



'''simple docstring'''




import unittest

import numpy as np
import timeout_decorator  # noqa

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

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						lowerCamelCase							:Any       = '''platform'''

						import jax
						import jax.numpy as jnp

						from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    shift_tokens_right,
						)



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				):
			'''simple docstring'''





			if attention_mask is None:
						A_     :						Tuple     =					np.where(input_ids != config.pad_token_id						,				1						,				0   )
			if decoder_attention_mask is None:
						A_     :						Any     =					np.where(decoder_input_ids != config.pad_token_id						,				1						,				0   )
			if head_mask is None:
						A_     :						Dict     =					np.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						A_     :						Dict     =					np.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						A_     :						Tuple     =					np.ones((config.decoder_layers, config.decoder_attention_heads)   )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": attention_mask,
			}







class      _lowerCAmelCase							:


	def __init__(self	,				lowercase	,				lowercase=13	,				lowercase=7	,				lowercase=True	,				lowercase=False	,				lowercase=99	,				lowercase=16	,				lowercase=2	,				lowercase=4	,				lowercase=4	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=32	,				lowercase=2	,				lowercase=1	,				lowercase=0	,				lowercase=0.02	,				):
				A_     :						int     =					parent
				A_     :						Union[str, Any]     =					batch_size
				A_     :						Any     =					seq_length
				A_     :						Any     =					is_training
				A_     :						Optional[Any]     =					use_labels
				A_     :						Optional[int]     =					vocab_size
				A_     :						List[str]     =					hidden_size
				A_     :						str     =					num_hidden_layers
				A_     :						Optional[Any]     =					num_attention_heads
				A_     :						str     =					intermediate_size
				A_     :						str     =					hidden_act
				A_     :						Dict     =					hidden_dropout_prob
				A_     :						List[str]     =					attention_probs_dropout_prob
				A_     :						str     =					max_position_embeddings
				A_     :						List[str]     =					eos_token_id
				A_     :						Dict     =					pad_token_id
				A_     :						Dict     =					bos_token_id
				A_     :						Tuple     =					initializer_range


	def    _a     (self							):
				A_     :						Any     =					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,				self.vocab_size							)	,				3	,				self.vocab_size							)
				A_     :						Tuple     =					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,				dtype=np.intaa							))	,				-1							)

				A_     :						Optional[Any]     =					shift_tokens_right(lowercase	,				1	,				2							)

				A_     :						Optional[Any]     =					BlenderbotSmallConfig(
				    vocab_size=self.vocab_size	,				d_model=self.hidden_size	,				encoder_layers=self.num_hidden_layers	,				decoder_layers=self.num_hidden_layers	,				encoder_attention_heads=self.num_attention_heads	,				decoder_attention_heads=self.num_attention_heads	,				encoder_ffn_dim=self.intermediate_size	,				decoder_ffn_dim=self.intermediate_size	,				dropout=self.hidden_dropout_prob	,				attention_dropout=self.attention_probs_dropout_prob	,				max_position_embeddings=self.max_position_embeddings	,				eos_token_id=self.eos_token_id	,				bos_token_id=self.bos_token_id	,				pad_token_id=self.pad_token_id	,				initializer_range=self.initializer_range	,				use_cache=lowercase	,				)
				A_     :						Any     =					prepare_blenderbot_inputs_dict(lowercase	,				lowercase	,				lowercase							)
				return config, inputs_dict


	def    _a     (self							):
				A_,   A_     :						Dict     =					self.prepare_config_and_inputs()
				return config, inputs_dict


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						Any     =					20
				A_     :						int     =					model_class_name(lowercase							)

				A_     :						Optional[Any]     =					model.encode(inputs_dict["""input_ids"""]							)

				A_,   A_     :						int     =					(
				    inputs_dict["""decoder_input_ids"""],
				    inputs_dict["""decoder_attention_mask"""],
				)

				A_     :						List[Any]     =					model.init_cache(decoder_input_ids.shape[0]	,				lowercase	,				lowercase							)
				A_     :						List[str]     =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,				dtype="""i4"""							)

				A_     :						int     =					jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]	,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,				)
				A_     :						Any     =					model.decode(
				    decoder_input_ids[:, :-1]	,				lowercase	,				decoder_attention_mask=lowercase	,				past_key_values=lowercase	,				decoder_position_ids=lowercase	,				)

