





'''simple docstring'''




import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 				__snake_case     (					__magic_name__       ,       __magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  AutoencoderKL
	__lowerCAmelCase       						=  '''sample'''
	__lowerCAmelCase       						=  1E-2





	@property
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   4
		snake_case__	       =   3
		snake_case__	       =   (32, 32)

		snake_case__	       =   floats_tensor((batch_size, num_channels) + sizes		).to(UpperCamelCase_		)

		return {"sample": image}





	@property
	def   _snake_case   (		self		)      ->		List[Any]:
		return (3, 32, 32)





	@property
	def   _snake_case   (		self		)      ->		Any:
		return (3, 32, 32)





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   {
		    'block_out_channels': [32, 64],
		    'in_channels': 3,
		    'out_channels': 3,
		    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
		    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
		    'latent_channels': 4,
		}
		snake_case__	       =   self.dummy_input
		return init_dict, inputs_dict





	def   _snake_case   (		self		)      ->		Any:
		pass





	def   _snake_case   (		self		)      ->		Optional[int]:
		pass





	@unittest.skipIf(torch_device == 'mps'      ,       'Gradient checkpointing skipped on MPS'		)
	def   _snake_case   (		self		)      ->		List[str]:
		# enable deterministic behavior for gradient checkpointing
		snake_case__       ,  snake_case__	       =   self.prepare_init_args_and_inputs_for_common()
		snake_case__	       =   self.model_class(**UpperCamelCase_		)
		model.to(UpperCamelCase_		)

		assert not model.is_gradient_checkpointing and model.training

		snake_case__	       =   model(**UpperCamelCase_		).sample
		# run the backwards pass on the model. For backwards pass, for simplicity purpose,
		# we won't calculate the loss and rather backprop on out.sum()
		model.zero_grad()

		snake_case__	       =   torch.randn_like(UpperCamelCase_		)
		snake_case__	       =   (out - labels).mean()
		loss.backward()

		# re-instantiate the model now enabling gradient checkpointing
		snake_case__	       =   self.model_class(**UpperCamelCase_		)
		# clone model
		model_a.load_state_dict(model.state_dict()		)
		model_a.to(UpperCamelCase_		)
		model_a.enable_gradient_checkpointing()

		assert model_a.is_gradient_checkpointing and model_a.training

		snake_case__	       =   model_a(**UpperCamelCase_		).sample
		# run the backwards pass on the model. For backwards pass, for simplicity purpose,
		# we won't calculate the loss and rather backprop on out.sum()
		model_a.zero_grad()
		snake_case__	       =   (out_a - labels).mean()
		loss_a.backward()

		# compare the output and parameters gradients
		self.assertTrue((loss - loss_a).abs() < 1E-5		)
		snake_case__	       =   dict(model.named_parameters()		)
		snake_case__	       =   dict(model_a.named_parameters()		)
		for name, param in named_params.items():
			self.assertTrue(torch_all_close(param.grad.data      ,       named_params_a[name].grad.data      ,       atol=5E-5		)		)





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__       ,  snake_case__	       =   AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'      ,       output_loading_info=UpperCamelCase_		)
		self.assertIsNotNone(UpperCamelCase_		)
		self.assertEqual(len(loading_info['missing_keys']		)      ,       0		)

		model.to(UpperCamelCase_		)
		snake_case__	       =   model(**self.dummy_input		)

		assert image is not None, "Make sure output is not None"





	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'		)
		snake_case__	       =   model.to(UpperCamelCase_		)
		model.eval()

		if torch_device == "mps":
			snake_case__	       =   torch.manual_seed(0		)
		else:
			snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)

		snake_case__	       =   torch.randn(
		    1      ,       model.config.in_channels      ,       model.config.sample_size      ,       model.config.sample_size      ,       generator=torch.manual_seed(0		)      ,       )
		snake_case__	       =   image.to(UpperCamelCase_		)
		with torch.no_grad():
			snake_case__	       =   model(UpperCamelCase_      ,       sample_posterior=UpperCamelCase_      ,       generator=UpperCamelCase_		).sample

		snake_case__	       =   output[0, -1, -3:, -3:].flatten().cpu()

		# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
		# the expected output slices are not the same for CPU and GPU.
		if torch_device == "mps":
			snake_case__	       =   torch.tensor(
			    [
			        -4.0_0_7_8E-0_1,
			        -3.8_3_2_3E-0_4,
			        -1.2_6_8_1E-0_1,
			        -1.1_4_6_2E-0_1,
			        2.0_0_9_5E-0_1,
			        1.0_8_9_3E-0_1,
			        -8.8_2_4_7E-0_2,
			        -3.0_3_6_1E-0_1,
			        -9.8_6_4_4E-0_3,
			    ]		)
		elif torch_device == "cpu":
			snake_case__	       =   torch.tensor(
			    [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]		)
		else:
			snake_case__	       =   torch.tensor(
			    [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]		)

		self.assertTrue(torch_all_close(UpperCamelCase_      ,       UpperCamelCase_      ,       rtol=1E-2		)		)






