
'''simple docstring'''


import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)




def lowercase_	(   _lowercase				)     ->							Dict:
					'''simple docstring'''

					lowerCamelCase_					:      str     =		torch.exp(a__				)
					lowerCamelCase_					:      Tuple     =		torch.sum(a__			,       dim=1				)  # sum of exp(x_i)
					lowerCamelCase_					:      int     =		torch.sum(x * exp_x			,       dim=1				)  # sum of x_i * exp(x_i)
					return torch.log(a__				) - B / A

class 	__lowercase				(  nn.Module    ):

			def __init__(self			,   A      ):
								super().__init__()
								lowerCamelCase_					:      List[str]     =		config.output_attentions
								lowerCamelCase_					:      Optional[Any]     =		config.output_hidden_states
								lowerCamelCase_					:      List[str]     =		nn.ModuleList([BertLayer(__A      ) for _ in range(config.num_hidden_layers      )]      )
								lowerCamelCase_					:      Optional[Any]     =		nn.ModuleList([BertHighway(__A      ) for _ in range(config.num_hidden_layers      )]      )

								lowerCamelCase_					:      Dict     =		[-1 for _ in range(config.num_hidden_layers      )]

			def        UpperCAmelCase__ (self			,   A      ):
								if (type(__A      ) is float) or (type(__A      ) is int):
													for i in range(len(self.early_exit_entropy      )      ):
																		lowerCamelCase_					:      Tuple     =		x
								else:
													lowerCamelCase_					:      Optional[int]     =		x

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Optional[int]     =		pooler.state_dict()
								for highway in self.highway:
													for name, param in highway.pooler.state_dict().items():
																		param.copy_(loaded_model[name]      )






			def        UpperCAmelCase__ (self			,   A			,   A=None			,   A=None			,   A=None			,   A=None			,   ):
								lowerCamelCase_					:      List[Any]     =		()
								lowerCamelCase_					:      Tuple     =		()
								lowerCamelCase_					:      List[str]     =		()
								for i, layer_module in enumerate(self.layer      ):
													if self.output_hidden_states:
																		lowerCamelCase_					:      str     =		all_hidden_states + (hidden_states,)

													lowerCamelCase_					:      List[str]     =		layer_module(
													    __A			,   __A			,   head_mask[i]			,   __A			,   __A      )
													lowerCamelCase_					:      Tuple     =		layer_outputs[0]

													if self.output_attentions:
																		lowerCamelCase_					:      List[str]     =		all_attentions + (layer_outputs[1],)

													lowerCamelCase_					:      List[str]     =		(hidden_states,)
													if self.output_hidden_states:
																		lowerCamelCase_					:      Any     =		current_outputs + (all_hidden_states,)
													if self.output_attentions:
																		lowerCamelCase_					:      str     =		current_outputs + (all_attentions,)

													lowerCamelCase_					:      List[str]     =		self.highway[i](__A      )
													# logits, pooled_output

													if not self.training:
																		lowerCamelCase_					:      List[str]     =		highway_exit[0]
																		lowerCamelCase_					:      Tuple     =		entropy(__A      )
																		lowerCamelCase_					:      int     =		highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																		lowerCamelCase_					:      Tuple     =		all_highway_exits + (highway_exit,)

																		if highway_entropy < self.early_exit_entropy[i]:
																							lowerCamelCase_					:      Tuple     =		(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																							raise HighwayException(__A			,   i + 1      )
													else:
																		lowerCamelCase_					:      Optional[int]     =		all_highway_exits + (highway_exit,)

        # Add last layer
								if self.output_hidden_states:
													lowerCamelCase_					:      int     =		all_hidden_states + (hidden_states,)

								lowerCamelCase_					:      Optional[int]     =		(hidden_states,)
								if self.output_hidden_states:
													lowerCamelCase_					:      Dict     =		outputs + (all_hidden_states,)
								if self.output_attentions:
													lowerCamelCase_					:      Optional[int]     =		outputs + (all_attentions,)

								lowerCamelCase_					:      Any     =		outputs + (all_highway_exits,)
								return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits

@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "      ,     __lowercase      ,     )
class 	__lowercase				(  __lowercase    ):

			def __init__(self			,   A      ):
								super().__init__(__A      )
								lowerCamelCase_					:      List[str]     =		config

								lowerCamelCase_					:      str     =		BertEmbeddings(__A      )
								lowerCamelCase_					:      Any     =		DeeBertEncoder(__A      )
								lowerCamelCase_					:      int     =		BertPooler(__A      )

								self.init_weights()

			def        UpperCAmelCase__ (self      ):
								self.encoder.init_highway_pooler(self.pooler      )

			def        UpperCAmelCase__ (self      ):
								return self.embeddings.word_embeddings

