





import logging

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,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


_lowerCAmelCase 				=     logging.getLogger(__name__)

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__=None      ,       a__=None       ):
         A_     :				int			      =	self.layer[current_layer](a__      ,       a__      ,       head_mask[current_layer]       )

         A_     :				str			      =	layer_outputs[0]

         return hidden_states






@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''			,       _lowerCamelCase			,       )
class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __init__(		self      ,       a__       ):
         super().__init__(a__       )

         A_     :				Optional[int]			      =	BertEncoderWithPabee(a__       )

         self.init_weights()
         A_     :				List[str]			      =	0
         A_     :				Any			      =	0
         A_     :				Any			      =	0

         A_     :				Union[str, Any]			      =	0
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Any			      =	threshold
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				int			      =	patience
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[Any]			      =	0
         A_     :				Dict			      =	0
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	self.inference_layers_num / self.inference_instances_num
         A_     :				Optional[int]			      =	(
             F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
             F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
         )
         print(a__       )






      @add_start_docstrings_to_model_forward(a__       )
      def 		_lowerCamelCase      (		self      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=False      ,       ):

         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:
            A_     :				List[Any]			      =	input_ids.size()
         elif inputs_embeds is not None:
            A_     :				Optional[Any]			      =	inputs_embeds.size()[:-1]
         else:
            raise ValueError("""You have to specify either input_ids or inputs_embeds"""       )

         A_     :				List[str]			      =	input_ids.device if input_ids is not None else inputs_embeds.device

         if attention_mask is None:
            A_     :				List[str]			      =	torch.ones(a__      ,       device=a__       )
         if token_type_ids is None:
            A_     :				Tuple			      =	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.
         A_     :				torch.Tensor			      =	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 self.config.is_decoder and encoder_hidden_states is not None:
            A_     :				Optional[int]			      =	encoder_hidden_states.size()
            A_     :				Tuple			      =	(encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
               A_     :				Union[str, Any]			      =	torch.ones(a__      ,       device=a__       )
            A_     :				Dict			      =	self.invert_attention_mask(a__       )
         else:
            A_     :				Any			      =	None

         # 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]
         A_     :				Dict			      =	self.get_head_mask(a__      ,       self.config.num_hidden_layers       )

         A_     :				Dict			      =	self.embeddings(
             input_ids=a__      ,       position_ids=a__      ,       token_type_ids=a__      ,       inputs_embeds=a__       )
         A_     :				Union[str, Any]			      =	embedding_output

         if self.training:
            A_     :				Any			      =	[]
            for i in range(self.config.num_hidden_layers       ):
               A_     :				Dict			      =	self.encoder.adaptive_forward(
                   a__      ,       current_layer=a__      ,       attention_mask=a__      ,       head_mask=a__       )

               A_     :				List[str]			      =	self.pooler(a__       )
               A_     :				List[Any]			      =	output_layers[i](output_dropout(a__       )       )
               res.append(a__       )
         elif self.patience == 0:  # Use all layers for inference
            A_     :				List[Any]			      =	self.encoder(
                a__      ,       attention_mask=a__      ,       head_mask=a__      ,       encoder_hidden_states=a__      ,       encoder_attention_mask=a__      ,       )
            A_     :				List[Any]			      =	self.pooler(encoder_outputs[0]       )
            A_     :				List[Any]			      =	[output_layers[self.config.num_hidden_layers - 1](a__       )]
         else:
            A_     :				Tuple			      =	0
            A_     :				Tuple			      =	None
            A_     :				int			      =	0
            for i in range(self.config.num_hidden_layers       ):
               calculated_layer_num += 1
               A_     :				List[str]			      =	self.encoder.adaptive_forward(
                   a__      ,       current_layer=a__      ,       attention_mask=a__      ,       head_mask=a__       )

               A_     :				Optional[Any]			      =	self.pooler(a__       )
               A_     :				Union[str, Any]			      =	output_layers[i](a__       )
               if regression:
                  A_     :				Dict			      =	logits.detach()
                  if patient_result is not None:
                     A_     :				List[str]			      =	patient_result.detach()
                  if (patient_result is not None) and torch.abs(patient_result - labels       ) < self.regression_threshold:
                     patient_counter += 1
                  else:
                     A_     :				int			      =	0
               else:
                  A_     :				Optional[Any]			      =	logits.detach().argmax(dim=1       )
                  if patient_result is not None:
                     A_     :				Optional[int]			      =	patient_result.detach().argmax(dim=1       )
                  if (patient_result is not None) and torch.all(labels.eq(a__       )       ):
                     patient_counter += 1
                  else:
                     A_     :				Optional[Any]			      =	0

               A_     :				Any			      =	logits
               if patient_counter == self.patience:
                  break
            A_     :				Dict			      =	[patient_result]
            self.inference_layers_num += calculated_layer_num
            self.inference_instances_num += 1

         return res






@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''			,       _lowerCamelCase			,       )
class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __init__(		self      ,       a__       ):
         super().__init__(a__       )
         A_     :				List[str]			      =	config.num_labels

