




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     a__					(      snake_case__				,       snake_case__				,       snake_case__				,       unittest.TestCase   ):
						_a							:					List[str]      =					StableDiffusionInstructPixaPixPipeline
						_a							:					Tuple      =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
						_a							:					Any      =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_a							:					Union[str, Any]      =					IMAGE_TO_IMAGE_IMAGE_PARAMS
						_a							:					Optional[Any]      =					IMAGE_TO_IMAGE_IMAGE_PARAMS






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											torch.manual_seed(0    )
											__lowerCAmelCase            = UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)	,    layers_per_block=2	,    sample_size=3_2	,    in_channels=8	,    out_channels=4	,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,    cross_attention_dim=3_2	,    )
											__lowerCAmelCase            = PNDMScheduler(skip_prk_steps=_A    )
											torch.manual_seed(0    )
											__lowerCAmelCase            = AutoencoderKL(
											    block_out_channels=[3_2, 6_4]	,    in_channels=3	,    out_channels=3	,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,    latent_channels=4	,    )
											torch.manual_seed(0    )
											__lowerCAmelCase            = CLIPTextConfig(
											    bos_token_id=0	,    eos_token_id=2	,    hidden_size=3_2	,    intermediate_size=3_7	,    layer_norm_eps=1E-0_5	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1_0_0_0	,    )
											__lowerCAmelCase            = CLIPTextModel(_A    )
											__lowerCAmelCase            = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A=0    ):




											"""simple docstring"""



											__lowerCAmelCase            = floats_tensor((1, 3, 3_2, 3_2)	,    rng=random.Random(_A    )    ).to(_A    )
											__lowerCAmelCase            = image.cpu().permute(0	,    2	,    3	,    1    )[0]
											__lowerCAmelCase            = Image.fromarray(np.uinta(_A    )    ).convert("RGB"    )
											if str(_A    ).startswith("mps"    ):
																__lowerCAmelCase            = torch.manual_seed(_A    )
											else:
																__lowerCAmelCase            = torch.Generator(device=_A    ).manual_seed(_A    )
											__lowerCAmelCase            = {
											    "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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = "cpu"  # ensure determinism for the device-dependent torch.Generator
											__lowerCAmelCase            = self.get_dummy_components()
											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline(**_A    )
											__lowerCAmelCase            = sd_pipe.to(_A    )
											sd_pipe.set_progress_bar_config(disable=_A    )

											__lowerCAmelCase            = self.get_dummy_inputs(_A    )
											__lowerCAmelCase            = sd_pipe(**_A    ).images
											__lowerCAmelCase            = image[0, -3:, -3:, -1]
											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase            = np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = "cpu"  # ensure determinism for the device-dependent torch.Generator
											__lowerCAmelCase            = self.get_dummy_components()
											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline(**_A    )
											__lowerCAmelCase            = sd_pipe.to(_A    )
											sd_pipe.set_progress_bar_config(disable=_A    )

											__lowerCAmelCase            = self.get_dummy_inputs(_A    )
											__lowerCAmelCase            = "french fries"
											__lowerCAmelCase            = sd_pipe(**_A	,    negative_prompt=_A    )
											__lowerCAmelCase            = output.images
											__lowerCAmelCase            = image[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase            = np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = "cpu"  # ensure determinism for the device-dependent torch.Generator
											__lowerCAmelCase            = self.get_dummy_components()
											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline(**_A    )
											__lowerCAmelCase            = sd_pipe.to(_A    )
											sd_pipe.set_progress_bar_config(disable=_A    )

											__lowerCAmelCase            = self.get_dummy_inputs(_A    )
											__lowerCAmelCase            = [inputs["prompt"]] * 2

											__lowerCAmelCase            = np.array(inputs["image"]    ).astype(np.floataa    ) / 2_55.0
											__lowerCAmelCase            = torch.from_numpy(_A    ).unsqueeze(0    ).to(_A    )
											__lowerCAmelCase            = image / 2 + 0.5
											__lowerCAmelCase            = image.permute(0	,    3	,    1	,    2    )
											__lowerCAmelCase            = image.repeat(2	,    1	,    1	,    1    )

											__lowerCAmelCase            = sd_pipe(**_A    ).images
											__lowerCAmelCase            = image[-1, -3:, -3:, -1]

											assert image.shape == (2, 3_2, 3_2, 3)
											__lowerCAmelCase            = np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = "cpu"  # ensure determinism for the device-dependent torch.Generator
											__lowerCAmelCase            = self.get_dummy_components()
											__lowerCAmelCase            = EulerAncestralDiscreteScheduler(
											    beta_start=0.0_00_85	,    beta_end=0.0_12	,    beta_schedule="scaled_linear"    )
											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline(**_A    )
											__lowerCAmelCase            = sd_pipe.to(_A    )
											sd_pipe.set_progress_bar_config(disable=_A    )

											__lowerCAmelCase            = self.get_dummy_inputs(_A    )
											__lowerCAmelCase            = sd_pipe(**_A    ).images
											__lowerCAmelCase            = image[0, -3:, -3:, -1]

											__lowerCAmelCase            = [round(_A	,    4    ) for x in image_slice.flatten().tolist()]
											print(",".join([str(_A    ) for x in slice]    )    )

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase            = np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											super().test_inference_batch_single_identical(expected_max_diff=3E-3    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.get_dummy_components()
											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline(**_A    )
											__lowerCAmelCase            = VaeImageProcessor(do_resize=_A	,    do_normalize=_A    )
											__lowerCAmelCase            = pipe.to(_A    )
											pipe.set_progress_bar_config(disable=_A    )

