
"""simple docstring"""







from abc import ABC, abstractmethod
from typing import List, Optional




class 			A__	(     _lowerCamelCase):

		def __init__(				self			):
			# test for the above condition
			self.test()

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				0
			__lowerCAmelCase      :						Dict						      =				False
			while not completed:
				if counter == 1:
					self.reset()
				__lowerCAmelCase      :						Union[str, Any]						      =				self.advance()
				if not self.does_advance(_SCREAMING_SNAKE_CASE			):
					raise Exception(
					    'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'			)

				__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[Any]						      =				self.update(_SCREAMING_SNAKE_CASE			)
				counter += 1

				if counter > 1_00_00:
					raise Exception('update() does not fulfill the constraint.'			)

			if self.remaining() != 0:
				raise Exception('Custom Constraint is not defined correctly.'			)

		@abstractmethod
		def 							__lowerCamelCase							(				self			):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."			)

		@abstractmethod
		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."			)

		@abstractmethod
		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."			)

		@abstractmethod
		def 							__lowerCamelCase							(				self			):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."			)

		@abstractmethod
		def 							__lowerCamelCase							(				self			):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."			)







		@abstractmethod
		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=False			):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."			)




class 			A__	(     _lowerCamelCase):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE			):
			super(_SCREAMING_SNAKE_CASE	,		self			).__init__()

			if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) or len(_SCREAMING_SNAKE_CASE			) == 0:
				raise ValueError(f"`token_ids` has to be a non-empty list, but is {token_ids}."			)
			if any((not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) or token_id < 0) for token_id in token_ids			):
				raise ValueError(f"Each list in `token_ids` has to be a list of positive integers, but is {token_ids}."			)

			__lowerCAmelCase      :						Optional[Any]						      =				token_ids

			__lowerCAmelCase      :						Tuple						      =				len(self.token_ids			)
			__lowerCAmelCase      :						Optional[int]						      =				-1  # the index of the currently fulfilled step
			__lowerCAmelCase      :						Any						      =				False

		def 							__lowerCamelCase							(				self			):
			if self.completed:
				return None
			return self.token_ids[self.fulfilled_idx + 1]

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE			)}"			)

			if self.completed:
				return False

			return token_id == self.token_ids[self.fulfilled_idx + 1]

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE			)}"			)

			__lowerCAmelCase      :						str						      =				False
			__lowerCAmelCase      :						Union[str, Any]						      =				False
			__lowerCAmelCase      :						List[str]						      =				False

			if self.does_advance(_SCREAMING_SNAKE_CASE			):
				self.fulfilled_idx += 1
				__lowerCAmelCase      :						Tuple						      =				True
				if self.fulfilled_idx == (self.seqlen - 1):
					__lowerCAmelCase      :						Optional[int]						      =				True
				__lowerCAmelCase      :						Optional[Any]						      =				completed
			else:
				# failed to make progress.
				__lowerCAmelCase      :						Dict						      =				True
				self.reset()
			return stepped, completed, reset

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				False
			__lowerCAmelCase      :						Optional[Any]						      =				0

		def 							__lowerCamelCase							(				self			):
			return self.seqlen - (self.fulfilled_idx + 1)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=False			):
			__lowerCAmelCase      :						Tuple						      =				PhrasalConstraint(self.token_ids			)

			if stateful:
				__lowerCAmelCase      :						Tuple						      =				self.seqlen
				__lowerCAmelCase      :						List[Any]						      =				self.fulfilled_idx
				__lowerCAmelCase      :						Optional[Any]						      =				self.completed

			return new_constraint




class 			A__	:

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=True			):
			__lowerCAmelCase      :						Tuple						      =				max([len(_SCREAMING_SNAKE_CASE			) for one in nested_token_ids]			)

			__lowerCAmelCase      :						int						      =				{}
			for token_ids in nested_token_ids:
				__lowerCAmelCase      :						Tuple						      =				root
				for tidx, token_id in enumerate(_SCREAMING_SNAKE_CASE			):
					if token_id not in level:
						__lowerCAmelCase      :						List[Any]						      =				{}

					__lowerCAmelCase      :						str						      =				level[token_id]

			if no_subsets and self.has_subsets(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(
				    'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
				    f" {nested_token_ids}."			)

			__lowerCAmelCase      :						Optional[int]						      =				root

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Optional[Any]						      =				self.trie

			for current_token in current_seq:
				__lowerCAmelCase      :						List[Any]						      =				start[current_token]

