



'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 		lowerCamelCase__      (						lowerCamelCase__	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						KandinskyVaaInpaintPipeline
					A_						    =						["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
					A_						    =						[
					    """image_embeds""",
					    """negative_image_embeds""",
					    """image""",
					    """mask_image""",
					]
					A_						    =						[
					    """generator""",
					    """height""",
					    """width""",
					    """latents""",
					    """guidance_scale""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					A_						    =						False




					@property
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Any:



						'''simple docstring'''





						return 32




					@property
					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       List[str]:



						'''simple docstring'''





						return 32




					@property
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       int:



						'''simple docstring'''





						return self.time_input_dim




					@property
					def 						__UpperCAmelCase     (  self				:	Any					)  ->       Any:



						'''simple docstring'''





						return self.time_input_dim * 4




					@property
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       str:



						'''simple docstring'''





						return 100




					@property
					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[int]:



						'''simple docstring'''





						torch.manual_seed(0					)

						_lowercase					:       int       =		{
						    '''in_channels''': 9,
						    # Out channels is double in channels because predicts mean and variance
						    '''out_channels''': 8,
						    '''addition_embed_type''': '''image''',
						    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
						    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
						    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
						    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
						    '''layers_per_block''': 1,
						    '''encoder_hid_dim''': self.text_embedder_hidden_size,
						    '''encoder_hid_dim_type''': '''image_proj''',
						    '''cross_attention_dim''': self.cross_attention_dim,
						    '''attention_head_dim''': 4,
						    '''resnet_time_scale_shift''': '''scale_shift''',
						    '''class_embed_type''': None,
						}

						_lowercase					:       List[Any]       =		UNetaDConditionModel(**__lowerCamelCase					)
						return model




					@property
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Dict:



						'''simple docstring'''





						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}




					@property
					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       int:



						'''simple docstring'''





						torch.manual_seed(0					)
						_lowercase					:       Any       =		VQModel(**self.dummy_movq_kwargs					)
						return model




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.dummy_unet
						_lowercase					:       List[str]       =		self.dummy_movq

						_lowercase					:       List[str]       =		DDIMScheduler(
						    num_train_timesteps=1000     ,  beta_schedule='linear'     ,  beta_start=0.0_00_85     ,  beta_end=0.0_12     ,  clip_sample=__lowerCamelCase     ,  set_alpha_to_one=__lowerCamelCase     ,  steps_offset=1     ,  prediction_type='epsilon'     ,  thresholding=__lowerCamelCase     ,  )

						_lowercase					:       Optional[Any]       =		{
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''movq''': movq,
						}

						return components




					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	List[str]=0					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		floats_tensor((1, self.text_embedder_hidden_size)     ,  rng=random.Random(__lowerCamelCase					)					).to(__lowerCamelCase					)
						_lowercase					:       int       =		floats_tensor((1, self.text_embedder_hidden_size)     ,  rng=random.Random(seed + 1					)					).to(
						    __lowerCamelCase					)
						# create init_image
						_lowercase					:       Dict       =		floats_tensor((1, 3, 64, 64)     ,  rng=random.Random(__lowerCamelCase					)					).to(__lowerCamelCase					)
						_lowercase					:       str       =		image.cpu().permute(0     ,  2     ,  3     ,  1					)[0]
						_lowercase					:       Optional[int]       =		Image.fromarray(np.uinta(__lowerCamelCase					)					).convert('RGB'					).resize((256, 256)					)
						# create mask
						_lowercase					:       int       =		np.ones((64, 64)     ,  dtype=np.floataa					)
						_lowercase					:       List[Any]       =		0

						if str(__lowerCamelCase					).startswith('mps'					):
							_lowercase					:       Any       =		torch.manual_seed(__lowerCamelCase					)
						else:
							_lowercase					:       Optional[int]       =		torch.Generator(device=__lowerCamelCase					).manual_seed(__lowerCamelCase					)
						_lowercase					:       Optional[int]       =		{
						    '''image''': init_image,
						    '''mask_image''': mask,
						    '''image_embeds''': image_embeds,
						    '''negative_image_embeds''': negative_image_embeds,
						    '''generator''': generator,
						    '''height''': 64,
						    '''width''': 64,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 4.0,
						    '''output_type''': '''np''',
						}
						return inputs




					def 						__UpperCAmelCase     (  self				:	int					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Tuple       =		'''cpu'''

						_lowercase					:       Union[str, Any]       =		self.get_dummy_components()

						_lowercase					:       Optional[Any]       =		self.pipeline_class(**__lowerCamelCase					)
						_lowercase					:       Tuple       =		pipe.to(__lowerCamelCase					)

						pipe.set_progress_bar_config(disable=__lowerCamelCase					)

						_lowercase					:       Optional[int]       =		pipe(**self.get_dummy_inputs(__lowerCamelCase					)					)
						_lowercase					:       Optional[Any]       =		output.images

