):
    if outer_width**2 > limit:
      lowerCamelCase__:      Dict			  =max(ceil(sqrt(outer_width**2 - limit		)		)	,    1		)
    else:
      lowerCamelCase__:      str			  =1
    if (outer_width - hole_width_lower_bound) % 2:
      hole_width_lower_bound += 1

    answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

  return answer


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

