
"""simple docstring"""





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class A_	(lowercase__				,lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						str       =		StableDiffusionDiffEditPipeline
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		frozenset(
      []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  SCREAMING_SNAKE_CASE__			:						str       =		frozenset([]      )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     torch.manual_seed(0  )
     UpperCAmelCase_					:					str			     =    UNetaDConditionModel(
         block_out_channels=(32, 64)				,   layers_per_block=2				,   sample_size=32				,   in_channels=4				,   out_channels=4				,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				,   cross_attention_dim=32				,   attention_head_dim=(2, 4)				,   use_linear_projection=lowercase_				,   )
     UpperCAmelCase_					:					str			     =    DDIMScheduler(
         beta_start=0.0_00_85				,   beta_end=0.0_12				,   beta_schedule="scaled_linear"				,   clip_sample=lowercase_				,   set_alpha_to_one=lowercase_				,   )
     UpperCAmelCase_					:					str			     =    DDIMInverseScheduler(
         beta_start=0.0_00_85				,   beta_end=0.0_12				,   beta_schedule="scaled_linear"				,   clip_sample=lowercase_				,   set_alpha_to_zero=lowercase_				,   )
     torch.manual_seed(0  )
     UpperCAmelCase_					:					str			     =    AutoencoderKL(
         block_out_channels=[32, 64]				,   in_channels=3				,   out_channels=3				,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,   latent_channels=4				,   sample_size=128				,   )
     torch.manual_seed(0  )
     UpperCAmelCase_					:					int			     =    CLIPTextConfig(
         bos_token_id=0				,   eos_token_id=2				,   hidden_size=32				,   intermediate_size=37				,   layer_norm_eps=1E-0_5				,   num_attention_heads=4				,   num_hidden_layers=5				,   pad_token_id=1				,   vocab_size=1000				,   hidden_act="gelu"				,   projection_dim=512				,   )
     UpperCAmelCase_					:					Optional[int]			     =    CLIPTextModel(lowercase_  )
     UpperCAmelCase_					:					Union[str, Any]			     =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

     UpperCAmelCase_					:					List[Any]			     =    {
         "unet": unet,
         "scheduler": scheduler,
         "inverse_scheduler": inverse_scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }

     return components







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_=0  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    floats_tensor((1, 16, 16)				,   rng=random.Random(lowercase_  )  ).to(lowercase_  )
     UpperCAmelCase_					:					int			     =    floats_tensor((1, 2, 4, 16, 16)				,   rng=random.Random(lowercase_  )  ).to(lowercase_  )
     if str(lowercase_  ).startswith("mps"  ):
        UpperCAmelCase_					:					List[Any]			     =    torch.manual_seed(lowercase_  )
     else:
        UpperCAmelCase_					:					Optional[int]			     =    torch.Generator(device=lowercase_  ).manual_seed(lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    {
         "prompt": "a dog and a newt",
         "mask_image": mask,
         "image_latents": latents,
         "generator": generator,
         "num_inference_steps": 2,
         "inpaint_strength": 1.0,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }

     return inputs







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_=0  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    floats_tensor((1, 3, 32, 32)				,   rng=random.Random(lowercase_  )  ).to(lowercase_  )
     UpperCAmelCase_					:					List[str]			     =    image.cpu().permute(0				,   2				,   3				,   1  )[0]
     UpperCAmelCase_					:					Optional[int]			     =    Image.fromarray(np.uinta(lowercase_  )  ).convert("RGB"  )
     if str(lowercase_  ).startswith("mps"  ):
        UpperCAmelCase_					:					int			     =    torch.manual_seed(lowercase_  )
     else:
        UpperCAmelCase_					:					Optional[Any]			     =    torch.Generator(device=lowercase_  ).manual_seed(lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    {
         "image": image,
         "source_prompt": "a cat and a frog",
         "target_prompt": "a dog and a newt",
         "generator": generator,
         "num_inference_steps": 2,
         "num_maps_per_mask": 2,
         "mask_encode_strength": 1.0,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }

