


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						StableDiffusionInstructPixaPixPipeline
							_UpperCAmelCase      =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
							_UpperCAmelCase      =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							_UpperCAmelCase      =						IMAGE_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase      =						IMAGE_TO_IMAGE_IMAGE_PARAMS






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)			,						layers_per_block=2			,						sample_size=3_2			,						in_channels=8			,						out_channels=4			,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,						cross_attention_dim=3_2			,						)
										SCREAMING_SNAKE_CASE_		:     Any					=					PNDMScheduler(skip_prk_steps=lowerCAmelCase__       )
										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     int					=					AutoencoderKL(
										    block_out_channels=[3_2, 6_4]			,						in_channels=3			,						out_channels=3			,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,						latent_channels=4			,						)
										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					CLIPTextConfig(
										    bos_token_id=0			,						eos_token_id=2			,						hidden_size=3_2			,						intermediate_size=3_7			,						layer_norm_eps=1E-05			,						num_attention_heads=4			,						num_hidden_layers=5			,						pad_token_id=1			,						vocab_size=1_0_0_0			,						)
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					CLIPTextModel(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					{
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'text_encoder': text_encoder,
										    'tokenizer': tokenizer,
										    'safety_checker': None,
										    'feature_extractor': None,
										}
										return components






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





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					floats_tensor((1, 3, 3_2, 3_2)			,						rng=random.Random(lowerCAmelCase__       )       ).to(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					image.cpu().permute(0			,						2			,						3			,						1       )[0]
										SCREAMING_SNAKE_CASE_		:     Tuple					=					Image.fromarray(np.uinta(lowerCAmelCase__       )       ).convert('RGB'       )
										if str(lowerCAmelCase__       ).startswith('mps'       ):
													SCREAMING_SNAKE_CASE_		:     int					=					torch.manual_seed(lowerCAmelCase__       )
										else:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.Generator(device=lowerCAmelCase__       ).manual_seed(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{
										    'prompt': 'A painting of a squirrel eating a burger',
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 2,
										    'guidance_scale': 6.0,
										    'image_guidance_scale': 1,
										    'output_type': 'numpy',
										}
										return inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					sd_pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     Dict					=					image[0, -3:, -3:, -1]
										assert image.shape == (1, 3_2, 3_2, 3)
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     int					=					StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'french fries'
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					sd_pipe(**lowerCAmelCase__			,						negative_prompt=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					output.images
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image[0, -3:, -3:, -1]

										assert image.shape == (1, 3_2, 3_2, 3)
										SCREAMING_SNAKE_CASE_		:     str					=					np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					[inputs['prompt']] * 2

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					np.array(inputs['image']       ).astype(np.floataa       ) / 255.0
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.from_numpy(lowerCAmelCase__       ).unsqueeze(0       ).to(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image / 2 + 0.5
										SCREAMING_SNAKE_CASE_		:     int					=					image.permute(0			,						3			,						1			,						2       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image.repeat(2			,						1			,						1			,						1       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					sd_pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     Dict					=					image[-1, -3:, -3:, -1]

										assert image.shape == (2, 3_2, 3_2, 3)
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     List[str]					=					EulerAncestralDiscreteScheduler(
										    beta_start=0.00_085			,						beta_end=0.012			,						beta_schedule='scaled_linear'       )
										SCREAMING_SNAKE_CASE_		:     str					=					StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					sd_pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     int					=					image[0, -3:, -3:, -1]

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[round(lowerCAmelCase__			,						4       ) for x in image_slice.flatten().tolist()]
										print(','.join([str(lowerCAmelCase__       ) for x in slice]       )       )

										assert image.shape == (1, 3_2, 3_2, 3)
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					VaeImageProcessor(do_resize=lowerCAmelCase__			,						do_normalize=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase__			,						input_image_type='pt'       )       )[0]

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					components['vae']
										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_dummy_inputs_by_type(lowerCAmelCase__			,						input_image_type='pt'       )

										for image_param in self.image_latents_params:
													if image_param in inputs.keys():
																SCREAMING_SNAKE_CASE_		:     str					=					vae.encode(inputs[image_param]       ).latent_dist.mode()

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					pipe(**lowerCAmelCase__       )[0]

										SCREAMING_SNAKE_CASE_		:     Tuple					=					np.abs(out - out_latents_inputs       ).max()
										self.assertLess(lowerCAmelCase__			,						1E-4			,						'passing latents as image input generate different result from passing image'       )







