
'''simple docstring'''
from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    for param, grad_param in zip(model_a.parameters(),		model_b.parameters()       ):
        if not param.requires_grad:
            continue
        if not did_step:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad,		grad_param.grad       ) is False
            ), f'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
        else:
            # Grads should be in sync
            assert (
                torch.allclose(param.grad,		grad_param.grad       ) is True
            ), f'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=True       ):
    """simple docstring"""






    model.train()
    snake_case_ :    Any     			=						model(UpperCamelCase__       )
    snake_case_ :    str     			=						F.mse_loss(UpperCamelCase__,		target.to(output.device       )       )
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(UpperCamelCase__       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    set_seed(4_2       )
    snake_case_ :    str     			=						RegressionModel()
    snake_case_ :    List[Any]     			=						deepcopy(UpperCamelCase__       )
    snake_case_ :    Any     			=						RegressionDataset(length=8_0       )
    snake_case_ :    Optional[Any]     			=						DataLoader(UpperCamelCase__,		batch_size=1_6       )
    model.to(accelerator.device       )
    if sched:
        snake_case_ :    Tuple     			=						AdamW(params=model.parameters(),		lr=1E-3       )
        snake_case_ :    Optional[Any]     			=						AdamW(params=ddp_model.parameters(),		lr=1E-3       )
        snake_case_ :    str     			=						LambdaLR(UpperCamelCase__,		lr_lambda=lambda __SCREAMING_SNAKE_CASE       : epoch**0.65       )
        snake_case_ :    Union[str, Any]     			=						LambdaLR(UpperCamelCase__,		lr_lambda=lambda __SCREAMING_SNAKE_CASE       : epoch**0.65       )
    # Make a copy of `model`
    if sched:
        snake_case_			, snake_case_			, snake_case_			, snake_case_ :    List[str]     			=						accelerator.prepare(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )
    else:
        snake_case_			, snake_case_ :    Tuple     			=						accelerator.prepare(UpperCamelCase__,		UpperCamelCase__       )
    if sched:
        return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
    return model, ddp_model, dataloader

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_			, snake_case_			, snake_case_ :    Optional[int]     			=						get_training_setup(UpperCamelCase__       )
    # Use a single batch
    snake_case_			, snake_case_ :    Dict     			=						next(iter(UpperCamelCase__       )       ).values()
    for iteration in range(3       ):
        # Gather the distributed inputs and targs for the base model
        snake_case_			, snake_case_ :    Any     			=						accelerator.gather((ddp_input, ddp_target)       )
        snake_case_			, snake_case_ :    Tuple     			=						input.to(accelerator.device       ), target.to(accelerator.device       )
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(UpperCamelCase__       ):
                step_model(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )
        else:
            # Sync grads
            step_model(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )

        # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
        check_model_parameters(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )
        for param, ddp_param in zip(model.parameters(),		ddp_model.parameters()       ):
            if not param.requires_grad:
                continue
            assert torch.allclose(
                param.grad,		ddp_param.grad       ), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_3_3_7 + iteration       )
        snake_case_ :    Optional[Any]     			=						ddp_input[torch.randperm(len(UpperCamelCase__       )       )]

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_			, snake_case_			, snake_case_ :    Any     			=						get_training_setup(UpperCamelCase__       )
    # Use a single batch
    snake_case_			, snake_case_ :    List[str]     			=						next(iter(UpperCamelCase__       )       ).values()
    for iteration in range(3       ):
        # Gather the distributed inputs and targs for the base model
        snake_case_			, snake_case_ :    Optional[int]     			=						accelerator.gather((ddp_input, ddp_target)       )
        snake_case_			, snake_case_ :    List[Any]     			=						input.to(accelerator.device       ), target.to(accelerator.device       )
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(UpperCamelCase__       ):
                step_model(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )
        else:
            # Sync grads
            step_model(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters(),		ddp_model.parameters()       ):
            if not param.requires_grad:
                continue
            if iteration % 2 == 0:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad,		ddp_param.grad       ) is False
                ), f'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
            else:
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad,		ddp_param.grad       ) is True
                ), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_3_3_7 + iteration       )
        snake_case_ :    str     			=						ddp_input[torch.randperm(len(UpperCamelCase__       )       )]

