


"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
     import tensorflow as tf

if is_tensorflow_text_available():
     from transformers.models.bert import TFBertTokenizer


lowerCAmelCase__				    =       ['''bert-base-uncased''', '''bert-base-cased''']
lowerCAmelCase__				    =       '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():


     class 				SCREAMING_SNAKE_CASE__    (		tf.keras.Model    ):
            """simple docstring"""


            def __init__(						self		,					snake_case__						):






              """simple docstring"""





              super().__init__()
              lowerCAmelCase					:					Tuple								=							tokenizer
              lowerCAmelCase					:					int								=							AutoConfig.from_pretrained(snake_case__						)
              lowerCAmelCase					:					Dict								=							TFAutoModel.from_config(snake_case__						)







            def 	lowercase__							(						self		,					snake_case__						):






              """simple docstring"""





              lowerCAmelCase					:					Any								=							self.tokenizer(snake_case__						)
              lowerCAmelCase					:					Optional[Any]								=							self.bert(**snake_case__						)
              return out["pooler_output"]


@require_tf
@require_tensorflow_text
class 				SCREAMING_SNAKE_CASE__    (		unittest.TestCase    ):
       """simple docstring"""


       def 	lowercase__							(						self						):






         """simple docstring"""





         super().setUp()

         lowerCAmelCase					:					Tuple								=							[
             BertTokenizer.from_pretrained(snake_case__						) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
         ]  # repeat for when fast_bert_tokenizer=false
         lowerCAmelCase					:					Optional[int]								=							[TFBertTokenizer.from_pretrained(snake_case__						) for checkpoint in TOKENIZER_CHECKPOINTS] + [
             TFBertTokenizer.from_pretrained(snake_case__		,					use_fast_bert_tokenizer=snake_case__						)
             for checkpoint in TOKENIZER_CHECKPOINTS
         ]
         assert len(self.tokenizers						) == len(self.tf_tokenizers						)

         lowerCAmelCase					:					Union[str, 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					:					int								=							list(zip(self.test_sentences		,					self.test_sentences[::-1]						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         for tokenizer, tf_tokenizer in zip(self.tokenizers		,					self.tf_tokenizers						):
           for test_inputs in (self.test_sentences, self.paired_sentences):
             lowerCAmelCase					:					List[str]								=							tokenizer(snake_case__		,					return_tensors="tf"		,					padding="longest"						)
             lowerCAmelCase					:					str								=							tf_tokenizer(snake_case__						)

             for key in python_outputs.keys():
               self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape						)						)
               self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]		,					tf.intaa						) == tf_outputs[key]						)						)

       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase					:					Dict								=							tf_tokenizer(self.paired_sentences						)
           lowerCAmelCase					:					Union[str, Any]								=							tf_tokenizer(
               text=[sentence[0] for sentence in self.paired_sentences]		,					text_pair=[sentence[1] for sentence in self.paired_sentences]		,					)
           for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]		,					tf.intaa						) == separated_outputs[key]						)						)

       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase					:					List[Any]								=							tf.function(snake_case__						)
           for test_inputs in (self.test_sentences, self.paired_sentences):
             lowerCAmelCase					:					Union[str, Any]								=							tf.constant(snake_case__						)
             lowerCAmelCase					:					Dict								=							compiled_tokenizer(snake_case__						)
             lowerCAmelCase					:					str								=							tf_tokenizer(snake_case__						)

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







       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase					:					Optional[int]								=							ModelToSave(tokenizer=snake_case__						)
           lowerCAmelCase					:					Optional[Any]								=							tf.convert_to_tensor(self.test_sentences						)
           lowerCAmelCase					:					Dict								=							model(snake_case__						)  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
             lowerCAmelCase					:					Tuple								=							Path(snake_case__						) / "saved.model"
             model.save(snake_case__						)
             lowerCAmelCase					:					Optional[Any]								=							tf.keras.models.load_model(snake_case__						)
           lowerCAmelCase					:					int								=							loaded_model(snake_case__						)
           # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
           self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output						)						)		,					1e-5						)









"""simple docstring"""







from typing import Any


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							data
         lowerCAmelCase					:					Tuple								=							None


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							None

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.head
         while temp is not None:
           print(temp.data		,					end=" "						)
           lowerCAmelCase					:					List[str]								=							temp.next
         print()

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							Node(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							self.head
         lowerCAmelCase					:					Optional[Any]								=							new_node







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         if node_data_a == node_data_a:
           return
         else:
           lowerCAmelCase					:					str								=							self.head
           while node_a is not None and node_a.data != node_data_a:
             lowerCAmelCase					:					Union[str, Any]								=							node_a.next

           lowerCAmelCase					:					Any								=							self.head
           while node_a is not None and node_a.data != node_data_a:
             lowerCAmelCase					:					List[Any]								=							node_a.next

           if node_a is None or node_a is None:
             return

           lowerCAmelCase						,  lowerCAmelCase					:					str								=							node_a.data, node_a.data


if __name__ == "__main__":
     lowerCAmelCase__				    =       LinkedList()
     for i in range(5, 0, -1):
          ll.push(i)

     ll.print_list()

     ll.swap_nodes(1, 4)
     print('''After swapping''')
     ll.print_list()





