




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							:						Optional[int]					       =  ["gpt2"]
lowerCamelCase							:						Any					       =  "gpt2"

if is_tf_available():







 class     A(		tf.Module  ):



  '''simple docstring'''


  def __init__(							self   :    Optional[int]		,				A_   :    List[Any]    )  ->      Tuple:

        """simple docstring"""






        super().__init__()
        lowerCamelCase_							=       tokenizer
        lowerCamelCase_							=       AutoConfig.from_pretrained(A_    )
        lowerCamelCase_							=       TFGPTaLMHeadModel.from_config(A_    )





  @tf.function(input_signature=(tf.TensorSpec((None,)		,				tf.string		,				name='text'    ),)    )
  def 							a__					(							self   :    Tuple		,				A_   :    List[str]    )  ->      str:

        """simple docstring"""






        lowerCamelCase_							=       self.tokenizer(A_    )
        lowerCamelCase_							=       tokenized['input_ids'].to_tensor()

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

        lowerCamelCase_							=       self.model(input_ids=A_		,				attention_mask=A_    )['logits']

        return outputs







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



 '''simple docstring'''


 def 							a__					(							self   :    Any    )  ->      Optional[Any]:

       """simple docstring"""






       super().setUp()

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

       lowerCamelCase_							=       [
           '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_							=       list(zip(self.test_sentences		,				self.test_sentences[::-1]    )    )

 def 							a__					(							self   :    Optional[int]    )  ->      Union[str, Any]:

       """simple docstring"""






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

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

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

 @slow
 def 							a__					(							self   :    Any    )  ->      Dict:

       """simple docstring"""






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

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

 @slow
 def 							a__					(							self   :    Tuple    )  ->      Optional[int]:

       """simple docstring"""






       for tf_tokenizer in self.tf_tokenizers:
             lowerCamelCase_							=       ModelToSave(tokenizer=A_    )
             lowerCamelCase_							=       tf.convert_to_tensor([self.test_sentences[0]]    )
             lowerCamelCase_							=       model.serving(A_    )  # Build model with some sample inputs
             with TemporaryDirectory() as tempdir:
                   lowerCamelCase_							=       Path(A_    ) / 'saved.model'
                   tf.saved_model.save(A_		,				A_		,				signatures={'serving_default': model.serving}    )
                   lowerCamelCase_							=       tf.saved_model.load(A_    )
             lowerCamelCase_							=       loaded_model.signatures['serving_default'](A_    )['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   :    List[str]    )  ->      Optional[int]:

       """simple docstring"""






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

             lowerCamelCase_							=       tf_tokenizer.get_config()
             lowerCamelCase_							=       TFGPTaTokenizer.from_config(A_    )
             lowerCamelCase_							=       model_from_config(A_    )

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





 @slow
 def 							a__					(							self   :    List[Any]    )  ->      Any:

       """simple docstring"""






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

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

                   lowerCamelCase_							=       out['input_ids'].numpy().shape[1]

                   assert out_length == max_length










import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # Initialise PyTorch model
       A__		   =		BigBirdConfig.from_json_file(UpperCamelCase__    )
       print(f"Building PyTorch model from configuration: {config}"    )

       if is_trivia_qa:
              A__		   =		BigBirdForQuestionAnswering(UpperCamelCase__    )
       else:
              A__		   =		BigBirdForPreTraining(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_big_bird(UpperCamelCase__					,	UpperCamelCase__					,	is_trivia_qa=UpperCamelCase__    )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"    )
       model.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
       a__: Optional[int]							   =						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--big_bird_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       parser.add_argument(
           '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
       )
       a__: Tuple							   =						parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