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





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__=0       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.manual_seed(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'       )
										SCREAMING_SNAKE_CASE_		:     str					=					{
										    'prompt': 'turn him into a cyborg',
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 3,
										    'guidance_scale': 7.5,
										    'image_guidance_scale': 1.0,
										    'output_type': 'numpy',
										}
										return inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'			,						safety_checker=lowerCAmelCase__       )
										pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     str					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     int					=					image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										SCREAMING_SNAKE_CASE_		:     List[str]					=					np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]       )

										assert np.abs(expected_slice - image_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'			,						safety_checker=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
										pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     Any					=					image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]       )

										assert np.abs(expected_slice - image_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'			,						safety_checker=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					DDIMScheduler.from_config(pipe.scheduler.config       )
										pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     Tuple					=					image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]       )

										assert np.abs(expected_slice - image_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					0

										def callback_fn(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ) -> None:
													SCREAMING_SNAKE_CASE_		:     Any					=					True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																SCREAMING_SNAKE_CASE_		:     str					=					latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 6_4, 6_4)
																SCREAMING_SNAKE_CASE_		:     Dict					=					latents[0, -3:, -3:, -1]
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]       )

																assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2
													elif step == 2:
																SCREAMING_SNAKE_CASE_		:     Tuple					=					latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 6_4, 6_4)
																SCREAMING_SNAKE_CASE_		:     Optional[int]					=					latents[0, -3:, -3:, -1]
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]       )

																assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2

										SCREAMING_SNAKE_CASE_		:     int					=					False

										SCREAMING_SNAKE_CASE_		:     Dict					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'			,						safety_checker=lowerCAmelCase__			,						torch_dtype=torch.floataa       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.get_inputs()
										pipe(**lowerCAmelCase__			,						callback=lowerCAmelCase__			,						callback_steps=1       )
										assert callback_fn.has_been_called
										assert number_of_steps == 3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'			,						safety_checker=lowerCAmelCase__			,						torch_dtype=torch.floataa       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing(1       )
										pipe.enable_sequential_cpu_offload()

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					pipe(**lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.cuda.max_memory_allocated()
										# make sure that less than 2.2 GB is allocated
										assert mem_bytes < 2.2 * 1_0**9






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.get_inputs()
										# resize to resolution that is divisible by 8 but not 16 or 32
										SCREAMING_SNAKE_CASE_		:     Tuple					=					inputs['image'].resize((5_0_4, 5_0_4)       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					'timbrooks/instruct-pix2pix'
										SCREAMING_SNAKE_CASE_		:     List[str]					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    lowerCAmelCase__			,						safety_checker=lowerCAmelCase__			,						)
										pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Dict					=					pipe(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					output.images[0]

										SCREAMING_SNAKE_CASE_		:     Any					=					image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

										assert image.shape == (5_0_4, 5_0_4, 3)
										SCREAMING_SNAKE_CASE_		:     Any					=					np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5E-3









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 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=7			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=9_9			,						lowerCAmelCase__=3_2			,						lowerCAmelCase__=5			,						lowerCAmelCase__=4			,						lowerCAmelCase__=3_7			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=5_1_2			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__=2			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=4			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					parent
										SCREAMING_SNAKE_CASE_		:     List[str]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					seq_length
										SCREAMING_SNAKE_CASE_		:     Any					=					is_training
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					use_attention_mask
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					use_token_type_ids
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					use_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Any					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     List[str]					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     int					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     str					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Tuple					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     Any					=					type_vocab_size
										SCREAMING_SNAKE_CASE_		:     str					=					type_sequence_label_size
										SCREAMING_SNAKE_CASE_		:     str					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					num_choices






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

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

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					None
										if self.use_token_type_ids:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size       )

										SCREAMING_SNAKE_CASE_		:     Any					=					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=lowerCAmelCase__			,						initializer_range=self.initializer_range			,						)

										return config, input_ids, token_type_ids, attention_mask






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					config_and_inputs
										SCREAMING_SNAKE_CASE_		:     List[str]					=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
										return config, inputs_dict






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					config_and_inputs

										SCREAMING_SNAKE_CASE_		:     str					=					True
										SCREAMING_SNAKE_CASE_		:     Dict					=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
										SCREAMING_SNAKE_CASE_		:     int					=					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 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						True

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					FlaxRobertaModelTester(self       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					model_class_name.from_pretrained('roberta-base'			,						from_pt=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     str					=					model(np.ones((1, 1)       )       )
													self.assertIsNotNone(lowerCAmelCase__       )