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE=False,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    snake_case_ :    Tuple     			=						Accelerator(
        split_batches=UpperCamelCase__,		dispatch_batches=UpperCamelCase__,		gradient_accumulation_steps=2       )
    # Test that context manager behaves properly
    snake_case_			, snake_case_			, snake_case_ :    Dict     			=						get_training_setup(UpperCamelCase__       )
    for iteration, batch in enumerate(UpperCamelCase__       ):
        snake_case_			, snake_case_ :    Any     			=						batch.values()
        # Gather the distributed inputs and targs for the base model
        snake_case_			, snake_case_ :    str     			=						accelerator.gather((ddp_input, ddp_target)       )
        snake_case_			, snake_case_ :    int     			=						input.to(accelerator.device       ), target.to(accelerator.device       )
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )
        # Do "gradient accumulation" (noop)
        with accelerator.accumulate(UpperCamelCase__       ):
            step_model(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters(),		ddp_model.parameters()       ):
            if not param.requires_grad:
                continue
            if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase__       ) - 1):
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad,		ddp_param.grad       ) is True
                ), f'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
            else:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad,		ddp_param.grad       ) is False
                ), f'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_3_3_7 + iteration       )
        snake_case_ :    Dict     			=						ddp_input[torch.randperm(len(UpperCamelCase__       )       )]
    GradientState._reset_state()

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE=False,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    snake_case_ :    Dict     			=						Accelerator(
        split_batches=UpperCamelCase__,		dispatch_batches=UpperCamelCase__,		gradient_accumulation_steps=2       )
    # Test that context manager behaves properly
    snake_case_			, snake_case_			, snake_case_			, snake_case_			, snake_case_			, snake_case_			, snake_case_ :    int     			=						get_training_setup(UpperCamelCase__,		UpperCamelCase__       )
    for iteration, batch in enumerate(UpperCamelCase__       ):
        snake_case_			, snake_case_ :    List[str]     			=						batch.values()
        # Gather the distributed inputs and targs for the base model
        snake_case_			, snake_case_ :    Any     			=						accelerator.gather((ddp_input, ddp_target)       )
        snake_case_			, snake_case_ :    str     			=						input.to(accelerator.device       ), target.to(accelerator.device       )
        # Perform our initial ground truth step in non "DDP"
        model.train()
        ddp_model.train()
        step_model(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )
        opt.step()

        if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__       )):
            if split_batches:
                sched.step()
            else:
                for _ in range(accelerator.num_processes       ):
                    sched.step()
        opt.zero_grad()
        # Perform gradient accumulation under wrapper
        with accelerator.accumulate(UpperCamelCase__       ):
            step_model(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )
            ddp_opt.step()
            ddp_sched.step()
            ddp_opt.zero_grad()

        # Learning rates should be the same
        assert (
            opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
        ), f'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
        snake_case_ :    List[str]     			=						(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__       ))
        if accelerator.num_processes > 1:
            check_model_parameters(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__       )
        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_3_3_7 + iteration       )
    GradientState._reset_state()

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    List[str]     			=						Accelerator()

    snake_case_ :    Optional[int]     			=						RegressionDataset(length=8_0       )
    snake_case_ :    Optional[int]     			=						DataLoader(UpperCamelCase__,		batch_size=1_6       )
    snake_case_ :    List[str]     			=						RegressionDataset(length=9_6       )
    snake_case_ :    str     			=						DataLoader(UpperCamelCase__,		batch_size=1_6       )
    snake_case_			, snake_case_ :    Dict     			=						accelerator.prepare(UpperCamelCase__,		UpperCamelCase__       )
    assert accelerator.gradient_state.active_dataloader is None
    for iteration, _ in enumerate(UpperCamelCase__       ):
        assert id(accelerator.gradient_state.active_dataloader       ) == id(UpperCamelCase__       )
        if iteration < len(UpperCamelCase__       ) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
            if iteration == 1:
                for batch_num, _ in enumerate(UpperCamelCase__       ):
                    assert id(accelerator.gradient_state.active_dataloader       ) == id(UpperCamelCase__       )
                    if batch_num < len(UpperCamelCase__       ) - 1:
                        assert not accelerator.gradient_state.end_of_dataloader
                    else:
                        assert accelerator.gradient_state.end_of_dataloader
        else:
            assert accelerator.gradient_state.end_of_dataloader
    assert accelerator.gradient_state.active_dataloader is None

