


"""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


snake_case__		:     List[Any]    				=				['''gpt2''']
snake_case__		:     Optional[int]    				=				'''gpt2'''

if is_tf_available():



 class 		snake_case_(       tf.Module			):






      def __init__(	self  :					Optional[Any]					,				UpperCamelCase_  :					List[str]				):
            super().__init__()
            lowerCAmelCase					:	Any       =   tokenizer
            lowerCAmelCase					:	Any       =   AutoConfig.from_pretrained(UpperCamelCase_				)
            lowerCAmelCase					:	Optional[int]       =   TFGPTaLMHeadModel.from_config(UpperCamelCase_				)






      @tf.function(input_signature=(tf.TensorSpec((None,)					,				tf.string					,				name='''text'''				),)				)
      def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					List[str]				):
            lowerCAmelCase					:	Dict       =   self.tokenizer(UpperCamelCase_				)
            lowerCAmelCase					:	List[str]       =   tokenized['''input_ids'''].to_tensor()

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

            lowerCAmelCase					:	Any       =   self.model(input_ids=UpperCamelCase_					,				attention_mask=UpperCamelCase_				)['''logits''']

            return outputs



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






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           super().setUp()

           lowerCAmelCase					:	Optional[Any]       =   [GPTaTokenizer.from_pretrained(UpperCamelCase_				) for checkpoint in (TOKENIZER_CHECKPOINTS)]
           lowerCAmelCase					:	Tuple       =   [TFGPTaTokenizer.from_pretrained(UpperCamelCase_				) for checkpoint in TOKENIZER_CHECKPOINTS]
           assert len(self.tokenizers				) == len(self.tf_tokenizers				)

           lowerCAmelCase					:	Any       =   [
               '''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[Any]       =   list(zip(self.test_sentences					,				self.test_sentences[::-1]				)				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           for tokenizer, tf_tokenizer in zip(self.tokenizers					,				self.tf_tokenizers				):
                 for test_inputs in self.test_sentences:
                       lowerCAmelCase					:	Tuple       =   tokenizer([test_inputs]					,				return_tensors='''tf'''				)
                       lowerCAmelCase					:	Optional[int]       =   tf_tokenizer([test_inputs]				)

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

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






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

                 lowerCAmelCase					:	Optional[int]       =   tf_tokenizer.get_config()
                 lowerCAmelCase					:	int       =   TFGPTaTokenizer.from_config(UpperCamelCase_				)
                 lowerCAmelCase					:	List[str]       =   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  :					Optional[Any]				):
           for tf_tokenizer in self.tf_tokenizers:
                 # for the test to run
                 lowerCAmelCase					:	str       =   1_2_3_1_2_3

                 for max_length in [3, 5, 1_0_2_4]:
                       lowerCAmelCase					:	Optional[int]       =   tf.convert_to_tensor([self.test_sentences[0]]				)
                       lowerCAmelCase					:	Any       =   tf_tokenizer(UpperCamelCase_					,				max_length=UpperCamelCase_				)

                       lowerCAmelCase					:	Dict       =   out['''input_ids'''].numpy().shape[1]

                       assert out_length == max_length






"""simple docstring"""





# using dfs for finding eulerian path traversal


def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			List[Any] ,   _snake_case				:			str ,   _snake_case				:			List[Any]=None			):
      lowerCAmelCase					:	Any       =   (path or []) + [u]
      for v in graph[u]:
            if visited_edge[u][v] is False:
                  lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   True, True
                  lowerCAmelCase					:	int       =   dfs(_snake_case ,   _snake_case ,   _snake_case ,   _snake_case			)
      return path


def      _snake_case						(						_snake_case				:			Optional[int] ,   _snake_case				:			Dict			):
      lowerCAmelCase					:	Tuple       =   0
      lowerCAmelCase					:	Optional[Any]       =   -1
      for i in range(_snake_case			):
            if i not in graph.keys():
                  continue
            if len(graph[i]			) % 2 == 1:
                  odd_degree_nodes += 1
                  lowerCAmelCase					:	Optional[Any]       =   i
      if odd_degree_nodes == 0:
            return 1, odd_node
      if odd_degree_nodes == 2:
            return 2, odd_node
      return 3, odd_node


def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			List[Any]			):
      lowerCAmelCase					:	Any       =   [[False for _ in range(max_node + 1			)] for _ in range(max_node + 1			)]
      lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   check_circuit_or_path(_snake_case ,   _snake_case			)
      if check == 3:
            print('''graph is not Eulerian'''			)
            print('''no path'''			)
            return
      lowerCAmelCase					:	Dict       =   1
      if check == 2:
            lowerCAmelCase					:	int       =   odd_node
            print('''graph has a Euler path'''			)
      if check == 1:
            print('''graph has a Euler cycle'''			)
      lowerCAmelCase					:	List[str]       =   dfs(_snake_case ,   _snake_case ,   _snake_case			)
      print(_snake_case			)


def      _snake_case						(						):
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
      lowerCAmelCase					:	Union[str, Any]       =   {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
      lowerCAmelCase					:	List[Any]       =   {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3], 2: [1, 3], 3: [1, 2]}
      lowerCAmelCase					:	Any       =   {
          1: [],
          2: []
          # all degree is zero
      }
      lowerCAmelCase					:	List[str]       =   10
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)


if __name__ == "__main__":
 main()