				A_     :						int     =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,				dtype="""i4"""							)
				A_     :						Any     =					model.decode(
				    decoder_input_ids[:, -1:]	,				lowercase	,				decoder_attention_mask=lowercase	,				past_key_values=outputs_cache.past_key_values	,				decoder_position_ids=lowercase	,				)

				A_     :						str     =					model.decode(lowercase	,				lowercase							)

				A_     :						Dict     =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
				self.parent.assertTrue(diff < 1E-3	,				msg=F'Max diff is {diff}'							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						Any     =					20
				A_     :						Optional[int]     =					model_class_name(lowercase							)

				A_     :						Union[str, Any]     =					model.encode(inputs_dict["""input_ids"""]							)

				A_,   A_     :						Tuple     =					(
				    inputs_dict["""decoder_input_ids"""],
				    inputs_dict["""decoder_attention_mask"""],
				)

				A_     :						Optional[Any]     =					jnp.concatenate(
				    [
				        decoder_attention_mask,
				        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
				    ]	,				axis=-1	,				)

				A_     :						List[str]     =					model.init_cache(decoder_input_ids.shape[0]	,				lowercase	,				lowercase							)
				A_     :						Tuple     =					jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]	,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,				)

				A_     :						Union[str, Any]     =					model.decode(
				    decoder_input_ids[:, :-1]	,				lowercase	,				decoder_attention_mask=lowercase	,				past_key_values=lowercase	,				decoder_position_ids=lowercase	,				)
				A_     :						Tuple     =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,				dtype="""i4"""							)
				A_     :						Optional[Any]     =					model.decode(
				    decoder_input_ids[:, -1:]	,				lowercase	,				past_key_values=outputs_cache.past_key_values	,				decoder_attention_mask=lowercase	,				decoder_position_ids=lowercase	,				)

				A_     :						Optional[int]     =					model.decode(lowercase	,				lowercase	,				decoder_attention_mask=lowercase							)

				A_     :						Dict     =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
				self.parent.assertTrue(diff < 1E-3	,				msg=F'Max diff is {diff}'							)







@require_flax
class      _lowerCAmelCase							(      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 99


	def    _a     (self							):
				A_     :						Union[str, Any]     =					np.array(
				    [
				        [71, 82, 18, 33, 46, 91, 2],
				        [68, 34, 26, 58, 30, 82, 2],
				        [5, 97, 17, 39, 94, 40, 2],
				        [76, 83, 94, 25, 70, 78, 2],
				        [87, 59, 41, 35, 48, 66, 2],
				        [55, 13, 16, 58, 5, 2, 1],  # note padding
				        [64, 27, 31, 51, 12, 75, 2],
				        [52, 64, 86, 17, 83, 39, 2],
				        [48, 61, 9, 24, 71, 82, 2],
				        [26, 1, 60, 48, 22, 13, 2],
				        [21, 5, 62, 28, 14, 76, 2],
				        [45, 98, 37, 86, 59, 48, 2],
				        [70, 70, 50, 9, 28, 0, 2],
				    ]	,				dtype=np.intaa	,				)

				A_     :						List[str]     =					input_ids.shape[0]
				A_     :						Any     =					BlenderbotSmallConfig(
				    vocab_size=self.vocab_size	,				d_model=24	,				encoder_layers=2	,				decoder_layers=2	,				encoder_attention_heads=2	,				decoder_attention_heads=2	,				encoder_ffn_dim=32	,				decoder_ffn_dim=32	,				max_position_embeddings=48	,				eos_token_id=2	,				pad_token_id=1	,				bos_token_id=0	,				)
				return config, input_ids, batch_size


	def    _a     (self							):
				A_,   A_,   A_     :						Union[str, Any]     =					self._get_config_and_data()
				A_     :						List[str]     =					FlaxBlenderbotSmallForConditionalGeneration(lowercase							)
				A_     :						Tuple     =					lm_model(input_ids=lowercase							)
				A_     :						List[str]     =					(batch_size, input_ids.shape[1], config.vocab_size)
				self.assertEqual(outputs["""logits"""].shape	,				lowercase							)


	def    _a     (self							):
				A_     :						Optional[int]     =					BlenderbotSmallConfig(
				    vocab_size=self.vocab_size	,				d_model=14	,				encoder_layers=2	,				decoder_layers=2	,				encoder_attention_heads=2	,				decoder_attention_heads=2	,				encoder_ffn_dim=8	,				decoder_ffn_dim=8	,				max_position_embeddings=48	,				)
				A_     :						Tuple     =					FlaxBlenderbotSmallForConditionalGeneration(lowercase							)
				A_     :						List[str]     =					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	,				dtype=np.intaa							)
				A_     :						List[Any]     =					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	,				dtype=np.intaa							)
				A_     :						List[str]     =					lm_model(input_ids=lowercase	,				decoder_input_ids=lowercase							)
				A_     :						List[str]     =					(*summary.shape, config.vocab_size)
				self.assertEqual(outputs["""logits"""].shape	,				lowercase							)