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Dict     			=						Accelerator()
    snake_case_ :    int     			=						accelerator.state
    if state.local_process_index == 0:
        print("**Test `accumulate` gradient accumulation with dataloader break**"       )
    test_dataloader_break()
    if state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print("**Test NOOP `no_sync` context manager**"       )
        test_noop_sync(UpperCamelCase__       )
    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
        if state.local_process_index == 0:
            print("**Test Distributed `no_sync` context manager**"       )
        test_distributed_sync(UpperCamelCase__       )
    if state.distributed_type == DistributedType.MULTI_GPU:
        for split_batch in [True, False]:
            for dispatch_batches in [True, False]:
                if state.local_process_index == 0:
                    print(
                        "**Test `accumulate` gradient accumulation, ",		f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**',		)
                test_gradient_accumulation(UpperCamelCase__,		UpperCamelCase__       )

    # Currently will break on torch 2.0 +, need to investigate why
    if is_torch_version("<",		"2.0"       ) or state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print(
                "**Test `accumulate` gradient accumulation with optimizer and scheduler, ",		"`split_batches=False`, `dispatch_batches=False`**",		)
        test_gradient_accumulation_with_opt_and_scheduler()
        if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                for dispatch_batches in [True, False]:
                    if not split_batch and not dispatch_batches:
                        continue
                    if state.local_process_index == 0:
                        print(
                            "**Test `accumulate` gradient accumulation with optimizer and scheduler, ",		f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**',		)
                    test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase__,		UpperCamelCase__       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    main()


if __name__ == "__main__":
  main()






'''simple docstring'''
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_				(	snake_case__    ,						snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						StableDiffusionInstructPixaPixPipeline
 UpperCAmelCase_		    =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
 UpperCAmelCase_		    =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase_		    =						IMAGE_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase_		    =						IMAGE_TO_IMAGE_IMAGE_PARAMS





 def        snake_case__		(    self):
     torch.manual_seed(0)
     snake_case_ :    List[str]     			=						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 ,       )
     snake_case_ :    Any     			=						PNDMScheduler(skip_prk_steps=lowercase_)
     torch.manual_seed(0)
     snake_case_ :    Tuple     			=						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)
     snake_case_ :    Union[str, 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=10_00 ,       )
     snake_case_ :    Union[str, Any]     			=						CLIPTextModel(lowercase_)
     snake_case_ :    Optional[Any]     			=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

     snake_case_ :    str     			=						{
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=0):
     snake_case_ :    int     			=						floats_tensor((1, 3, 32, 32) ,       rng=random.Random(lowercase_)).to(lowercase_)
     snake_case_ :    Any     			=						image.cpu().permute(0 ,       2 ,       3 ,       1)[0]
     snake_case_ :    str     			=						Image.fromarray(np.uinta(lowercase_)).convert("RGB")
     if str(lowercase_).startswith("mps"):
         snake_case_ :    Union[str, Any]     			=						torch.manual_seed(lowercase_)
     else:
         snake_case_ :    Any     			=						torch.Generator(device=lowercase_).manual_seed(lowercase_)
     snake_case_ :    List[str]     			=						{
         "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        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Tuple     			=						self.get_dummy_components()
     snake_case_ :    Any     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    Union[str, Any]     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    Optional[Any]     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    List[Any]     			=						sd_pipe(**lowercase_).images
     snake_case_ :    List[Any]     			=						image[0, -3:, -3:, -1]
     assert image.shape == (1, 32, 32, 3)
     snake_case_ :    Optional[int]     			=						np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Union[str, Any]     			=						self.get_dummy_components()
     snake_case_ :    str     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    List[str]     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

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

     assert image.shape == (1, 32, 32, 3)
     snake_case_ :    Tuple     			=						np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Union[str, Any]     			=						self.get_dummy_components()
     snake_case_ :    Tuple     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    Tuple     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    List[Any]     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    str     			=						[inputs["prompt"]] * 2

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

     snake_case_ :    List[str]     			=						sd_pipe(**lowercase_).images
     snake_case_ :    Dict     			=						image[-1, -3:, -3:, -1]

     assert image.shape == (2, 32, 32, 3)
     snake_case_ :    List[Any]     			=						np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Tuple     			=						self.get_dummy_components()
     snake_case_ :    Tuple     			=						EulerAncestralDiscreteScheduler(
         beta_start=0.00_085 ,       beta_end=0.012 ,       beta_schedule="scaled_linear")
     snake_case_ :    Dict     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    Union[str, Any]     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    Tuple     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    Optional[int]     			=						sd_pipe(**lowercase_).images
     snake_case_ :    Union[str, Any]     			=						image[0, -3:, -3:, -1]