     return inputs







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_=0  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    floats_tensor((1, 3, 32, 32)				,   rng=random.Random(lowercase_  )  ).to(lowercase_  )
     UpperCAmelCase_					:					int			     =    image.cpu().permute(0				,   2				,   3				,   1  )[0]
     UpperCAmelCase_					:					Dict			     =    Image.fromarray(np.uinta(lowercase_  )  ).convert("RGB"  )
     if str(lowercase_  ).startswith("mps"  ):
        UpperCAmelCase_					:					Union[str, Any]			     =    torch.manual_seed(lowercase_  )
     else:
        UpperCAmelCase_					:					str			     =    torch.Generator(device=lowercase_  ).manual_seed(lowercase_  )
     UpperCAmelCase_					:					Union[str, Any]			     =    {
         "image": image,
         "prompt": "a cat and a frog",
         "generator": generator,
         "num_inference_steps": 2,
         "inpaint_strength": 1.0,
         "guidance_scale": 6.0,
         "decode_latents": True,
         "output_type": "numpy",
     }
     return inputs







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if not hasattr(self.pipeline_class				,   "_optional_components"  ):
        return

     UpperCAmelCase_					:					str			     =    self.get_dummy_components()
     UpperCAmelCase_					:					List[str]			     =    self.pipeline_class(**lowercase_  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

     # set all optional components to None and update pipeline config accordingly
     for optional_component in pipe._optional_components:
        setattr(lowercase_				,   lowercase_				,   lowercase_  )
     pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}  )

     UpperCAmelCase_					:					List[Any]			     =    self.get_dummy_inputs(lowercase_  )
     UpperCAmelCase_					:					Dict			     =    pipe(**lowercase_  )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(lowercase_  )
        UpperCAmelCase_					:					Optional[int]			     =    self.pipeline_class.from_pretrained(lowercase_  )
        pipe_loaded.to(lowercase_  )
        pipe_loaded.set_progress_bar_config(disable=lowercase_  )

     for optional_component in pipe._optional_components:
        self.assertTrue(
            getattr(lowercase_				,   lowercase_  ) is None				,   F"""`{optional_component}` did not stay set to None after loading."""				,   )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_dummy_inputs(lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    pipe_loaded(**lowercase_  )[0]

     UpperCAmelCase_					:					Any			     =    np.abs(output - output_loaded  ).max()
     self.assertLess(lowercase_				,   1E-4  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    "cpu"

     UpperCAmelCase_					:					List[Any]			     =    self.get_dummy_components()
     UpperCAmelCase_					:					Any			     =    self.pipeline_class(**lowercase_  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					Dict			     =    self.get_dummy_mask_inputs(lowercase_  )
     UpperCAmelCase_					:					str			     =    pipe.generate_mask(**lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    mask[0, -3:, -3:]

     self.assertEqual(mask.shape				,   (1, 16, 16)  )
     UpperCAmelCase_					:					Union[str, Any]			     =    np.array([0] * 9  )
     UpperCAmelCase_					:					Dict			     =    np.abs(mask_slice.flatten() - expected_slice  ).max()
     self.assertLessEqual(lowercase_				,   1E-3  )
     self.assertEqual(mask[0, -3, -4]				,   0  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    "cpu"

     UpperCAmelCase_					:					str			     =    self.get_dummy_components()
     UpperCAmelCase_					:					Optional[int]			     =    self.pipeline_class(**lowercase_  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					Dict			     =    self.get_dummy_inversion_inputs(lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    pipe.invert(**lowercase_  ).images
     UpperCAmelCase_					:					Union[str, Any]			     =    image[0, -1, -3:, -3:]

     self.assertEqual(image.shape				,   (2, 32, 32, 3)  )
     UpperCAmelCase_					:					Optional[int]			     =    np.array(
         [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]				,   )
     UpperCAmelCase_					:					Optional[Any]			     =    np.abs(image_slice.flatten() - expected_slice  ).max()
     self.assertLessEqual(lowercase_				,   1E-3  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().test_inference_batch_single_identical(expected_max_diff=5E-3  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    "cpu"

     UpperCAmelCase_					:					Optional[int]			     =    self.get_dummy_components()

     UpperCAmelCase_					:					Optional[Any]			     =    {"beta_start": 0.0_00_85, "beta_end": 0.0_12, "beta_schedule": "scaled_linear"}
     UpperCAmelCase_					:					Any			     =    DPMSolverMultistepScheduler(**lowercase_  )
     UpperCAmelCase_					:					Dict			     =    DPMSolverMultistepInverseScheduler(**lowercase_  )

