




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_					    = ["""gpt2"""]
lowerCAmelCase_					    = """gpt2"""

if is_tf_available():






     class  _lowerCAmelCase     (  tf.Module	):

           def __init__(   self      ,				__UpperCAmelCase		):
             super().__init__()
             lowerCAmelCase__				:  Any		       =     tokenizer
             lowerCAmelCase__				:  Union[str, Any]		       =     AutoConfig.from_pretrained(__UpperCAmelCase		)
             lowerCAmelCase__				:  List[Any]		       =     TFGPTaLMHeadModel.from_config(__UpperCAmelCase		)







           @tf.function(input_signature=(tf.TensorSpec((None,)      ,				tf.string      ,				name='''text'''		),)		)
           def 	__magic_name__(   self      ,				__UpperCAmelCase		):
             lowerCAmelCase__				:  Optional[Any]		       =     self.tokenizer(__UpperCAmelCase		)
             lowerCAmelCase__				:  Dict		       =     tokenized['''input_ids'''].to_tensor()

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

             lowerCAmelCase__				:  str		       =     self.model(input_ids=__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase		)['''logits''']

             return outputs






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

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

        lowerCAmelCase__				:  Optional[int]		       =     [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__				:  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''',
        ]
        lowerCAmelCase__				:  Any		       =     list(zip(self.test_sentences      ,				self.test_sentences[::-1]		)		)

      def 	__magic_name__(   self		):
        for tokenizer, tf_tokenizer in zip(self.tokenizers      ,				self.tf_tokenizers		):
          for test_inputs in self.test_sentences:
            lowerCAmelCase__				:  int		       =     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__				:  Dict		       =     python_outputs[key].numpy()
              lowerCAmelCase__				:  List[str]		       =     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 	__magic_name__(   self		):
        for tf_tokenizer in self.tf_tokenizers:
          lowerCAmelCase__				:  Optional[int]		       =     tf.function(__UpperCAmelCase		)
          for test_inputs in self.test_sentences:
            lowerCAmelCase__				:  List[str]		       =     tf.constant(__UpperCAmelCase		)
            lowerCAmelCase__				:  Optional[int]		       =     compiled_tokenizer(__UpperCAmelCase		)
            lowerCAmelCase__				:  Tuple		       =     tf_tokenizer(__UpperCAmelCase		)

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

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

          lowerCAmelCase__				:  Any		       =     tf_tokenizer.get_config()
          lowerCAmelCase__				:  List[str]		       =     TFGPTaTokenizer.from_config(__UpperCAmelCase		)
          lowerCAmelCase__				:  Dict		       =     model_from_config(__UpperCAmelCase		)

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







      @slow
      def 	__magic_name__(   self		):
        for tf_tokenizer in self.tf_tokenizers:
          # for the test to run
          lowerCAmelCase__				:  Any		       =     12_3123

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

            lowerCAmelCase__				:  List[Any]		       =     out['''input_ids'''].numpy().shape[1]

            assert out_length == max_length







from abc import ABC, abstractmethod
from argparse import ArgumentParser






class  _lowerCAmelCase     (  _lowercase	):

      @staticmethod
      @abstractmethod
      def 	__magic_name__(   __UpperCAmelCase		):
        raise NotImplementedError()







      @abstractmethod
      def 	__magic_name__(   self		):
        raise NotImplementedError()