			__lowerCAmelCase      :						Optional[Any]						      =				list(start.keys()			)

			return next_tokens

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Union[str, Any]						      =				self.next_tokens(_SCREAMING_SNAKE_CASE			)

			return len(_SCREAMING_SNAKE_CASE			) == 0

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[str]						      =				list(root.values()			)
			if len(_SCREAMING_SNAKE_CASE			) == 0:
				return 1
			else:
				return sum([self.count_leaves(_SCREAMING_SNAKE_CASE			) for nn in next_nodes]			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Dict						      =				self.count_leaves(_SCREAMING_SNAKE_CASE			)
			return len(_SCREAMING_SNAKE_CASE			) != leaf_count




class 			A__	(     _lowerCamelCase):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE			):
			super(_SCREAMING_SNAKE_CASE	,		self			).__init__()

			if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) or len(_SCREAMING_SNAKE_CASE			) == 0:
				raise ValueError(f"`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}."			)
			if any(not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) for token_ids in nested_token_ids			):
				raise ValueError(f"`nested_token_ids` has to be a list of lists, but is {nested_token_ids}."			)
			if any(
			    any((not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) or token_id < 0) for token_id in token_ids			)
			    for token_ids in nested_token_ids			):
				raise ValueError(
				    f"Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}."			)

			__lowerCAmelCase      :						Optional[Any]						      =				DisjunctiveTrie(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				nested_token_ids

			__lowerCAmelCase      :						List[str]						      =				self.trie.max_height
			__lowerCAmelCase      :						Tuple						      =				[]
			__lowerCAmelCase      :						List[Any]						      =				False

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[Any]						      =				self.trie.next_tokens(self.current_seq			)

			if len(_SCREAMING_SNAKE_CASE			) == 0:
				return None
			else:
				return token_list

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE			)}"			)

			__lowerCAmelCase      :						Optional[Any]						      =				self.trie.next_tokens(self.current_seq			)

			return token_id in next_tokens

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE			)}"			)

			__lowerCAmelCase      :						Optional[Any]						      =				False
			__lowerCAmelCase      :						List[Any]						      =				False
			__lowerCAmelCase      :						Optional[Any]						      =				False

			if self.does_advance(_SCREAMING_SNAKE_CASE			):
				self.current_seq.append(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						Tuple						      =				True
			else:
				__lowerCAmelCase      :						Dict						      =				True
				self.reset()

			__lowerCAmelCase      :						List[str]						      =				self.trie.reached_leaf(self.current_seq			)
			__lowerCAmelCase      :						int						      =				completed

			return stepped, completed, reset

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				False
			__lowerCAmelCase      :						List[Any]						      =				[]

		def 							__lowerCamelCase							(				self			):
			if self.completed:
				# since this can be completed without reaching max height
				return 0
			else:
				return self.seqlen - len(self.current_seq			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=False			):
			__lowerCAmelCase      :						Dict						      =				DisjunctiveConstraint(self.token_ids			)

			if stateful:
				__lowerCAmelCase      :						List[str]						      =				self.seqlen
				__lowerCAmelCase      :						List[Any]						      =				self.current_seq
				__lowerCAmelCase      :						Dict						      =				self.completed

			return new_constraint




class 			A__	:

		def __init__(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Optional[int]						      =				constraints

			# max # of steps required to fulfill a given constraint
			__lowerCAmelCase      :						List[Any]						      =				max([c.seqlen for c in constraints]			)
			__lowerCAmelCase      :						Any						      =				len(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				False

			self.init_state()

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				[]
			__lowerCAmelCase      :						Optional[int]						      =				None
			__lowerCAmelCase      :						Optional[int]						      =				[constraint.copy(stateful=_SCREAMING_SNAKE_CASE			) for constraint in self.constraints]

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				0
			if self.inprogress_constraint:
				# extra points for having a constraint mid-fulfilled
				add += self.max_seqlen - self.inprogress_constraint.remaining()

			return (len(self.complete_constraints			) * self.max_seqlen) + add

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				[]
			if self.inprogress_constraint is None:
				for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
					__lowerCAmelCase      :						Optional[Any]						      =				constraint.advance()
					if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
						token_list.append(_SCREAMING_SNAKE_CASE			)
					elif isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
						token_list.extend(_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						Tuple						      =				self.inprogress_constraint.advance()
				if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
					token_list.append(_SCREAMING_SNAKE_CASE			)
				elif isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
					token_list.extend(_SCREAMING_SNAKE_CASE			)

			if len(_SCREAMING_SNAKE_CASE			) == 0:
				return None
			else:
				return token_list