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

     assert image.shape == (1, 32, 32, 3)
     snake_case_ :    Tuple     			=						np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3





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





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.get_dummy_components()
     snake_case_ :    Union[str, Any]     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    List[str]     			=						VaeImageProcessor(do_resize=lowercase_ ,       do_normalize=lowercase_)
     snake_case_ :    List[Any]     			=						pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    str     			=						pipe(**self.get_dummy_inputs_by_type(lowercase_ ,       input_image_type="pt"))[0]

     snake_case_ :    str     			=						components["vae"]
     snake_case_ :    str     			=						self.get_dummy_inputs_by_type(lowercase_ ,       input_image_type="pt")

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

     snake_case_ :    List[str]     			=						pipe(**lowercase_)[0]

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






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





 def        snake_case__		(    self):
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





 def        snake_case__		(    self ,       lowercase_=0):
     snake_case_ :    int     			=						torch.manual_seed(lowercase_)
     snake_case_ :    Union[str, Any]     			=						load_image(
         "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg")
     snake_case_ :    Tuple     			=						{
         "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        snake_case__		(    self):
     snake_case_ :    str     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    int     			=						self.get_inputs()
     snake_case_ :    Dict     			=						pipe(**lowercase_).images
     snake_case_ :    str     			=						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    Dict     			=						np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555])

     assert np.abs(expected_slice - image_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    int     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_)
     snake_case_ :    Dict     			=						LMSDiscreteScheduler.from_config(pipe.scheduler.config)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    int     			=						self.get_inputs()
     snake_case_ :    Dict     			=						pipe(**lowercase_).images
     snake_case_ :    str     			=						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    str     			=						np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301])

     assert np.abs(expected_slice - image_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_)
     snake_case_ :    List[Any]     			=						DDIMScheduler.from_config(pipe.scheduler.config)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    str     			=						self.get_inputs()
     snake_case_ :    Optional[Any]     			=						pipe(**lowercase_).images
     snake_case_ :    Dict     			=						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    List[str]     			=						np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753])

     assert np.abs(expected_slice - image_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						0

     def callback_fn(lowercase_ ,       lowercase_ ,       lowercase_) -> None:
         snake_case_ :    Optional[Any]     			=						True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 1:
             snake_case_ :    Optional[Any]     			=						latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             snake_case_ :    Optional[int]     			=						latents[0, -3:, -3:, -1]
             snake_case_ :    Optional[int]     			=						np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983])

             assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2
         elif step == 2:
             snake_case_ :    List[str]     			=						latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             snake_case_ :    Union[str, Any]     			=						latents[0, -3:, -3:, -1]
             snake_case_ :    Any     			=						np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115])

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

     snake_case_ :    Optional[int]     			=						False

     snake_case_ :    Union[str, Any]     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_ ,       torch_dtype=torch.floataa)
     snake_case_ :    Dict     			=						pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    Any     			=						self.get_inputs()
     pipe(**lowercase_ ,       callback=lowercase_ ,       callback_steps=1)
     assert callback_fn.has_been_called
     assert number_of_steps == 3





 def        snake_case__		(    self):
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     snake_case_ :    int     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_ ,       torch_dtype=torch.floataa)
     snake_case_ :    str     			=						pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing(1)
     pipe.enable_sequential_cpu_offload()

     snake_case_ :    Dict     			=						self.get_inputs()
     snake_case_ :    int     			=						pipe(**lowercase_)

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





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.get_inputs()
     # resize to resolution that is divisible by 8 but not 16 or 32
     snake_case_ :    Union[str, Any]     			=						inputs["image"].resize((5_04, 5_04))

     snake_case_ :    str     			=						"timbrooks/instruct-pix2pix"
     snake_case_ :    Any     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         lowercase_ ,       safety_checker=lowercase_ ,       )
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    Any     			=						pipe(**lowercase_)
     snake_case_ :    int     			=						output.images[0]

     snake_case_ :    Tuple     			=						image[2_55:2_58, 3_83:3_86, -1]

     assert image.shape == (5_04, 5_04, 3)
     snake_case_ :    Optional[int]     			=						np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590])

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




