


"""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


__snake_case    					=					['gpt2']
__snake_case    					=					'gpt2'

if is_tf_available():

   class 				_SCREAMING_SNAKE_CASE				(							tf.Module  ):
      """simple docstring"""



      def __init__(						self ,     lowerCamelCase__						)      ->							Any:
       super().__init__()
       lowercase__       :       Optional[Any]				=						tokenizer
       lowercase__       :       Optional[int]				=						AutoConfig.from_pretrained(lowerCamelCase__						)
       lowercase__       :       int				=						TFGPTaLMHeadModel.from_config(lowerCamelCase__						)







      @tf.function(input_signature=(tf.TensorSpec((None,) ,     tf.string ,     name="""text"""						),)						)
      def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							Tuple:
       lowercase__       :       List[Any]				=						self.tokenizer(lowerCamelCase__						)
       lowercase__       :       Tuple				=						tokenized["""input_ids"""].to_tensor()

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

       lowercase__       :       Optional[Any]				=						self.model(input_ids=lowerCamelCase__ ,     attention_mask=lowerCamelCase__						)["""logits"""]

       return outputs

@require_tf
@require_keras_nlp
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							int:
    super().setUp()

    lowercase__       :       Dict				=						[GPTaTokenizer.from_pretrained(lowerCamelCase__						) for checkpoint in (TOKENIZER_CHECKPOINTS)]
    lowercase__       :       Optional[int]				=						[TFGPTaTokenizer.from_pretrained(lowerCamelCase__						) for checkpoint in TOKENIZER_CHECKPOINTS]
    assert len(self.tokenizers						) == len(self.tf_tokenizers						)

    lowercase__       :       Optional[int]				=						[
        """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""",
    ]
    lowercase__       :       str				=						list(zip(self.test_sentences ,     self.test_sentences[::-1]						)						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    for tokenizer, tf_tokenizer in zip(self.tokenizers ,     self.tf_tokenizers						):
     for test_inputs in self.test_sentences:
      lowercase__       :       int				=						tokenizer([test_inputs] ,     return_tensors="""tf"""						)
      lowercase__       :       Optional[Any]				=						tf_tokenizer([test_inputs]						)

      for key in python_outputs.keys():
       # convert them to numpy to avoid messing with ragged tensors
       lowercase__       :       Optional[int]				=						python_outputs[key].numpy()
       lowercase__       :       Optional[int]				=						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 					UpperCAmelCase__(						self						)      ->							Tuple:
    for tf_tokenizer in self.tf_tokenizers:
     lowercase__       :       Dict				=						tf.function(lowerCamelCase__						)
     for test_inputs in self.test_sentences:
      lowercase__       :       List[Any]				=						tf.constant(lowerCamelCase__						)
      lowercase__       :       Optional[int]				=						compiled_tokenizer(lowerCamelCase__						)
      lowercase__       :       List[str]				=						tf_tokenizer(lowerCamelCase__						)

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


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

     lowercase__       :       str				=						tf_tokenizer.get_config()
     lowercase__       :       Optional[Any]				=						TFGPTaTokenizer.from_config(lowerCamelCase__						)
     lowercase__       :       Tuple				=						model_from_config(lowerCamelCase__						)

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







   @slow
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    for tf_tokenizer in self.tf_tokenizers:
     # for the test to run
     lowercase__       :       Union[str, Any]				=						12_3123

     for max_length in [3, 5, 1024]:
      lowercase__       :       Tuple				=						tf.convert_to_tensor([self.test_sentences[0]]						)
      lowercase__       :       List[Any]				=						tf_tokenizer(lowerCamelCase__ ,     max_length=lowerCamelCase__						)

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

      assert out_length == max_length



"""simple docstring"""


from decimal import Decimal, getcontext
from math import ceil, factorial


def    _lowerCamelCase   (     lowerCamelCase__	:							int  ):

 if not isinstance(lowerCamelCase__       ,		lowerCamelCase__  ):
  raise TypeError("""Undefined for non-integers"""  )
 elif precision < 1:
  raise ValueError("""Undefined for non-natural numbers"""  )

 lowercase__       :       List[str]				=						precision
 lowercase__       :       Optional[int]				=						ceil(precision / 14  )
 lowercase__       :       Union[str, Any]				=						42_68_80 * Decimal(1_00_05  ).sqrt()
 lowercase__       :       Optional[Any]				=						1
 lowercase__       :       Optional[Any]				=						13_59_14_09
 lowercase__       :       List[Any]				=						Decimal(lowerCamelCase__  )
 for k in range(1       ,		lowerCamelCase__  ):
  lowercase__       :       Optional[int]				=						factorial(6 * k  ) // (factorial(3 * k  ) * factorial(lowerCamelCase__  ) ** 3)
  linear_term += 5_45_14_01_34
  exponential_term *= -26_25_37_41_26_40_76_80_00
  partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
 return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
   __snake_case    					=					50
   print(F"The first {n} digits of pi is: {pi(n)}")