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			self.init_state()

			if token_ids is not None:
				for token in token_ids:
					# completes or steps **one** constraint
					__lowerCAmelCase      ,       __lowerCAmelCase      :						Dict						      =				self.add(_SCREAMING_SNAKE_CASE			)

					# the entire list of constraints are fulfilled
					if self.completed:
						break

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"`token_id` should be an `int`, but is `{token_id}`."			)

			__lowerCAmelCase      ,       __lowerCAmelCase      :						Tuple						      =				False, False

			if self.completed:
				__lowerCAmelCase      :						Union[str, Any]						      =				True
				__lowerCAmelCase      :						List[str]						      =				False
				return complete, stepped

			if self.inprogress_constraint is not None:
				# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
				# job, simply update the state

				__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						List[str]						      =				self.inprogress_constraint.update(_SCREAMING_SNAKE_CASE			)
				if reset:
					# 1. If the next token breaks the progress, then we must restart.
					#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

					#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
					#     constraint, not the full list of constraints.

					self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_SCREAMING_SNAKE_CASE			)			)
					__lowerCAmelCase      :						Union[str, Any]						      =				None

				if complete:
					# 2. If the next token completes the constraint, move it to completed list, set
					#     inprogress to None. If there are no pending constraints either, then this full list of constraints
					#     is complete.

					self.complete_constraints.append(self.inprogress_constraint			)
					__lowerCAmelCase      :						List[str]						      =				None

					if len(self.pending_constraints			) == 0:
						# we're done!
						__lowerCAmelCase      :						Dict						      =				True

			else:
				# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
				# of constraints?

				for cidx, pending_constraint in enumerate(self.pending_constraints			):
					if pending_constraint.does_advance(_SCREAMING_SNAKE_CASE			):
						__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						Union[str, Any]						      =				pending_constraint.update(_SCREAMING_SNAKE_CASE			)

						if not stepped:
							raise Exception(
							    '`constraint.update(token_id)` is not yielding incremental progress, '
							    'even though `constraint.does_advance(token_id)` is true.'			)

						if complete:
							self.complete_constraints.append(_SCREAMING_SNAKE_CASE			)
							__lowerCAmelCase      :						str						      =				None

						if not complete and stepped:
							__lowerCAmelCase      :						int						      =				pending_constraint

						if complete or stepped:
							# If we made any progress at all, then it's at least not a "pending constraint".

							__lowerCAmelCase      :						str						      =				(
							    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
							)

							if len(self.pending_constraints			) == 0 and self.inprogress_constraint is None:
								# If there's no longer any pending after this and no inprogress either, then we must be
								# complete.

								__lowerCAmelCase      :						str						      =				True

							break  # prevent accidentally stepping through multiple constraints with just one token.

			return complete, stepped







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=True			):
			__lowerCAmelCase      :						Union[str, Any]						      =				ConstraintListState(self.constraints			)  # we actually never though self.constraints objects
			# throughout this process. So it's at initialization state.

			if stateful:
				__lowerCAmelCase      :						Optional[Any]						      =				[
				    constraint.copy(stateful=_SCREAMING_SNAKE_CASE			) for constraint in self.complete_constraints
				]
				if self.inprogress_constraint is not None:
					__lowerCAmelCase      :						Optional[Any]						      =				self.inprogress_constraint.copy(stateful=_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						List[Any]						      =				[constraint.copy() for constraint in self.pending_constraints]

			return new_state

"""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 			A__	(     _lowerCamelCase	,					unittest.TestCase):
		A_						:	List[Any]           =     KandinskyVaaInpaintPipeline
		A_						:	Optional[int]           =     ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		A_						:	Any           =     [
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		A_						:	Any           =     [
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		A_						:	Any           =     False

		@property
		def 							__lowerCamelCase							(				self			):
			return 32

		@property
		def 							__lowerCamelCase							(				self			):
			return 32

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

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

		@property
		def 							__lowerCamelCase							(				self			):
			return 1_00

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

			__lowerCAmelCase      :						Optional[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,
			}

			__lowerCAmelCase      :						Any						      =				UNetaDConditionModel(**_SCREAMING_SNAKE_CASE			)
			return model

		@property
		def 							__lowerCamelCase							(				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 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)
			__lowerCAmelCase      :						Optional[Any]						      =				VQModel(**self.dummy_movq_kwargs			)
			return model