@slow
class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Union[str, Any]:
		return F'''gaussian_noise_s={seed}_shape={'_'.join([str(UpperCamelCase_		) for s in shape]		)}.npy'''





	def   _snake_case   (		self		)      ->		List[Any]:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self      ,       UpperCamelCase_=0      ,       UpperCamelCase_=(4, 3, 512, 512)      ,       UpperCamelCase_=False		)      ->		Optional[Any]:
		snake_case__	       =   torch.floataa if fpaa else torch.floataa
		snake_case__	       =   torch.from_numpy(load_hf_numpy(self.get_file_format(UpperCamelCase_      ,       UpperCamelCase_		)		)		).to(UpperCamelCase_		).to(UpperCamelCase_		)
		return image





	def   _snake_case   (		self      ,       UpperCamelCase_="CompVis/stable-diffusion-v1-4"      ,       UpperCamelCase_=False		)      ->		Dict:
		snake_case__	       =   'fp16' if fpaa else None
		snake_case__	       =   torch.floataa if fpaa else torch.floataa

		snake_case__	       =   AutoencoderKL.from_pretrained(
		    UpperCamelCase_      ,       subfolder='vae'      ,       torch_dtype=UpperCamelCase_      ,       revision=UpperCamelCase_      ,       )
		model.to(UpperCamelCase_		).eval()

		return model





	def   _snake_case   (		self      ,       UpperCamelCase_=0		)      ->		Tuple:
		if torch_device == "mps":
			return torch.manual_seed(UpperCamelCase_		)
		return torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
	        [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
	        # fmt: on
	    ]		)
	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		List[str]:
		snake_case__	       =   self.get_sd_vae_model()
		snake_case__	       =   self.get_sd_image(UpperCamelCase_		)
		snake_case__	       =   self.get_generator(UpperCamelCase_		)

		with torch.no_grad():
			snake_case__	       =   model(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       sample_posterior=UpperCamelCase_		).sample

		assert sample.shape == image.shape

		snake_case__	       =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
		snake_case__	       =   torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice		)

		assert torch_all_close(UpperCamelCase_      ,       UpperCamelCase_      ,       atol=3E-3		)





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
	        [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
	        # fmt: on
	    ]		)
	@require_torch_gpu
	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		List[str]:
		snake_case__	       =   self.get_sd_vae_model(fpaa=UpperCamelCase_		)
		snake_case__	       =   self.get_sd_image(UpperCamelCase_      ,       fpaa=UpperCamelCase_		)
		snake_case__	       =   self.get_generator(UpperCamelCase_		)

		with torch.no_grad():
			snake_case__	       =   model(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       sample_posterior=UpperCamelCase_		).sample

		assert sample.shape == image.shape

		snake_case__	       =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
		snake_case__	       =   torch.tensor(UpperCamelCase_		)

		assert torch_all_close(UpperCamelCase_      ,       UpperCamelCase_      ,       atol=1E-2		)





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
	        [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
	        # fmt: on
	    ]		)
	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Tuple:
		snake_case__	       =   self.get_sd_vae_model()
		snake_case__	       =   self.get_sd_image(UpperCamelCase_		)

		with torch.no_grad():
			snake_case__	       =   model(UpperCamelCase_		).sample

		assert sample.shape == image.shape

		snake_case__	       =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
		snake_case__	       =   torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice		)

		assert torch_all_close(UpperCamelCase_      ,       UpperCamelCase_      ,       atol=3E-3		)





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
	        [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
	        # fmt: on
	    ]		)
	@require_torch_gpu
	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		List[Any]:
		snake_case__	       =   self.get_sd_vae_model()
		snake_case__	       =   self.get_sd_image(UpperCamelCase_      ,       shape=(3, 4, 64, 64)		)

		with torch.no_grad():
			snake_case__	       =   model.decode(UpperCamelCase_		).sample

		assert list(sample.shape		) == [3, 3, 512, 512]

		snake_case__	       =   sample[-1, -2:, :2, -2:].flatten().cpu()
		snake_case__	       =   torch.tensor(UpperCamelCase_		)

		assert torch_all_close(UpperCamelCase_      ,       UpperCamelCase_      ,       atol=1E-3		)





	@parameterized.expand(
	    [
	        # fmt: off
	        [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
	        [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
	        # fmt: on
	    ]		)
	@require_torch_gpu
	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		List[str]:
		snake_case__	       =   self.get_sd_vae_model(fpaa=UpperCamelCase_		)
		snake_case__	       =   self.get_sd_image(UpperCamelCase_      ,       shape=(3, 4, 64, 64)      ,       fpaa=UpperCamelCase_		)

		with torch.no_grad():
			snake_case__	       =   model.decode(UpperCamelCase_		).sample

		assert list(sample.shape		) == [3, 3, 512, 512]

		snake_case__	       =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
		snake_case__	       =   torch.tensor(UpperCamelCase_		)

		assert torch_all_close(UpperCamelCase_      ,       UpperCamelCase_      ,       atol=5E-3		)