         A_     :				Dict			      =	BertModelWithPabee(a__       )
         A_     :				Dict			      =	nn.Dropout(config.hidden_dropout_prob       )
         A_     :				List[Any]			      =	nn.ModuleList(
             [nn.Linear(config.hidden_size      ,       self.config.num_labels       ) for _ in range(config.num_hidden_layers       )]       )

         self.init_weights()






      @add_start_docstrings_to_model_forward(a__       )
      def 		_lowerCamelCase      (		self      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       ):
         A_     :				Optional[int]			      =	self.bert(
             input_ids=a__      ,       attention_mask=a__      ,       token_type_ids=a__      ,       position_ids=a__      ,       head_mask=a__      ,       inputs_embeds=a__      ,       output_dropout=self.dropout      ,       output_layers=self.classifiers      ,       regression=self.num_labels == 1      ,       )

         A_     :				str			      =	(logits[-1],)

         if labels is not None:
            A_     :				Tuple			      =	None
            A_     :				Tuple			      =	0
            for ix, logits_item in enumerate(a__       ):
               if self.num_labels == 1:
                  #  We are doing regression
                  A_     :				Union[str, Any]			      =	MSELoss()
                  A_     :				Any			      =	loss_fct(logits_item.view(-1       )      ,       labels.view(-1       )       )
               else:
                  A_     :				int			      =	CrossEntropyLoss()
                  A_     :				Optional[Any]			      =	loss_fct(logits_item.view(-1      ,       self.num_labels       )      ,       labels.view(-1       )       )
               if total_loss is None:
                  A_     :				str			      =	loss
               else:
                  total_loss += loss * (ix + 1)
               total_weights += ix + 1
            A_     :				int			      =	(total_loss / total_weights,) + outputs

         return outputs













import gc
import random
import unittest

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

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

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


enable_full_determinism()

class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase			,       _lowerCamelCase			,       unittest.TestCase     ):
      a						       =							StableDiffusionInstructPixaPixPipeline
      a						       =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
      a						       =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      a						       =							IMAGE_TO_IMAGE_IMAGE_PARAMS
      a						       =							IMAGE_TO_IMAGE_IMAGE_PARAMS
      def 		_lowerCamelCase      (		self       ):
         torch.manual_seed(0       )
         A_     :				Optional[Any]			      =	UNetaDConditionModel(
             block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=8      ,       out_channels=4      ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,       cross_attention_dim=32      ,       )
         A_     :				int			      =	PNDMScheduler(skip_prk_steps=a__       )
         torch.manual_seed(0       )
         A_     :				int			      =	AutoencoderKL(
             block_out_channels=[32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,       latent_channels=4      ,       )
         torch.manual_seed(0       )
         A_     :				List[Any]			      =	CLIPTextConfig(
             bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       )
         A_     :				List[Any]			      =	CLIPTextModel(a__       )
         A_     :				int			      =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

         A_     :				Tuple			      =	{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=0       ):
         A_     :				Tuple			      =	floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(a__       )       ).to(a__       )
         A_     :				List[Any]			      =	image.cpu().permute(0      ,       2      ,       3      ,       1       )[0]
         A_     :				Union[str, Any]			      =	Image.fromarray(np.uinta(a__       )       ).convert("""RGB"""       )
         if str(a__       ).startswith("""mps"""       ):
            A_     :				Dict			      =	torch.manual_seed(a__       )
         else:
            A_     :				List[Any]			      =	torch.Generator(device=a__       ).manual_seed(a__       )
         A_     :				Optional[int]			      =	{
             """prompt""": """A painting of a squirrel eating a burger""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """image_guidance_scale""": 1,
             """output_type""": """numpy""",
         }
         return inputs
      def 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Tuple			      =	self.get_dummy_components()
         A_     :				List[Any]			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				List[Any]			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				List[str]			      =	self.get_dummy_inputs(a__       )
         A_     :				str			      =	sd_pipe(**a__       ).images
         A_     :				Dict			      =	image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         A_     :				List[str]			      =	np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[Any]			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Tuple			      =	self.get_dummy_components()
         A_     :				Tuple			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				Tuple			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Optional[int]			      =	self.get_dummy_inputs(a__       )
         A_     :				Union[str, Any]			      =	"""french fries"""
         A_     :				str			      =	sd_pipe(**a__      ,       negative_prompt=a__       )
         A_     :				Any			      =	output.images
         A_     :				Optional[int]			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A_     :				List[str]			      =	np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Dict			      =	self.get_dummy_components()
         A_     :				Dict			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				str			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Dict			      =	self.get_dummy_inputs(a__       )
         A_     :				int			      =	[inputs["""prompt"""]] * 2

