
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__							:							List[str]									=			["bert-base-uncased", "bert-base-cased"]
lowercase__							:							Tuple									=			"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():






				class        SCREAMING_SNAKE_CASE__    (							tf.keras.Model    ):

											"""simple docstring"""






											def __init__(  self     ,	SCREAMING_SNAKE_CASE_			)->   Tuple:





																	'''simple docstring'''






																	super().__init__()
																	__UpperCamelCase       =			tokenizer
																	__UpperCamelCase       =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
																	__UpperCamelCase       =			TFAutoModel.from_config(SCREAMING_SNAKE_CASE_			)



											def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Dict:





																	'''simple docstring'''






																	__UpperCamelCase       =			self.tokenizer(SCREAMING_SNAKE_CASE_			)
																	__UpperCamelCase       =			self.bert(**SCREAMING_SNAKE_CASE_			)
																	return out["pooler_output"]






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

							"""simple docstring"""






							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													super().setUp()

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

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



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													for tokenizer, tf_tokenizer in zip(self.tokenizers     ,	self.tf_tokenizers			):
																			for test_inputs in (self.test_sentences, self.paired_sentences):
																									__UpperCamelCase       =			tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors='''tf'''     ,	padding='''longest'''			)
																									__UpperCamelCase       =			tf_tokenizer(SCREAMING_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     A__		(  self			)->   Tuple:





													'''simple docstring'''






													for tf_tokenizer in self.tf_tokenizers:
																			__UpperCamelCase       =			tf_tokenizer(self.paired_sentences			)
																			__UpperCamelCase       =			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]:





													'''simple docstring'''






													for tf_tokenizer in self.tf_tokenizers:
																			__UpperCamelCase       =			tf.function(SCREAMING_SNAKE_CASE_			)
																			for test_inputs in (self.test_sentences, self.paired_sentences):
																									__UpperCamelCase       =			tf.constant(SCREAMING_SNAKE_CASE_			)
																									__UpperCamelCase       =			compiled_tokenizer(SCREAMING_SNAKE_CASE_			)
																									__UpperCamelCase       =			tf_tokenizer(SCREAMING_SNAKE_CASE_			)

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



							@slow
							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													for tf_tokenizer in self.tf_tokenizers:
																			__UpperCamelCase       =			ModelToSave(tokenizer=SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			tf.convert_to_tensor(self.test_sentences			)
																			__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)  # Build model with some sample inputs
																			with TemporaryDirectory() as tempdir:
																									__UpperCamelCase       =			Path(SCREAMING_SNAKE_CASE_			) / '''saved.model'''
																									model.save(SCREAMING_SNAKE_CASE_			)
																									__UpperCamelCase       =			tf.keras.models.load_model(SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			loaded_model(SCREAMING_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 unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax






@require_flax
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  FlaxAutoencoderKL



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			4
													__UpperCamelCase       =			3
													__UpperCamelCase       =			(32, 32)

													__UpperCamelCase       =			jax.random.PRNGKey(0			)
													__UpperCamelCase       =			jax.random.uniform(SCREAMING_SNAKE_CASE_     ,	((batch_size, num_channels) + sizes)			)

													return {"sample": image, "prng_key": prng_key}



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			{
													    '''block_out_channels''': [32, 64],
													    '''in_channels''': 3,
													    '''out_channels''': 3,
													    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
													    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
													    '''latent_channels''': 4,
													}
													__UpperCamelCase       =			self.dummy_input
													return init_dict, inputs_dict