	@parameterized.expand([(13,), (16,), (27,)]		)
	@require_torch_gpu
	@unittest.skipIf(not is_xformers_available()      ,       reason='xformers is not required when using PyTorch 2.0.'		)
	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Dict:
		snake_case__	       =   self.get_sd_vae_model(fpaa=UpperCamelCase_		)
		snake_case__	       =   self.get_sd_image(UpperCamelCase_      ,       shape=(3, 4, 64, 64)      ,       fpaa=UpperCamelCase_		)

		with torch.no_grad():
			snake_case__	       =   model.decode(UpperCamelCase_		).sample

		model.enable_xformers_memory_efficient_attention()
		with torch.no_grad():
			snake_case__	       =   model.decode(UpperCamelCase_		).sample

		assert list(sample.shape		) == [3, 3, 512, 512]

		assert torch_all_close(UpperCamelCase_      ,       UpperCamelCase_      ,       atol=1E-1		)





	@parameterized.expand([(13,), (16,), (37,)]		)
	@require_torch_gpu
	@unittest.skipIf(not is_xformers_available()      ,       reason='xformers is not required when using PyTorch 2.0.'		)
	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Optional[int]:
		snake_case__	       =   self.get_sd_vae_model()
		snake_case__	       =   self.get_sd_image(UpperCamelCase_      ,       shape=(3, 4, 64, 64)		)

		with torch.no_grad():
			snake_case__	       =   model.decode(UpperCamelCase_		).sample

		model.enable_xformers_memory_efficient_attention()
		with torch.no_grad():
			snake_case__	       =   model.decode(UpperCamelCase_		).sample

		assert list(sample.shape		) == [3, 3, 512, 512]

		assert torch_all_close(UpperCamelCase_      ,       UpperCamelCase_      ,       atol=1E-2		)





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
	        [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
	        # fmt: on
	    ]		)
	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Tuple:
		snake_case__	       =   self.get_sd_vae_model()
		snake_case__	       =   self.get_sd_image(UpperCamelCase_		)
		snake_case__	       =   self.get_generator(UpperCamelCase_		)

		with torch.no_grad():
			snake_case__	       =   model.encode(UpperCamelCase_		).latent_dist
			snake_case__	       =   dist.sample(generator=UpperCamelCase_		)

		assert list(sample.shape		) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

		snake_case__	       =   sample[0, -1, -3:, -3:].flatten().cpu()
		snake_case__	       =   torch.tensor(UpperCamelCase_		)

		snake_case__	       =   3E-3 if torch_device != 'mps' else 1E-2
		assert torch_all_close(UpperCamelCase_      ,       UpperCamelCase_      ,       atol=UpperCamelCase_		)










'''simple docstring'''




import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)






class 				__snake_case     (					unittest.TestCase							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=7      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=99      ,       UpperCamelCase_=32      ,       UpperCamelCase_=5      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=512      ,       UpperCamelCase_=16      ,       UpperCamelCase_=2      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=4      ,       )      ->		List[Any]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   seq_length
		snake_case__	       =   is_training
		snake_case__	       =   use_attention_mask
		snake_case__	       =   use_token_type_ids
		snake_case__	       =   use_labels
		snake_case__	       =   vocab_size
		snake_case__	       =   hidden_size
		snake_case__	       =   num_hidden_layers
		snake_case__	       =   num_attention_heads
		snake_case__	       =   intermediate_size
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   type_vocab_size
		snake_case__	       =   type_sequence_label_size
		snake_case__	       =   initializer_range
		snake_case__	       =   num_choices





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size		)

		snake_case__	       =   None
		if self.use_attention_mask:
			snake_case__	       =   random_attention_mask([self.batch_size, self.seq_length]		)

		snake_case__	       =   None
		if self.use_token_type_ids:
			snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size		)

		snake_case__	       =   RobertaConfig(
		    vocab_size=self.vocab_size      ,       hidden_size=self.hidden_size      ,       num_hidden_layers=self.num_hidden_layers      ,       num_attention_heads=self.num_attention_heads      ,       intermediate_size=self.intermediate_size      ,       hidden_act=self.hidden_act      ,       hidden_dropout_prob=self.hidden_dropout_prob      ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,       max_position_embeddings=self.max_position_embeddings      ,       type_vocab_size=self.type_vocab_size      ,       is_decoder=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       )

		return config, input_ids, token_type_ids, attention_mask





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs
		snake_case__	       =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
		return config, inputs_dict





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs

		snake_case__	       =   True
		snake_case__	       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       vocab_size=2		)

		return (
		    config,
		    input_ids,
		    token_type_ids,
		    encoder_hidden_states,
		    encoder_attention_mask,
		)






@require_flax
class 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  True

	__lowerCAmelCase       						=  (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   FlaxRobertaModelTester(self		)





	@slow
	def   _snake_case   (		self		)      ->		Tuple:
		for model_class_name in self.all_model_classes:
			snake_case__	       =   model_class_name.from_pretrained('roberta-base'      ,       from_pt=UpperCamelCase_		)
			snake_case__	       =   model(np.ones((1, 1)		)		)
			self.assertIsNotNone(UpperCamelCase_		)