											__lowerCAmelCase            = pipe(**self.get_dummy_inputs_by_type(_A	,    input_image_type="pt"    )    )[0]

											__lowerCAmelCase            = components["vae"]
											__lowerCAmelCase            = 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():
																					__lowerCAmelCase            = vae.encode(inputs[image_param]    ).latent_dist.mode()

											__lowerCAmelCase            = pipe(**_A    )[0]

											__lowerCAmelCase            = 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     a__					(      unittest.TestCase   ):






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






						def        __SCREAMING_SNAKE_CASE(     self	,    _A=0    ):




											"""simple docstring"""



											__lowerCAmelCase            = torch.manual_seed(_A    )
											__lowerCAmelCase            = load_image(
											    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"    )
											__lowerCAmelCase            = {
											    "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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    "timbrooks/instruct-pix2pix"	,    safety_checker=_A    )
											pipe.to(_A    )
											pipe.set_progress_bar_config(disable=_A    )
											pipe.enable_attention_slicing()

											__lowerCAmelCase            = self.get_inputs()
											__lowerCAmelCase            = pipe(**_A    ).images
											__lowerCAmelCase            = image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowerCAmelCase            = np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    "timbrooks/instruct-pix2pix"	,    safety_checker=_A    )
											__lowerCAmelCase            = LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
											pipe.to(_A    )
											pipe.set_progress_bar_config(disable=_A    )
											pipe.enable_attention_slicing()

											__lowerCAmelCase            = self.get_inputs()
											__lowerCAmelCase            = pipe(**_A    ).images
											__lowerCAmelCase            = image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowerCAmelCase            = np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    "timbrooks/instruct-pix2pix"	,    safety_checker=_A    )
											__lowerCAmelCase            = DDIMScheduler.from_config(pipe.scheduler.config    )
											pipe.to(_A    )
											pipe.set_progress_bar_config(disable=_A    )
											pipe.enable_attention_slicing()

											__lowerCAmelCase            = self.get_inputs()
											__lowerCAmelCase            = pipe(**_A    ).images
											__lowerCAmelCase            = image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowerCAmelCase            = np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = 0

											def callback_fn(_A	,    _A	,    _A    ) -> None:
																__lowerCAmelCase            = True
																nonlocal number_of_steps
																number_of_steps += 1
																if step == 1:
																					__lowerCAmelCase            = latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 6_4, 6_4)
																					__lowerCAmelCase            = latents[0, -3:, -3:, -1]
																					__lowerCAmelCase            = np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]    )

																					assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5E-2
																elif step == 2:
																					__lowerCAmelCase            = latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 6_4, 6_4)
																					__lowerCAmelCase            = latents[0, -3:, -3:, -1]
																					__lowerCAmelCase            = np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]    )

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

											__lowerCAmelCase            = False

											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    "timbrooks/instruct-pix2pix"	,    safety_checker=_A	,    torch_dtype=torch.floataa    )
											__lowerCAmelCase            = pipe.to(_A    )
											pipe.set_progress_bar_config(disable=_A    )
											pipe.enable_attention_slicing()

											__lowerCAmelCase            = self.get_inputs()
											pipe(**_A	,    callback=_A	,    callback_steps=1    )
											assert callback_fn.has_been_called
											assert number_of_steps == 3






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    "timbrooks/instruct-pix2pix"	,    safety_checker=_A	,    torch_dtype=torch.floataa    )
											__lowerCAmelCase            = pipe.to(_A    )
											pipe.set_progress_bar_config(disable=_A    )
											pipe.enable_attention_slicing(1    )
											pipe.enable_sequential_cpu_offload()

											__lowerCAmelCase            = self.get_inputs()
											__lowerCAmelCase            = pipe(**_A    )

											__lowerCAmelCase            = torch.cuda.max_memory_allocated()
											# make sure that less than 2.2 GB is allocated
											assert mem_bytes < 2.2 * 1_0**9






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.get_inputs()
											# resize to resolution that is divisible by 8 but not 16 or 32
											__lowerCAmelCase            = inputs["image"].resize((5_0_4, 5_0_4)    )

											__lowerCAmelCase            = "timbrooks/instruct-pix2pix"
											__lowerCAmelCase            = StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    _A	,    safety_checker=_A	,    )
											pipe.to(_A    )
											pipe.set_progress_bar_config(disable=_A    )
											pipe.enable_attention_slicing()

											__lowerCAmelCase            = pipe(**_A    )
											__lowerCAmelCase            = output.images[0]

											__lowerCAmelCase            = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

											assert image.shape == (5_0_4, 5_0_4, 3)
											__lowerCAmelCase            = np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]    )

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase__					    =    {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__					    =    [
					    """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CTRLForSequenceClassification""",
					    """CTRLLMHeadModel""",
					    """CTRLModel""",
					    """CTRLPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__					    =    [
					    """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFCTRLForSequenceClassification""",
					    """TFCTRLLMHeadModel""",
					    """TFCTRLModel""",
					    """TFCTRLPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
					from .tokenization_ctrl import CTRLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ctrl import (
										    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CTRLForSequenceClassification,
										    CTRLLMHeadModel,
										    CTRLModel,
										    CTRLPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ctrl import (
										    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCTRLForSequenceClassification,
										    TFCTRLLMHeadModel,
										    TFCTRLModel,
										    TFCTRLPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase__					    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