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

         A_     :				Optional[int]			      =	sd_pipe(**a__       ).images
         A_     :				Union[str, Any]			      =	image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         A_     :				Union[str, Any]			      =	np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Tuple			      =	self.get_dummy_components()
         A_     :				Dict			      =	EulerAncestralDiscreteScheduler(
             beta_start=0.0_0085      ,       beta_end=0.012      ,       beta_schedule="""scaled_linear"""       )
         A_     :				Union[str, Any]			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				Union[str, Any]			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Optional[int]			      =	self.get_dummy_inputs(a__       )
         A_     :				Union[str, Any]			      =	sd_pipe(**a__       ).images
         A_     :				Dict			      =	image[0, -3:, -3:, -1]

         A_     :				Any			      =	[round(a__      ,       4       ) for x in image_slice.flatten().tolist()]
         print(""",""".join([str(a__       ) for x in slice]       )       )

         assert image.shape == (1, 32, 32, 3)
         A_     :				Tuple			      =	np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[int]			      =	self.get_dummy_components()
         A_     :				List[Any]			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				str			      =	VaeImageProcessor(do_resize=a__      ,       do_normalize=a__       )
         A_     :				Union[str, Any]			      =	pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )

         A_     :				Optional[Any]			      =	pipe(**self.get_dummy_inputs_by_type(a__      ,       input_image_type="""pt"""       )       )[0]

         A_     :				Any			      =	components["""vae"""]
         A_     :				Any			      =	self.get_dummy_inputs_by_type(a__      ,       input_image_type="""pt"""       )

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

         A_     :				List[Any]			      =	pipe(**a__       )[0]

         A_     :				str			      =	np.abs(out - out_latents_inputs       ).max()
         self.assertLess(a__      ,       1E-4      ,       """passing latents as image input generate different result from passing image"""       )






@slow
@require_torch_gpu
class    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
      def 		_lowerCamelCase      (		self      ,       a__=0       ):
         A_     :				str			      =	torch.manual_seed(a__       )
         A_     :				Dict			      =	load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""       )
         A_     :				List[Any]			      =	{
             """prompt""": """turn him into a cyborg""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 3,
             """guidance_scale""": 7.5,
             """image_guidance_scale""": 1.0,
             """output_type""": """numpy""",
         }
         return inputs
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

         A_     :				Optional[int]			      =	self.get_inputs()
         A_     :				List[str]			      =	pipe(**a__       ).images
         A_     :				Union[str, Any]			      =	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         A_     :				Optional[Any]			      =	np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__       )
         A_     :				int			      =	LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

         A_     :				int			      =	self.get_inputs()
         A_     :				List[Any]			      =	pipe(**a__       ).images
         A_     :				List[Any]			      =	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         A_     :				Any			      =	np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__       )
         A_     :				Optional[int]			      =	DDIMScheduler.from_config(pipe.scheduler.config       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

         A_     :				str			      =	self.get_inputs()
         A_     :				List[str]			      =	pipe(**a__       ).images
         A_     :				Optional[int]			      =	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         A_     :				Optional[Any]			      =	np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	0

         def callback_fn(a__      ,       a__      ,       a__       ) -> None:
            A_     :				str			      =	True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
               A_     :				Optional[int]			      =	latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               A_     :				str			      =	latents[0, -3:, -3:, -1]
               A_     :				int			      =	np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]       )

               assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2
            elif step == 2:
               A_     :				Dict			      =	latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               A_     :				List[str]			      =	latents[0, -3:, -3:, -1]
               A_     :				Union[str, Any]			      =	np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]       )

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

         A_     :				Tuple			      =	False

         A_     :				Any			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__      ,       torch_dtype=torch.floataa       )
         A_     :				Optional[int]			      =	pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

         A_     :				Dict			      =	self.get_inputs()
         pipe(**a__      ,       callback=a__      ,       callback_steps=1       )
         assert callback_fn.has_been_called
         assert number_of_steps == 3
      def 		_lowerCamelCase      (		self       ):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         A_     :				Optional[int]			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__      ,       torch_dtype=torch.floataa       )
         A_     :				Optional[Any]			      =	pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing(1       )
         pipe.enable_sequential_cpu_offload()

         A_     :				List[Any]			      =	self.get_inputs()
         A_     :				int			      =	pipe(**a__       )

         A_     :				Dict			      =	torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9






      def 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         A_     :				Dict			      =	inputs["""image"""].resize((504, 504)       )

         A_     :				int			      =	"""timbrooks/instruct-pix2pix"""
         A_     :				List[Any]			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             a__      ,       safety_checker=a__      ,       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

         A_     :				Optional[int]			      =	pipe(**a__       )
         A_     :				Optional[Any]			      =	output.images[0]

         A_     :				str			      =	image[255:258, 383:386, -1]

         assert image.shape == (504, 504, 3)
         A_     :				Optional[Any]			      =	np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]       )

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