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Dict     =		value

			def        UpperCAmelCase__ (self			,   A      ):
								for layer, heads in heads_to_prune.items():
													self.encoder.layer[layer].attention.prune_heads(__A      )






			@add_start_docstrings_to_model_forward(__A      )
			def        UpperCAmelCase__ (self			,   A=None			,   A=None			,   A=None			,   A=None			,   A=None			,   A=None			,   A=None			,   A=None			,   ):
								if input_ids is not None and inputs_embeds is not None:
													raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''      )
								elif input_ids is not None:
													lowerCamelCase_					:      Optional[Any]     =		input_ids.size()
								elif inputs_embeds is not None:
													lowerCamelCase_					:      Optional[Any]     =		inputs_embeds.size()[:-1]
								else:
													raise ValueError('''You have to specify either input_ids or inputs_embeds'''      )

								lowerCamelCase_					:      Union[str, Any]     =		input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
													lowerCamelCase_					:      List[str]     =		torch.ones(__A			,   device=__A      )
								if encoder_attention_mask is None:
													lowerCamelCase_					:      Any     =		torch.ones(__A			,   device=__A      )
								if token_type_ids is None:
													lowerCamelCase_					:      Optional[Any]     =		torch.zeros(__A			,   dtype=torch.long			,   device=__A      )

								# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
								# ourselves in which case we just need to make it broadcastable to all heads.
								lowerCamelCase_					:      List[str]     =		self.get_extended_attention_mask(__A			,   __A			,   __A      )

								# If a 2D ou 3D attention mask is provided for the cross-attention
								# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
								if encoder_attention_mask.dim() == 3:
													lowerCamelCase_					:      Tuple     =		encoder_attention_mask[:, None, :, :]
								if encoder_attention_mask.dim() == 2:
													lowerCamelCase_					:      Dict     =		encoder_attention_mask[:, None, None, :]

								lowerCamelCase_					:      int     =		encoder_extended_attention_mask.to(
								    dtype=next(self.parameters()      ).dtype      )  # fp16 compatibility
								lowerCamelCase_					:      Tuple     =		(1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

								# Prepare head mask if needed
								# 1.0 in head_mask indicate we keep the head
								# attention_probs has shape bsz x n_heads x N x N
								# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
								# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
								lowerCamelCase_					:      str     =		self.get_head_mask(__A			,   self.config.num_hidden_layers      )

								lowerCamelCase_					:      Union[str, Any]     =		self.embeddings(
								    input_ids=__A			,   position_ids=__A			,   token_type_ids=__A			,   inputs_embeds=__A      )
								lowerCamelCase_					:      Optional[Any]     =		self.encoder(
								    __A			,   attention_mask=__A			,   head_mask=__A			,   encoder_hidden_states=__A			,   encoder_attention_mask=__A			,   )
								lowerCamelCase_					:      str     =		encoder_outputs[0]
								lowerCamelCase_					:      List[str]     =		self.pooler(__A      )

								lowerCamelCase_					:      str     =		(
								    sequence_output,
								    pooled_output,
								) + encoder_outputs[
								    1:
								]  # add hidden_states and attentions if they are here
								return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

class 	__lowercase				(  __lowercase    ):

			def __init__(self			,   A			,   A      ):
								lowerCamelCase_					:      Dict     =		message
								lowerCamelCase_					:      int     =		exit_layer  # start from 1!

class 	__lowercase				(  nn.Module    ):

			def __init__(self			,   A      ):
								super().__init__()
								lowerCamelCase_					:      Optional[int]     =		BertPooler(__A      )
								lowerCamelCase_					:      Optional[Any]     =		nn.Dropout(config.hidden_dropout_prob      )
								lowerCamelCase_					:      Union[str, Any]     =		nn.Linear(config.hidden_size			,   config.num_labels      )






			def        UpperCAmelCase__ (self			,   A      ):
								# Pooler
								lowerCamelCase_					:      str     =		encoder_outputs[0]
								lowerCamelCase_					:      Dict     =		self.pooler(__A      )
								# "return" pooler_output

								# BertModel
								lowerCamelCase_					:      Union[str, Any]     =		(pooler_input, pooler_output) + encoder_outputs[1:]
								# "return" bmodel_output

