





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__:      Optional[Any]									=		['gpt2']
a__:      Optional[Any]									=		'gpt2'

if is_tf_available():




       class     SCREAMING_SNAKE_CASE__ (						tf.Module      ):



        def __init__(							self,__lowerCamelCase ):
               super().__init__()
               A__											=						tokenizer
               A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
               A__											=						TFGPTaLMHeadModel.from_config(__lowerCamelCase )




        @tf.function(input_signature=(tf.TensorSpec((None,),tf.string,name='''text''' ),) )
        def 				UpperCamelCase			(							self,__lowerCamelCase ):
               A__											=						self.tokenizer(__lowerCamelCase )
               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=__lowerCamelCase,attention_mask=__lowerCamelCase )['''logits''']

               return outputs




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



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

        A__											=						[GPTaTokenizer.from_pretrained(__lowerCamelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
        A__											=						[TFGPTaTokenizer.from_pretrained(__lowerCamelCase ) 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 				UpperCamelCase			(							self ):
        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(__lowerCamelCase,tf.intaa ) == tf_outputs_values ) )



 @slow
 def 				UpperCamelCase			(							self ):
        for tf_tokenizer in self.tf_tokenizers:
               A__											=						tf.function(__lowerCamelCase )
               for test_inputs in self.test_sentences:
                      A__											=						tf.constant(__lowerCamelCase )
                      A__											=						compiled_tokenizer(__lowerCamelCase )
                      A__											=						tf_tokenizer(__lowerCamelCase )

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



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

               A__											=						tf_tokenizer.get_config()
               A__											=						TFGPTaTokenizer.from_config(__lowerCamelCase )
               A__											=						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 ):
        for tf_tokenizer in self.tf_tokenizers:
               # for the test to run
               A__											=						12_3123

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

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

                      assert out_length == max_length












class     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase ):
        A__											=						set_counts
        A__											=						max(__lowerCamelCase )
        A__											=						len(__lowerCamelCase )
        A__											=						[1] * num_sets
        A__											=						list(range(__lowerCamelCase ) )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.get_parent(__lowerCamelCase )
        A__											=						self.get_parent(__lowerCamelCase )

        if src_parent == dst_parent:
               return False

        if self.ranks[dst_parent] >= self.ranks[src_parent]:
               self.set_counts[dst_parent] += self.set_counts[src_parent]
               A__											=						0
               A__											=						dst_parent
               if self.ranks[dst_parent] == self.ranks[src_parent]:
                      self.ranks[dst_parent] += 1
               A__											=						self.set_counts[dst_parent]
        else:
               self.set_counts[src_parent] += self.set_counts[dst_parent]
               A__											=						0
               A__											=						src_parent
               A__											=						self.set_counts[src_parent]

        A__											=						max(self.max_set,__lowerCamelCase )
        return True




 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        if self.parents[disj_set] == disj_set:
               return disj_set
        A__											=						self.get_parent(self.parents[disj_set] )
        return self.parents[disj_set]