	def    _a     (self							):
				A_     :						Optional[Any]     =					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,				dtype=np.intaa							)
				A_     :						Any     =					shift_tokens_right(lowercase	,				1	,				2							)
				A_     :						Union[str, Any]     =					np.equal(lowercase	,				1							).astype(np.floataa							).sum()
				A_     :						Optional[int]     =					np.equal(lowercase	,				1							).astype(np.floataa							).sum()
				self.assertEqual(shifted.shape	,				input_ids.shape							)
				self.assertEqual(lowercase	,				n_pad_before - 1							)
				self.assertTrue(np.equal(shifted[:, 0]	,				2							).all()							)







@require_flax
class      _lowerCAmelCase							(      __UpperCAmelCase				,      unittest.TestCase				,      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		List[Any]		      = True
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = (
	    (
	        FlaxBlenderbotSmallModel,
	        FlaxBlenderbotSmallForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE       :		Any		      = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


	def    _a     (self							):
				A_     :						Tuple     =					FlaxBlenderbotSmallModelTester(self							)


	def    _a     (self							):
				A_,   A_     :						Tuple     =					self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(lowercase	,				lowercase	,				lowercase							)


	def    _a     (self							):
				A_,   A_     :						List[Any]     =					self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward_with_attn_mask(lowercase	,				lowercase	,				lowercase							)


	def    _a     (self							):
				A_,   A_     :						List[Any]     =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__							):
										A_     :						Any     =					self._prepare_for_class(lowercase	,				lowercase							)
										A_     :						str     =					model_class(lowercase							)

										@jax.jit
										def encode_jitted(lowercase	,				lowercase=None	,				**lowercase							):
													return model.encode(input_ids=lowercase	,				attention_mask=lowercase							)

										with self.subTest("""JIT Enabled"""							):
													A_     :						Optional[Any]     =					encode_jitted(**lowercase							).to_tuple()

										with self.subTest("""JIT Disabled"""							):
													with jax.disable_jit():
																A_     :						int     =					encode_jitted(**lowercase							).to_tuple()

										self.assertEqual(len(lowercase							)	,				len(lowercase							)							)
										for jitted_output, output in zip(lowercase	,				lowercase							):
													self.assertEqual(jitted_output.shape	,				output.shape							)


	def    _a     (self							):
				A_,   A_     :						Tuple     =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__							):
										A_     :						Optional[Any]     =					model_class(lowercase							)
										A_     :						Optional[Any]     =					model.encode(inputs_dict["""input_ids"""]	,				inputs_dict["""attention_mask"""]							)

										A_     :						Dict     =					{
										    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
										    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
										    """encoder_outputs""": encoder_outputs,
										}

										@jax.jit
										def decode_jitted(lowercase	,				lowercase	,				lowercase							):
													return model.decode(
													    decoder_input_ids=lowercase	,				decoder_attention_mask=lowercase	,				encoder_outputs=lowercase	,				)

										with self.subTest("""JIT Enabled"""							):
													A_     :						Dict     =					decode_jitted(**lowercase							).to_tuple()

										with self.subTest("""JIT Disabled"""							):
													with jax.disable_jit():
																A_     :						Optional[Any]     =					decode_jitted(**lowercase							).to_tuple()

										self.assertEqual(len(lowercase							)	,				len(lowercase							)							)
										for jitted_output, output in zip(lowercase	,				lowercase							):
													self.assertEqual(jitted_output.shape	,				output.shape							)


	@slow
	def    _a     (self							):
				for model_class_name in self.all_model_classes:
							A_     :						Optional[Any]     =					model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""							)
							# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
							A_     :						int     =					np.ones((1, 1)							) * model.config.eos_token_id
							A_     :						Optional[int]     =					model(lowercase							)
							self.assertIsNotNone(lowercase							)




'''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							(      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = KandinskyVaaInpaintPipeline
	__SCREAMING_SNAKE_CASE       :		int		      = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
	__SCREAMING_SNAKE_CASE       :		str		      = [
	    'image_embeds',
	    'negative_image_embeds',
	    'image',
	    'mask_image',
	]
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = [
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	__SCREAMING_SNAKE_CASE       :		List[str]		      = False