								# Dropout and classification
								lowerCamelCase_					:      Optional[int]     =		bmodel_output[1]

								lowerCamelCase_					:      str     =		self.dropout(__A      )
								lowerCamelCase_					:      Dict     =		self.classifier(__A      )

								return logits, pooled_output

@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "      ,     __lowercase      ,     )
class 	__lowercase				(  __lowercase    ):

			def __init__(self			,   A      ):
								super().__init__(__A      )
								lowerCamelCase_					:      List[Any]     =		config.num_labels
								lowerCamelCase_					:      Tuple     =		config.num_hidden_layers

								lowerCamelCase_					:      Optional[int]     =		DeeBertModel(__A      )
								lowerCamelCase_					:      Union[str, Any]     =		nn.Dropout(config.hidden_dropout_prob      )
								lowerCamelCase_					:      Union[str, Any]     =		nn.Linear(config.hidden_size			,   self.config.num_labels      )

								self.init_weights()






			@add_start_docstrings_to_model_forward(__A      )
			def        UpperCAmelCase__ (self			,   A=None			,   A=None			,   A=None			,   A=None			,   A=None			,   A=None			,   A=None			,   A=-1			,   A=False			,   ):
								lowerCamelCase_					:      Union[str, Any]     =		self.num_layers
								try:
													lowerCamelCase_					:      Optional[Any]     =		self.bert(
													    __A			,   attention_mask=__A			,   token_type_ids=__A			,   position_ids=__A			,   head_mask=__A			,   inputs_embeds=__A			,   )
													# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

													lowerCamelCase_					:      Any     =		outputs[1]

													lowerCamelCase_					:      str     =		self.dropout(__A      )
													lowerCamelCase_					:      Optional[Any]     =		self.classifier(__A      )
													lowerCamelCase_					:      Optional[Any]     =		(logits,) + outputs[2:]  # add hidden states and attention if they are here
								except HighwayException as e:
													lowerCamelCase_					:      List[str]     =		e.message
													lowerCamelCase_					:      int     =		e.exit_layer
													lowerCamelCase_					:      Optional[Any]     =		outputs[0]

								if not self.training:
													lowerCamelCase_					:      Any     =		entropy(__A      )
													lowerCamelCase_					:      Tuple     =		[]
													lowerCamelCase_					:      Union[str, Any]     =		[]
								if labels is not None:
													if self.num_labels == 1:
																		#  We are doing regression
																		lowerCamelCase_					:      Dict     =		MSELoss()
																		lowerCamelCase_					:      str     =		loss_fct(logits.view(-1      )			,   labels.view(-1      )      )
													else:
																		lowerCamelCase_					:      Optional[int]     =		CrossEntropyLoss()
																		lowerCamelCase_					:      Optional[Any]     =		loss_fct(logits.view(-1			,   self.num_labels      )			,   labels.view(-1      )      )

													# work with highway exits
													lowerCamelCase_					:      Optional[Any]     =		[]
													for highway_exit in outputs[-1]:
																		lowerCamelCase_					:      Optional[int]     =		highway_exit[0]
																		if not self.training:
																							highway_logits_all.append(__A      )
																							highway_entropy.append(highway_exit[2]      )
																		if self.num_labels == 1:
																							#  We are doing regression
																							lowerCamelCase_					:      List[Any]     =		MSELoss()
																							lowerCamelCase_					:      Union[str, Any]     =		loss_fct(highway_logits.view(-1      )			,   labels.view(-1      )      )
																		else:
																							lowerCamelCase_					:      Dict     =		CrossEntropyLoss()
																							lowerCamelCase_					:      List[Any]     =		loss_fct(highway_logits.view(-1			,   self.num_labels      )			,   labels.view(-1      )      )
																		highway_losses.append(__A      )

													if train_highway:
																		lowerCamelCase_					:      Any     =		(sum(highway_losses[:-1]      ),) + outputs
																		# exclude the final highway, of course
													else:
																		lowerCamelCase_					:      Union[str, Any]     =		(loss,) + outputs
								if not self.training:
													lowerCamelCase_					:      Dict     =		outputs + ((original_entropy, highway_entropy), exit_layer)
													if output_layer >= 0:
																		lowerCamelCase_					:      Optional[Any]     =		(
																		    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																		)  # use the highway of the last layer

								return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)




