






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_ :						Union[str, Any]      =      {
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						List[Any]      =      [
	    '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''NezhaForNextSentencePrediction''',
	    '''NezhaForMaskedLM''',
	    '''NezhaForPreTraining''',
	    '''NezhaForMultipleChoice''',
	    '''NezhaForQuestionAnswering''',
	    '''NezhaForSequenceClassification''',
	    '''NezhaForTokenClassification''',
	    '''NezhaModel''',
	    '''NezhaPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nezha import (
		    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NezhaForMaskedLM,
		    NezhaForMultipleChoice,
		    NezhaForNextSentencePrediction,
		    NezhaForPreTraining,
		    NezhaForQuestionAnswering,
		    NezhaForSequenceClassification,
		    NezhaForTokenClassification,
		    NezhaModel,
		    NezhaPreTrainedModel,
		)


else:
	import sys

	UpperCAmelCase_ :						int      =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








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


UpperCAmelCase_ :						Optional[Any]      =      ['''bert-base-uncased''', '''bert-base-cased''']
UpperCAmelCase_ :						List[str]      =      '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():

	class     _SCREAMING_SNAKE_CASE       (			tf.keras.Model      ):





		def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any]	):
			super().__init__()
			UpperCamelCase     :Any          =	tokenizer
			UpperCamelCase     :List[str]          =	AutoConfig.from_pretrained(__lowerCamelCase	)
			UpperCamelCase     :List[str]          =	TFAutoModel.from_config(__lowerCamelCase	)





		def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					str	):
			UpperCamelCase     :str          =	self.tokenizer(__lowerCamelCase	)
			UpperCamelCase     :Any          =	self.bert(**__lowerCamelCase	)
			return out["pooler_output"]

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





	def 						_A (		self				:					Dict	):
		super().setUp()

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

		UpperCamelCase     :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""",
		]
		UpperCamelCase     :Union[str, Any]          =	list(zip(self.test_sentences ,  self.test_sentences[::-1]	)	)





	def 						_A (		self				:					Optional[int]	):
		for tokenizer, tf_tokenizer in zip(self.tokenizers ,  self.tf_tokenizers	):
			for test_inputs in (self.test_sentences, self.paired_sentences):
				UpperCamelCase     :Any          =	tokenizer(__lowerCamelCase ,  return_tensors="""tf""" ,  padding="""longest"""	)
				UpperCamelCase     :str          =	tf_tokenizer(__lowerCamelCase	)

				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 						_A (		self				:					Dict	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :str          =	tf_tokenizer(self.paired_sentences	)
			UpperCamelCase     :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 						_A (		self				:					List[str]	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :List[Any]          =	tf.function(__lowerCamelCase	)
			for test_inputs in (self.test_sentences, self.paired_sentences):
				UpperCamelCase     :Any          =	tf.constant(__lowerCamelCase	)
				UpperCamelCase     :List[str]          =	compiled_tokenizer(__lowerCamelCase	)
				UpperCamelCase     :Optional[Any]          =	tf_tokenizer(__lowerCamelCase	)

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





	@slow
	def 						_A (		self				:					Tuple	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :List[str]          =	ModelToSave(tokenizer=__lowerCamelCase	)
			UpperCamelCase     :Union[str, Any]          =	tf.convert_to_tensor(self.test_sentences	)
			UpperCamelCase     :Union[str, Any]          =	model(__lowerCamelCase	)  # Build model with some sample inputs
			with TemporaryDirectory() as tempdir:
				UpperCamelCase     :List[str]          =	Path(__lowerCamelCase	) / """saved.model"""
				model.save(__lowerCamelCase	)
				UpperCamelCase     :List[Any]          =	tf.keras.models.load_model(__lowerCamelCase	)
			UpperCamelCase     :Dict          =	loaded_model(__lowerCamelCase	)
			# 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	)