     UpperCAmelCase_					:					Optional[int]			     =    self.pipeline_class(**lowercase_  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					str			     =    self.get_dummy_inversion_inputs(lowercase_  )
     UpperCAmelCase_					:					Any			     =    pipe.invert(**lowercase_  ).images
     UpperCAmelCase_					:					str			     =    image[0, -1, -3:, -3:]

     self.assertEqual(image.shape				,   (2, 32, 32, 3)  )
     UpperCAmelCase_					:					Optional[Any]			     =    np.array(
         [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]				,   )
     UpperCAmelCase_					:					Optional[Any]			     =    np.abs(image_slice.flatten() - expected_slice  ).max()
     self.assertLessEqual(lowercase_				,   1E-3  )




@require_torch_gpu
@slow
class A_	(unittest.TestCase      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







  @classmethod
  def        UpperCamelCase__					(		cls  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"  )

     UpperCAmelCase_					:					int			     =    raw_image.convert("RGB"  ).resize((768, 768)  )

     UpperCAmelCase_					:					List[Any]			     =    raw_image







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    torch.manual_seed(0  )

     UpperCAmelCase_					:					int			     =    StableDiffusionDiffEditPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1"				,   safety_checker=lowercase_				,   torch_dtype=torch.floataa  )
     UpperCAmelCase_					:					List[str]			     =    DDIMScheduler.from_config(pipe.scheduler.config  )
     UpperCAmelCase_					:					Union[str, Any]			     =    DDIMInverseScheduler.from_config(pipe.scheduler.config  )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					Optional[Any]			     =    "a bowl of fruit"
     UpperCAmelCase_					:					Any			     =    "a bowl of pears"

     UpperCAmelCase_					:					List[str]			     =    pipe.generate_mask(
         image=self.raw_image				,   source_prompt=lowercase_				,   target_prompt=lowercase_				,   generator=lowercase_				,   )

     UpperCAmelCase_					:					List[Any]			     =    pipe.invert(
         prompt=lowercase_				,   image=self.raw_image				,   inpaint_strength=0.7				,   generator=lowercase_  ).latents

     UpperCAmelCase_					:					Tuple			     =    pipe(
         prompt=lowercase_				,   mask_image=lowercase_				,   image_latents=lowercase_				,   generator=lowercase_				,   negative_prompt=lowercase_				,   inpaint_strength=0.7				,   output_type="numpy"				,   ).images[0]

     UpperCAmelCase_					:					List[Any]			     =    (
         np.array(
             load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/diffedit/pears.png"  ).resize((768, 768)  )  )
         / 255
     )
     assert np.abs((expected_image - image).max()  ) < 5E-1







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    torch.manual_seed(0  )

     UpperCAmelCase_					:					Optional[Any]			     =    StableDiffusionDiffEditPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1"				,   safety_checker=lowercase_				,   torch_dtype=torch.floataa  )
     UpperCAmelCase_					:					Any			     =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
     UpperCAmelCase_					:					List[Any]			     =    DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					List[str]			     =    "a bowl of fruit"
     UpperCAmelCase_					:					Optional[Any]			     =    "a bowl of pears"

     UpperCAmelCase_					:					List[Any]			     =    pipe.generate_mask(
         image=self.raw_image				,   source_prompt=lowercase_				,   target_prompt=lowercase_				,   generator=lowercase_				,   )

     UpperCAmelCase_					:					Tuple			     =    pipe.invert(
         prompt=lowercase_				,   image=self.raw_image				,   inpaint_strength=0.7				,   generator=lowercase_				,   num_inference_steps=25				,   ).latents

     UpperCAmelCase_					:					List[str]			     =    pipe(
         prompt=lowercase_				,   mask_image=lowercase_				,   image_latents=lowercase_				,   generator=lowercase_				,   negative_prompt=lowercase_				,   inpaint_strength=0.7				,   num_inference_steps=25				,   output_type="numpy"				,   ).images[0]

