

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


snake_case_			  = ['''gpt2''']
snake_case_			  = '''gpt2'''

if is_tf_available():


       class       SCREAMING_SNAKE_CASE__					(tf.Module     ):





           def __init__(      self						,   a):
                super().__init__()
                lowercase__    :   int      =       tokenizer
                lowercase__    :   Any      =       AutoConfig.from_pretrained(a)
                lowercase__    :   Optional[Any]      =       TFGPTaLMHeadModel.from_config(a)





           @tf.function(input_signature=(tf.TensorSpec((None,)						,   tf.string						,   name='text'),))
           def       snake_case_				(      self						,   a):
                lowercase__    :   List[str]      =       self.tokenizer(a)
                lowercase__    :   Tuple      =       tokenized['input_ids'].to_tensor()

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

                lowercase__    :   Tuple      =       self.model(input_ids=a						,   attention_mask=a)['logits']

                return outputs


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





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

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

         lowercase__    :   Optional[int]      =       [
             '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',
         ]
         lowercase__    :   int      =       list(zip(self.test_sentences						,   self.test_sentences[::-1]))





    def       snake_case_				(      self):
         for tokenizer, tf_tokenizer in zip(self.tokenizers						,   self.tf_tokenizers):
              for test_inputs in self.test_sentences:
                   lowercase__    :   Optional[Any]      =       tokenizer([test_inputs]						,   return_tensors='tf')
                   lowercase__    :   List[Any]      =       tf_tokenizer([test_inputs])

                   for key in python_outputs.keys():
                        # convert them to numpy to avoid messing with ragged tensors
                        lowercase__    :   int      =       python_outputs[key].numpy()
                        lowercase__    :   Any      =       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       snake_case_				(      self):
         for tf_tokenizer in self.tf_tokenizers:
              lowercase__    :   Optional[int]      =       tf.function(a)
              for test_inputs in self.test_sentences:
                   lowercase__    :   int      =       tf.constant(a)
                   lowercase__    :   Union[str, Any]      =       compiled_tokenizer(a)
                   lowercase__    :   int      =       tf_tokenizer(a)

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





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

              lowercase__    :   int      =       tf_tokenizer.get_config()
              lowercase__    :   int      =       TFGPTaTokenizer.from_config(a)
              lowercase__    :   Tuple      =       model_from_config(a)

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





    @slow
    def       snake_case_				(      self):
         for tf_tokenizer in self.tf_tokenizers:
              # for the test to run
              lowercase__    :   Any      =       12_3123

              for max_length in [3, 5, 1024]:
                   lowercase__    :   str      =       tf.convert_to_tensor([self.test_sentences[0]])
                   lowercase__    :   int      =       tf_tokenizer(a						,   max_length=a)

                   lowercase__    :   Optional[Any]      =       out['input_ids'].numpy().shape[1]

                   assert out_length == max_length




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if not all(x.isalpha() for x in string			):
          raise ValueError('String must only contain alphabetic characters.'			)

     lowercase__    :   Optional[int]      =       sorted(string.lower()			)
     return len(SCREAMING_SNAKE_CASE_			) == len(set(SCREAMING_SNAKE_CASE_			)			)


if __name__ == "__main__":
       snake_case_			  = input('''Enter a string ''').strip()

       snake_case_			  = is_isogram(input_str)
       print(F'''{input_str} is {'an' if isogram else 'not an'} isogram.''')

