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


__lowerCamelCase    :				List[str]							     =	['''gpt2''']
__lowerCamelCase    :				str							     =	'''gpt2'''

if is_tf_available():







   class __snake_case       (		tf.Module	):




      def __init__(					self    :       str ,			_lowercase    :       str   ):



        """simple docstring"""






        super().__init__()
        SCREAMING_SNAKE_CASE__    							=   tokenizer
        SCREAMING_SNAKE_CASE__    							=   AutoConfig.from_pretrained(_lowercase   )
        SCREAMING_SNAKE_CASE__    							=   TFGPTaLMHeadModel.from_config(_lowercase   )




      @tf.function(input_signature=(tf.TensorSpec((None,) ,			tf.string ,			name="""text"""   ),)   )
      def 			__a		(					self    :       Dict ,			_lowercase    :       Optional[int]   ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase   )
        SCREAMING_SNAKE_CASE__    							=   tokenized["""input_ids"""].to_tensor()

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

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

        return outputs







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




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     super().setUp()

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

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




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






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

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

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




   @slow
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     for tf_tokenizer in self.tf_tokenizers:
       SCREAMING_SNAKE_CASE__    							=   tf.function(_lowercase   )
       for test_inputs in self.test_sentences:
         SCREAMING_SNAKE_CASE__    							=   tf.constant(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   compiled_tokenizer(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   tf_tokenizer(_lowercase   )

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




   @slow
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     for tf_tokenizer in self.tf_tokenizers:
       SCREAMING_SNAKE_CASE__    							=   ModelToSave(tokenizer=_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   tf.convert_to_tensor([self.test_sentences[0]]   )
       SCREAMING_SNAKE_CASE__    							=   model.serving(_lowercase   )  # Build model with some sample inputs
       with TemporaryDirectory() as tempdir:
         SCREAMING_SNAKE_CASE__    							=   Path(_lowercase   ) / """saved.model"""
         tf.saved_model.save(_lowercase ,			_lowercase ,			signatures={"""serving_default""": model.serving}   )
         SCREAMING_SNAKE_CASE__    							=   tf.saved_model.load(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   loaded_model.signatures["""serving_default"""](_lowercase   )["""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    :       str   ):



     """simple docstring"""






     for tf_tokenizer in self.tf_tokenizers:
       SCREAMING_SNAKE_CASE__    							=   tf.convert_to_tensor([self.test_sentences[0]]   )
       SCREAMING_SNAKE_CASE__    							=   tf_tokenizer(_lowercase   )  # Build model with some sample inputs

       SCREAMING_SNAKE_CASE__    							=   tf_tokenizer.get_config()
       SCREAMING_SNAKE_CASE__    							=   TFGPTaTokenizer.from_config(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   model_from_config(_lowercase   )

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




   @slow
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     for tf_tokenizer in self.tf_tokenizers:
       # for the test to run
       SCREAMING_SNAKE_CASE__    							=   12_31_23

       for max_length in [3, 5, 10_24]:
         SCREAMING_SNAKE_CASE__    							=   tf.convert_to_tensor([self.test_sentences[0]]   )
         SCREAMING_SNAKE_CASE__    							=   tf_tokenizer(_lowercase ,			max_length=_lowercase   )

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

         assert out_length == max_length

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1]

  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0, 0
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5

  for _ in range(1				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    ugly_nums.append(__UpperCamelCase			)
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5
  return ugly_nums[-1]


if __name__ == "__main__":
   from doctest import testmod

   testmod(verbose=True)
   print(F"""{ugly_numbers(200) = }""")