     UpperCAmelCase_					:					List[Any]			     =    (
         np.array(
             load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/diffedit/pears.png"  ).resize((768, 768)  )  )
         / 255
     )
     assert np.abs((expected_image - image).max()  ) < 5E-1




import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
				import jax.numpy as jnp

				from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
				    FlaxRobertaPreLayerNormForCausalLM,
				    FlaxRobertaPreLayerNormForMaskedLM,
				    FlaxRobertaPreLayerNormForMultipleChoice,
				    FlaxRobertaPreLayerNormForQuestionAnswering,
				    FlaxRobertaPreLayerNormForSequenceClassification,
				    FlaxRobertaPreLayerNormForTokenClassification,
				    FlaxRobertaPreLayerNormModel,
				)





class       _snake_case					(     unittest.TestCase				):







			def __init__(				self						,   _a						,   _a=13						,   _a=7						,   _a=True						,   _a=True						,   _a=True						,   _a=True						,   _a=99						,   _a=32						,   _a=5						,   _a=4						,   _a=37						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=16						,   _a=2						,   _a=0.02						,   _a=4						,   ):
				__magic_name__		:		List[Any]	   =     parent
				__magic_name__		:		Optional[Any]	   =     batch_size
				__magic_name__		:		Dict	   =     seq_length
				__magic_name__		:		Union[str, Any]	   =     is_training
				__magic_name__		:		Optional[Any]	   =     use_attention_mask
				__magic_name__		:		Optional[Any]	   =     use_token_type_ids
				__magic_name__		:		int	   =     use_labels
				__magic_name__		:		List[Any]	   =     vocab_size
				__magic_name__		:		Union[str, Any]	   =     hidden_size
				__magic_name__		:		Optional[Any]	   =     num_hidden_layers
				__magic_name__		:		int	   =     num_attention_heads
				__magic_name__		:		Any	   =     intermediate_size
				__magic_name__		:		List[Any]	   =     hidden_act
				__magic_name__		:		List[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		List[Any]	   =     max_position_embeddings
				__magic_name__		:		Tuple	   =     type_vocab_size
				__magic_name__		:		List[str]	   =     type_sequence_label_size
				__magic_name__		:		Dict	   =     initializer_range
				__magic_name__		:		List[Any]	   =     num_choices







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size		)

				__magic_name__		:		List[Any]	   =     None
				if self.use_attention_mask:
					__magic_name__		:		List[str]	   =     random_attention_mask([self.batch_size, self.seq_length]		)

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

				__magic_name__		:		List[str]	   =     RobertaPreLayerNormConfig(
				    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=_a						,   initializer_range=self.initializer_range						,   )

				return config, input_ids, token_type_ids, attention_mask







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.prepare_config_and_inputs()
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		List[Any]	   =     config_and_inputs
				__magic_name__		:		List[str]	   =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     self.prepare_config_and_inputs()
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     config_and_inputs

				__magic_name__		:		Tuple	   =     True
				__magic_name__		:		int	   =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				__magic_name__		:		Optional[Any]	   =     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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    True

			UpperCamelCase__          =    (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     FlaxRobertaPreLayerNormModelTester(self		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				for model_class_name in self.all_model_classes:
					__magic_name__		:		Optional[Any]	   =     model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)
					__magic_name__		:		Dict	   =     model(np.ones((1, 1)		)		)
					self.assertIsNotNone(_a		)







@require_flax
class       _snake_case					(     unittest.TestCase				):







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)

				__magic_name__		:		Union[str, Any]	   =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,   dtype=jnp.intaa		)
				__magic_name__		:		List[str]	   =     model(_a		)[0]
				__magic_name__		:		str	   =     [1, 11, 50_265]
				self.assertEqual(list(output.shape		)						,   _a		)
				# compare the actual values for a slice.
				__magic_name__		:		List[str]	   =     np.array(
				    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]						,   dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3]						,   _a						,   atol=1e-4		)		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)

				__magic_name__		:		Tuple	   =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,   dtype=jnp.intaa		)
				__magic_name__		:		Tuple	   =     model(_a		)[0]
				# compare the actual values for a slice.
				__magic_name__		:		Dict	   =     np.array(
				    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]						,   dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3]						,   _a						,   atol=1e-4		)		)
