

'''simple docstring'''







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 					A__      (      tf.Module			):



       def __init__(					self		,				UpperCamelCase__       )						->      Tuple:





              '''simple docstring'''
              super().__init__()
              A_						=      tokenizer
              A_						=      AutoConfig.from_pretrained(__UpperCamelCase       )
              A_						=      TFGPTaLMHeadModel.from_config(__UpperCamelCase       )







       @tf.function(input_signature=(tf.TensorSpec((None,)		,				tf.string		,				name="""text"""       ),)       )
       def 					snake_case_			(					self		,				UpperCamelCase__       )						->      int:





              '''simple docstring'''
              A_						=      self.tokenizer(__UpperCamelCase       )
              A_						=      tokenized["""input_ids"""].to_tensor()

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

              A_						=      self.model(input_ids=__UpperCamelCase		,				attention_mask=__UpperCamelCase       )["""logits"""]

              return outputs


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



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





           '''simple docstring'''
           super().setUp()

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

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



    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''






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

                         for key in python_outputs.keys():
                                # convert them to numpy to avoid messing with ragged tensors
                                A_						=      python_outputs[key].numpy()
                                A_						=      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 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''






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

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



    @slow
    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''






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





           '''simple docstring'''






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

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

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







    @slow
    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''






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

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

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

                         assert out_length == max_length



'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase		       =    logging.get_logger(__name__)


class 					A__      (      _snake_case			):



    def __init__(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      None:





           '''simple docstring'''
           warnings.warn(
               """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
               """ use BeitImageProcessor instead."""		,				UpperCamelCase__		,				)
           super().__init__(*UpperCamelCase__		,				**UpperCamelCase__       )
