



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


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

if is_tf_available():






    class 						__a (     tf.keras.Model				):



     def __init__(						self							:    str				,	snake_case_							:    Dict)->							Any:
          super().__init__()
          __lowerCAmelCase	   =tokenizer
          __lowerCAmelCase	   =AutoConfig.from_pretrained(snake_case_)
          __lowerCAmelCase	   =TFAutoModel.from_config(snake_case_)



     def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    Any)->							List[Any]:
          __lowerCAmelCase	   =self.tokenizer(snake_case_)
          __lowerCAmelCase	   =self.bert(**snake_case_)
          return out["pooler_output"]






@require_tf
@require_tensorflow_text
class 						__a (     unittest.TestCase				):



 def 		UpperCamelCase							(						self							:    List[str])->							List[Any]:
      super().setUp()

      __lowerCAmelCase	   =[
          BertTokenizer.from_pretrained(snake_case_) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
      ]  # repeat for when fast_bert_tokenizer=false
      __lowerCAmelCase	   =[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	   =[
          """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(zip(self.test_sentences				,	self.test_sentences[::-1]))



 def 		UpperCamelCase							(						self							:    str)->							Optional[Any]:
      for tokenizer, tf_tokenizer in zip(self.tokenizers				,	self.tf_tokenizers):
           for test_inputs in (self.test_sentences, self.paired_sentences):
                __lowerCAmelCase	   =tokenizer(snake_case_				,	return_tensors="""tf"""				,	padding="""longest""")
                __lowerCAmelCase	   =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 		UpperCamelCase							(						self							:    Tuple)->							str:
      for tf_tokenizer in self.tf_tokenizers:
           __lowerCAmelCase	   =tf_tokenizer(self.paired_sentences)
           __lowerCAmelCase	   =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 		UpperCamelCase							(						self							:    List[Any])->							Optional[int]:
      for tf_tokenizer in self.tf_tokenizers:
           __lowerCAmelCase	   =tf.function(snake_case_)
           for test_inputs in (self.test_sentences, self.paired_sentences):
                __lowerCAmelCase	   =tf.constant(snake_case_)
                __lowerCAmelCase	   =compiled_tokenizer(snake_case_)
                __lowerCAmelCase	   =tf_tokenizer(snake_case_)

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



 @slow
 def 		UpperCamelCase							(						self							:    Optional[Any])->							Optional[int]:
      for tf_tokenizer in self.tf_tokenizers:
           __lowerCAmelCase	   =ModelToSave(tokenizer=snake_case_)
           __lowerCAmelCase	   =tf.convert_to_tensor(self.test_sentences)
           __lowerCAmelCase	   =model(snake_case_)  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
                __lowerCAmelCase	   =Path(snake_case_) / """saved.model"""
                model.save(snake_case_)
                __lowerCAmelCase	   =tf.keras.models.load_model(snake_case_)
           __lowerCAmelCase	   =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)









import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowercase_	       =			logging.get_logger(__name__)






class 						__a (     SCREAMING_SNAKE_CASE				):



	def __init__(						self							:    Optional[Any]				,	*snake_case_							:    List[str]				,	**snake_case_							:    Union[str, Any])->							None:
						warnings.warn(
						    """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
						    """ Please use PoolFormerImageProcessor instead."""				,	snake_case_				,	)
						super().__init__(*snake_case_				,	**snake_case_)




