





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__:    int  	=	['''gpt2''']
A__:    List[str]  	=	'''gpt2'''

if is_tf_available():




   class        _a    (    tf.Module):

         """simple docstring"""


         def __init__(		self:			List[Any]	,    __lowerCamelCase:			Tuple					):
             '''simple docstring'''


             super().__init__()
             UpperCamelCase__:	List[Any]					=     tokenizer
             UpperCamelCase__:	List[str]					=     AutoConfig.from_pretrained(__lowerCamelCase					)
             UpperCamelCase__:	str					=     TFGPTaLMHeadModel.from_config(__lowerCamelCase					)






         @tf.function(input_signature=(tf.TensorSpec((None,)	,    tf.string	,    name="text"					),)					)
         def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			int					):
             '''simple docstring'''


             UpperCamelCase__:	Union[str, Any]					=     self.tokenizer(__lowerCamelCase					)
             UpperCamelCase__:	Dict					=     tokenized["input_ids"].to_tensor()

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

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

             return outputs




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

      """simple docstring"""


      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          super().setUp()

          UpperCamelCase__:	List[Any]					=     [GPTaTokenizer.from_pretrained(__lowerCamelCase					) for checkpoint in (TOKENIZER_CHECKPOINTS)]
          UpperCamelCase__:	Any					=     [TFGPTaTokenizer.from_pretrained(__lowerCamelCase					) for checkpoint in TOKENIZER_CHECKPOINTS]
          assert len(self.tokenizers					) == len(self.tf_tokenizers					)

          UpperCamelCase__:	str					=     [
              "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",
          ]
          UpperCamelCase__:	Dict					=     list(zip(self.test_sentences	,    self.test_sentences[::-1]					)					)






      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


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

                  for key in python_outputs.keys():
                      # convert them to numpy to avoid messing with ragged tensors
                      UpperCamelCase__:	Any					=     python_outputs[key].numpy()
                      UpperCamelCase__:	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(__lowerCamelCase	,    tf.intaa					) == tf_outputs_values					)					)






      @slow
      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


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

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






      @slow
      def 			UpperCAmelCase_       (		self:			int					):
          '''simple docstring'''


          for tf_tokenizer in self.tf_tokenizers:
              UpperCamelCase__:	Union[str, Any]					=     ModelToSave(tokenizer=__lowerCamelCase					)
              UpperCamelCase__:	Optional[int]					=     tf.convert_to_tensor([self.test_sentences[0]]					)
              UpperCamelCase__:	int					=     model.serving(__lowerCamelCase					)  # Build model with some sample inputs
              with TemporaryDirectory() as tempdir:
                  UpperCamelCase__:	Dict					=     Path(__lowerCamelCase					) / "saved.model"
                  tf.saved_model.save(__lowerCamelCase	,    __lowerCamelCase	,    signatures={"serving_default": model.serving}					)
                  UpperCamelCase__:	str					=     tf.saved_model.load(__lowerCamelCase					)
              UpperCamelCase__:	Tuple					=     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[Any]					):
          '''simple docstring'''


          for tf_tokenizer in self.tf_tokenizers:
              UpperCamelCase__:	Union[str, Any]					=     tf.convert_to_tensor([self.test_sentences[0]]					)
              UpperCamelCase__:	Any					=     tf_tokenizer(__lowerCamelCase					)  # Build model with some sample inputs

              UpperCamelCase__:	str					=     tf_tokenizer.get_config()
              UpperCamelCase__:	int					=     TFGPTaTokenizer.from_config(__lowerCamelCase					)
              UpperCamelCase__:	Optional[int]					=     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:			List[str]					):
          '''simple docstring'''


          for tf_tokenizer in self.tf_tokenizers:
              # for the test to run
              UpperCamelCase__:	int					=     12_3123

              for max_length in [3, 5, 1024]:
                  UpperCamelCase__:	Tuple					=     tf.convert_to_tensor([self.test_sentences[0]]					)
                  UpperCamelCase__:	Optional[Any]					=     tf_tokenizer(__lowerCamelCase	,    max_length=__lowerCamelCase					)

                  UpperCamelCase__:	Union[str, Any]					=     out["input_ids"].numpy().shape[1]

                  assert out_length == max_length








import logging

from transformers import PretrainedConfig


A__:    Dict  	=	logging.getLogger(__name__)


A__:    List[Any]  	=	{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       """bertabs"""






      def __init__(		self:			Any	,    __lowerCamelCase:			Optional[int]=3_0522	,    __lowerCamelCase:			List[Any]=512	,    __lowerCamelCase:			Optional[Any]=6	,    __lowerCamelCase:			Optional[Any]=512	,    __lowerCamelCase:			List[str]=8	,    __lowerCamelCase:			List[Any]=512	,    __lowerCamelCase:			Optional[int]=0.2	,    __lowerCamelCase:			Optional[int]=6	,    __lowerCamelCase:			Optional[Any]=768	,    __lowerCamelCase:			int=8	,    __lowerCamelCase:			Union[str, Any]=2048	,    __lowerCamelCase:			Dict=0.2	,    **__lowerCamelCase:			Dict	,    ):
          '''simple docstring'''


          super().__init__(**__lowerCamelCase					)

          UpperCamelCase__:	str					=     vocab_size
          UpperCamelCase__:	Tuple					=     max_pos

          UpperCamelCase__:	Union[str, Any]					=     enc_layers
          UpperCamelCase__:	Optional[Any]					=     enc_hidden_size
          UpperCamelCase__:	Optional[Any]					=     enc_heads
          UpperCamelCase__:	Dict					=     enc_ff_size
          UpperCamelCase__:	Tuple					=     enc_dropout

          UpperCamelCase__:	Tuple					=     dec_layers
          UpperCamelCase__:	int					=     dec_hidden_size
          UpperCamelCase__:	int					=     dec_heads
          UpperCamelCase__:	str					=     dec_ff_size
          UpperCamelCase__:	Union[str, Any]					=     dec_dropout