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.dummy_unet
			__lowerCAmelCase      :						Optional[Any]						      =				self.dummy_movq

			__lowerCAmelCase      :						Tuple						      =				DDIMScheduler(
			    num_train_timesteps=10_00	,		beta_schedule='linear'	,		beta_start=0.0_0085	,		beta_end=0.012	,		clip_sample=_SCREAMING_SNAKE_CASE	,		set_alpha_to_one=_SCREAMING_SNAKE_CASE	,		steps_offset=1	,		prediction_type='epsilon'	,		thresholding=_SCREAMING_SNAKE_CASE	,		)

			__lowerCAmelCase      :						str						      =				{
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=0			):
			__lowerCAmelCase      :						List[Any]						      =				floats_tensor((1, self.text_embedder_hidden_size)	,		rng=random.Random(_SCREAMING_SNAKE_CASE			)			).to(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				floats_tensor((1, self.text_embedder_hidden_size)	,		rng=random.Random(seed + 1			)			).to(
			    _SCREAMING_SNAKE_CASE			)
			# create init_image
			__lowerCAmelCase      :						int						      =				floats_tensor((1, 3, 64, 64)	,		rng=random.Random(_SCREAMING_SNAKE_CASE			)			).to(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				image.cpu().permute(0	,		2	,		3	,		1			)[0]
			__lowerCAmelCase      :						str						      =				Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE			)			).convert('RGB'			).resize((2_56, 2_56)			)
			# create mask
			__lowerCAmelCase      :						Dict						      =				np.ones((64, 64)	,		dtype=np.floataa			)
			__lowerCAmelCase      :						List[str]						      =				0

			if str(_SCREAMING_SNAKE_CASE			).startswith('mps'			):
				__lowerCAmelCase      :						Optional[int]						      =				torch.manual_seed(_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						List[Any]						      =				torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				'cpu'

			__lowerCAmelCase      :						Dict						      =				self.get_dummy_components()

			__lowerCAmelCase      :						Union[str, Any]						      =				self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				pipe.to(_SCREAMING_SNAKE_CASE			)

			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[Any]						      =				pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						Optional[Any]						      =				output.images

			__lowerCAmelCase      :						Any						      =				pipe(
			    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)	,		return_dict=_SCREAMING_SNAKE_CASE	,		)[0]

			__lowerCAmelCase      :						Union[str, Any]						      =				image[0, -3:, -3:, -1]
			__lowerCAmelCase      :						Any						      =				image_from_tuple[0, -3:, -3:, -1]

			print(f"image.shape {image.shape}"			)

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

			__lowerCAmelCase      :						str						      =				np.array(
			    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]			)

			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 							__lowerCamelCase							(				self			):
			super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




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

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







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'			)

			__lowerCAmelCase      :						Union[str, Any]						      =				load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'			)
			__lowerCAmelCase      :						Any						      =				np.ones((7_68, 7_68)	,		dtype=np.floataa			)
			__lowerCAmelCase      :						int						      =				0

			__lowerCAmelCase      :						str						      =				'a hat'

			__lowerCAmelCase      :						str						      =				KandinskyVaaPriorPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior'	,		torch_dtype=torch.floataa			)
			pipe_prior.to(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[int]						      =				KandinskyVaaInpaintPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-decoder-inpaint'	,		torch_dtype=torch.floataa			)
			__lowerCAmelCase      :						Tuple						      =				pipeline.to(_SCREAMING_SNAKE_CASE			)
			pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				torch.Generator(device='cpu'			).manual_seed(0			)
			__lowerCAmelCase      ,       __lowerCAmelCase      :						Any						      =				pipe_prior(
			    _SCREAMING_SNAKE_CASE	,		generator=_SCREAMING_SNAKE_CASE	,		num_inference_steps=5	,		negative_prompt=''	,		).to_tuple()

			__lowerCAmelCase      :						Tuple						      =				pipeline(
			    image=_SCREAMING_SNAKE_CASE	,		mask_image=_SCREAMING_SNAKE_CASE	,		image_embeds=_SCREAMING_SNAKE_CASE	,		negative_image_embeds=_SCREAMING_SNAKE_CASE	,		generator=_SCREAMING_SNAKE_CASE	,		num_inference_steps=1_00	,		height=7_68	,		width=7_68	,		output_type='np'	,		)

			__lowerCAmelCase      :						Optional[Any]						      =				output.images[0]

			assert image.shape == (7_68, 7_68, 3)

			assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)