





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 		UpperCamelCase__			(				tf.Module       ):



           '''simple docstring'''

           def __init__(							self						:   Union[str, Any]						,lowerCamelCase__						:   str	)					->		List[Any]:




                 '''simple docstring'''


                 super().__init__()
                 SCREAMING_SNAKE_CASE		=						tokenizer
                 SCREAMING_SNAKE_CASE		=						AutoConfig.from_pretrained(lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						TFGPTaLMHeadModel.from_config(lowerCamelCase__	)






           @tf.function(input_signature=(tf.TensorSpec((None,)						,tf.string						,name="""text"""	),)	)
           def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Dict	)					->		Dict:




                 '''simple docstring'''


                 SCREAMING_SNAKE_CASE		=						self.tokenizer(lowerCamelCase__	)
                 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=lowerCamelCase__						,attention_mask=lowerCamelCase__	)["""logits"""]

                 return outputs

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



     '''simple docstring'''

     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		str:




           '''simple docstring'''


           super().setUp()

           SCREAMING_SNAKE_CASE		=						[GPTaTokenizer.from_pretrained(lowerCamelCase__	) for checkpoint in (TOKENIZER_CHECKPOINTS)]
           SCREAMING_SNAKE_CASE		=						[TFGPTaTokenizer.from_pretrained(lowerCamelCase__	) 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     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		str:




           '''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(lowerCamelCase__						,tf.intaa	) == tf_outputs_values	)	)






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		int:




           '''simple docstring'''


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

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






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Dict:




           '''simple docstring'''


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




           '''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(lowerCamelCase__	)  # Build model with some sample inputs

                 SCREAMING_SNAKE_CASE		=						tf_tokenizer.get_config()
                 SCREAMING_SNAKE_CASE		=						TFGPTaTokenizer.from_config(lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						model_from_config(lowerCamelCase__	)

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






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		List[str]:




           '''simple docstring'''


           for tf_tokenizer in self.tf_tokenizers:
                 # for the test to run
                 SCREAMING_SNAKE_CASE		=						123123

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

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

                       assert out_length == max_length









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 1			,						_SCREAMING_SNAKE_CASE = 10_00     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						1
      SCREAMING_SNAKE_CASE		=						0

      for divide_by_number in range(_SCREAMING_SNAKE_CASE			,						digit + 1     ):
            SCREAMING_SNAKE_CASE		=						[]
            SCREAMING_SNAKE_CASE		=						numerator
            for _ in range(1			,						digit + 1     ):
                  if now_divide in has_been_divided:
                        if longest_list_length < len(_SCREAMING_SNAKE_CASE     ):
                              SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE     )
                              SCREAMING_SNAKE_CASE		=						divide_by_number
                  else:
                        has_been_divided.append(_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						now_divide * 10 % divide_by_number

      return the_digit


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()