						_lowercase					:       Dict       =		pipe(
						    **self.get_dummy_inputs(__lowerCamelCase					)     ,  return_dict=__lowerCamelCase     ,  )[0]

						_lowercase					:       List[str]       =		image[0, -3:, -3:, -1]
						_lowercase					:       List[str]       =		image_from_tuple[0, -3:, -3:, -1]

						print(F'''image.shape {image.shape}'''					)

						assert image.shape == (1, 64, 64, 3)

						_lowercase					:       Dict       =		np.array(
						    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]					)

						assert (
						    np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''




					def 						__UpperCAmelCase     (  self				:	int					)  ->       Optional[int]:



						'''simple docstring'''





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






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




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Dict:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	int					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'					)

						_lowercase					:       List[str]       =		load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
						_lowercase					:       Optional[int]       =		np.ones((768, 768)     ,  dtype=np.floataa					)
						_lowercase					:       List[Any]       =		0

						_lowercase					:       Any       =		'''a hat'''

						_lowercase					:       Dict       =		KandinskyVaaPriorPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'     ,  torch_dtype=torch.floataa					)
						pipe_prior.to(__lowerCamelCase					)

						_lowercase					:       str       =		KandinskyVaaInpaintPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-decoder-inpaint'     ,  torch_dtype=torch.floataa					)
						_lowercase					:       Union[str, Any]       =		pipeline.to(__lowerCamelCase					)
						pipeline.set_progress_bar_config(disable=__lowerCamelCase					)

						_lowercase					:       Optional[Any]       =		torch.Generator(device='cpu'					).manual_seed(0					)
						_lowercase					:       Optional[Any]       =		pipe_prior(
						    __lowerCamelCase     ,  generator=__lowerCamelCase     ,  num_inference_steps=5     ,  negative_prompt=''     ,  ).to_tuple()

						_lowercase					:       Union[str, Any]       =		pipeline(
						    image=__lowerCamelCase     ,  mask_image=__lowerCamelCase     ,  image_embeds=__lowerCamelCase     ,  negative_image_embeds=__lowerCamelCase     ,  generator=__lowerCamelCase     ,  num_inference_steps=100     ,  height=768     ,  width=768     ,  output_type='np'     ,  )

						_lowercase					:       Optional[int]       =		output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(__lowerCamelCase     ,  __lowerCamelCase					)





'''simple docstring'''

import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)



class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def __init__(  self				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	List[str]=13     ,  UpperCamelCase_				:	Union[str, Any]=7     ,  UpperCamelCase_				:	Union[str, Any]=True     ,  UpperCamelCase_				:	Any=True     ,  UpperCamelCase_				:	Dict=True     ,  UpperCamelCase_				:	List[str]=True     ,  UpperCamelCase_				:	int=99     ,  UpperCamelCase_				:	Tuple=32     ,  UpperCamelCase_				:	List[str]=5     ,  UpperCamelCase_				:	Dict=4     ,  UpperCamelCase_				:	Tuple=37     ,  UpperCamelCase_				:	int="gelu"     ,  UpperCamelCase_				:	int=0.1     ,  UpperCamelCase_				:	Dict=0.1     ,  UpperCamelCase_				:	Any=512     ,  UpperCamelCase_				:	List[Any]=16     ,  UpperCamelCase_				:	Optional[int]=2     ,  UpperCamelCase_				:	Tuple=0.02     ,  UpperCamelCase_				:	Union[str, Any]=4     ,  )  ->       Tuple:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

						_lowercase					:       int       =		None
						if self.use_attention_mask:
							_lowercase					:       Optional[int]       =		random_attention_mask([self.batch_size, self.seq_length]					)

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

						_lowercase					:       str       =		RoFormerConfig(
						    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 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Dict       =		self.prepare_config_and_inputs()
						_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		config_and_inputs
						_lowercase					:       Optional[int]       =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict






@require_flax
class 		lowerCamelCase__      (						A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						True

					A_						    =						(
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Tuple       =		FlaxRoFormerModelTester(self					)




					@slow
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						for model_class_name in self.all_model_classes:
							_lowercase					:       Optional[int]       =		model_class_name.from_pretrained('junnyu/roformer_chinese_small'     ,  from_pt=UpperCamelCase_					)
							_lowercase					:       str       =		model(np.ones((1, 1)					)					)
							self.assertIsNotNone(UpperCamelCase_					)






@require_flax
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@slow
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'					)
						_lowercase					:       Any       =		jnp.array([[0, 1, 2, 3, 4, 5]]					)
						_lowercase					:       int       =		model(UpperCamelCase_					)[0]

						_lowercase					:       Union[str, Any]       =		5_0000

						_lowercase					:       str       =		(1, 6, vocab_size)
						self.assertEqual(output.shape     ,  UpperCamelCase_					)

						_lowercase					:       int       =		jnp.array(
						    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]					)

						self.assertTrue(jnp.allclose(output[:, :3, :3]     ,  UpperCamelCase_     ,  atol=1E-4					)					)
