






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   :							List[str]						= ["gpt2"]
__a   :							Dict						= "gpt2"

if is_tf_available():


  class    __lowercase   (						tf.Module	):







       '''simple docstring'''




       def __init__(					self							:      List[str]			,						UpperCamelCase_							:      Optional[int]					):


           """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 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      Tuple					):


           """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    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """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 		lowerCAmelCase_  (					self							:      List[str]					):


         """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 		lowerCAmelCase_  (					self							:      str					):


         """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 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """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 		lowerCAmelCase_  (					self							:      str					):


         """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 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         for tf_tokenizer in self.tf_tokenizers:
             # for the test to run
             __A       =						123_123

             for max_length in [3, 5, 1_024]:
                 __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












import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__a   :							Any						= logging.get_logger(__name__)


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Tuple			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         warnings.warn(
             """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use DeiTImageProcessor instead."""			,						UpperCamelCase_			,						)
         super().__init__(*UpperCamelCase_			,						**UpperCamelCase_					)