	@property
	def    _a     (self							):
				return 32


	@property
	def    _a     (self							):
				return 32


	@property
	def    _a     (self							):
				return self.time_input_dim


	@property
	def    _a     (self							):
				return self.time_input_dim * 4


	@property
	def    _a     (self							):
				return 100


	@property
	def    _a     (self							):
				torch.manual_seed(0							)

				A_     :						str     =					{
				    """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,
				}

				A_     :						str     =					UNetaDConditionModel(**lowercase							)
				return model


	@property
	def    _a     (self							):
				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    _a     (self							):
				torch.manual_seed(0							)
				A_     :						int     =					VQModel(**self.dummy_movq_kwargs							)
				return model


	def    _a     (self							):
				A_     :						Optional[Any]     =					self.dummy_unet
				A_     :						Dict     =					self.dummy_movq

				A_     :						List[Any]     =					DDIMScheduler(
				    num_train_timesteps=1000	,				beta_schedule="""linear"""	,				beta_start=0.0_00_85	,				beta_end=0.0_12	,				clip_sample=lowercase	,				set_alpha_to_one=lowercase	,				steps_offset=1	,				prediction_type="""epsilon"""	,				thresholding=lowercase	,				)

				A_     :						Any     =					{
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """movq""": movq,
				}

				return components


	def    _a     (self	,				lowercase	,				lowercase=0							):
				A_     :						Optional[Any]     =					floats_tensor((1, self.text_embedder_hidden_size)	,				rng=random.Random(lowercase							)							).to(lowercase							)
				A_     :						List[Any]     =					floats_tensor((1, self.text_embedder_hidden_size)	,				rng=random.Random(seed + 1							)							).to(
				    lowercase							)
				# create init_image
				A_     :						Any     =					floats_tensor((1, 3, 64, 64)	,				rng=random.Random(lowercase							)							).to(lowercase							)
				A_     :						Union[str, Any]     =					image.cpu().permute(0	,				2	,				3	,				1							)[0]
				A_     :						Tuple     =					Image.fromarray(np.uinta(lowercase							)							).convert("""RGB"""							).resize((256, 256)							)
				# create mask
				A_     :						List[str]     =					np.ones((64, 64)	,				dtype=np.floataa							)
				A_     :						Any     =					0

				if str(lowercase							).startswith("""mps"""							):
							A_     :						int     =					torch.manual_seed(lowercase							)
				else:
							A_     :						Dict     =					torch.Generator(device=lowercase							).manual_seed(lowercase							)
				A_     :						Any     =					{
				    """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    _a     (self							):
				A_     :						List[str]     =					"""cpu"""

				A_     :						str     =					self.get_dummy_components()

				A_     :						Any     =					self.pipeline_class(**lowercase							)
				A_     :						List[Any]     =					pipe.to(lowercase							)

				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						Union[str, Any]     =					pipe(**self.get_dummy_inputs(lowercase							)							)
				A_     :						List[str]     =					output.images

				A_     :						Union[str, Any]     =					pipe(
				    **self.get_dummy_inputs(lowercase							)	,				return_dict=lowercase	,				)[0]

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

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

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

				A_     :						Optional[int]     =					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    _a     (self							):
				super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







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


	def    _a     (self							):
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


	def    _a     (self							):
				A_     :						Optional[Any]     =					load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""							)

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

				A_     :						str     =					"""a hat"""

				A_     :						Union[str, Any]     =					KandinskyVaaPriorPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-prior"""	,				torch_dtype=torch.floataa							)
				pipe_prior.to(lowercase							)

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

				A_     :						int     =					torch.Generator(device="""cpu"""							).manual_seed(0							)
				A_,   A_     :						Dict     =					pipe_prior(
				    lowercase	,				generator=lowercase	,				num_inference_steps=5	,				negative_prompt=""""""	,				).to_tuple()

				A_     :						List[Any]     =					pipeline(
				    image=lowercase	,				mask_image=lowercase	,				image_embeds=lowercase	,				negative_image_embeds=lowercase	,				generator=lowercase	,				num_inference_steps=100	,				height=768	,				width=768	,				output_type="""np"""	,				)

				A_     :						Tuple     =					output.images[0]

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

				assert_mean_pixel_difference(lowercase	,				lowercase							)