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

			@property
			def        UpperCAmelCase__ (self      ):
								return 3_2

			@property
			def        UpperCAmelCase__ (self      ):
								return 3_2

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

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

			@property
			def        UpperCAmelCase__ (self      ):
								return 1_0_0

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

								lowerCamelCase_					:      Optional[Any]     =		{
								    '''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_					:      str     =		UNetaDConditionModel(**A      )
								return model

			@property
			def        UpperCAmelCase__ (self      ):
								return {
								    "block_out_channels": [3_2, 6_4],
								    "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": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}

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

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		self.dummy_unet
								lowerCamelCase_					:      Optional[int]     =		self.dummy_movq

								lowerCamelCase_					:      Union[str, Any]     =		DDIMScheduler(
								    num_train_timesteps=1_0_0_0			,   beta_schedule='''linear'''			,   beta_start=0.0_00_85			,   beta_end=0.0_12			,   clip_sample=A			,   set_alpha_to_one=A			,   steps_offset=1			,   prediction_type='''epsilon'''			,   thresholding=A			,   )

								lowerCamelCase_					:      Dict     =		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components

			def        UpperCAmelCase__ (self			,   A			,   A=0      ):
								lowerCamelCase_					:      List[Any]     =		floats_tensor((1, self.text_embedder_hidden_size)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      Tuple     =		floats_tensor((1, self.text_embedder_hidden_size)			,   rng=random.Random(seed + 1      )      ).to(
								    A      )
								# create init_image
								lowerCamelCase_					:      Dict     =		floats_tensor((1, 3, 6_4, 6_4)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      List[str]     =		image.cpu().permute(0			,   2			,   3			,   1      )[0]
								lowerCamelCase_					:      Any     =		Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      ).resize((2_5_6, 2_5_6)      )
								# create mask
								lowerCamelCase_					:      List[str]     =		np.ones((6_4, 6_4)			,   dtype=np.floataa      )
								lowerCamelCase_					:      Union[str, Any]     =		0

								if str(A      ).startswith('''mps'''      ):
													lowerCamelCase_					:      int     =		torch.manual_seed(A      )
								else:
													lowerCamelCase_					:      Union[str, Any]     =		torch.Generator(device=A      ).manual_seed(A      )
								lowerCamelCase_					:      Optional[Any]     =		{
								    '''image''': init_image,
								    '''mask_image''': mask,
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 4.0,
								    '''output_type''': '''np''',
								}
								return inputs

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		'''cpu'''

								lowerCamelCase_					:      str     =		self.get_dummy_components()

								lowerCamelCase_					:      Tuple     =		self.pipeline_class(**A      )
								lowerCamelCase_					:      Union[str, Any]     =		pipe.to(A      )

								pipe.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      str     =		pipe(**self.get_dummy_inputs(A      )      )
								lowerCamelCase_					:      Any     =		output.images

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

								lowerCamelCase_					:      List[Any]     =		image[0, -3:, -3:, -1]
								lowerCamelCase_					:      Tuple     =		image_from_tuple[0, -3:, -3:, -1]

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

								assert image.shape == (1, 6_4, 6_4, 3)

								lowerCamelCase_					:      Any     =		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      ):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3      )

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

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






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

								lowerCamelCase_					:      Optional[Any]     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
								lowerCamelCase_					:      List[str]     =		np.ones((7_6_8, 7_6_8)			,   dtype=np.floataa      )
								lowerCamelCase_					:      int     =		0

								lowerCamelCase_					:      Union[str, Any]     =		'''a hat'''

								lowerCamelCase_					:      Optional[int]     =		KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''			,   torch_dtype=torch.floataa      )
								pipe_prior.to(A      )

								lowerCamelCase_					:      Any     =		KandinskyVaaInpaintPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''			,   torch_dtype=torch.floataa      )
								lowerCamelCase_					:      Any     =		pipeline.to(A      )
								pipeline.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      Any     =		torch.Generator(device='''cpu'''      ).manual_seed(0      )
								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		pipe_prior(
								    A			,   generator=A			,   num_inference_steps=5			,   negative_prompt=''''''			,   ).to_tuple()

								lowerCamelCase_					:      Union[str, Any]     =		pipeline(
								    image=A			,   mask_image=A			,   image_embeds=A			,   negative_image_embeds=A			,   generator=A			,   num_inference_steps=1_0_0			,   height=7_6_8			,   width=7_6_8			,   output_type='''np'''			,   )

								lowerCamelCase_					:      List[str]     =		output.images[0]

								assert image.shape == (7_6_8, 7_6_8, 3)

								assert_mean_pixel_difference(A			,   A      )


