






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 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					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  _lowercase  (      self  :						str ):
						torch.manual_seed(0 )
						snake_case__				:   Union[str, Any]     =						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							,	)
						snake_case__				:   Any     =						PNDMScheduler(skip_prk_steps=__A )
						torch.manual_seed(0 )
						snake_case__				:   str     =						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 )
						snake_case__				:   Tuple     =						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							,	)
						snake_case__				:   int     =						CLIPTextModel(__A )
						snake_case__				:   Union[str, Any]     =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

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



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Dict							,	__A  :						Optional[Any]=0 ):
						snake_case__				:   List[Any]     =						floats_tensor((1, 3, 3_2, 3_2)							,	rng=random.Random(__A ) ).to(__A )
						snake_case__				:   str     =						image.cpu().permute(0							,	2							,	3							,	1 )[0]
						snake_case__				:   int     =						Image.fromarray(np.uinta(__A ) ).convert("RGB" )
						if str(__A ).startswith("mps" ):
							snake_case__				:   List[Any]     =						torch.manual_seed(__A )
						else:
							snake_case__				:   List[Any]     =						torch.Generator(device=__A ).manual_seed(__A )
						snake_case__				:   Optional[Any]     =						{
						    "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  _lowercase  (      self  :						Dict ):
						snake_case__				:   Tuple     =						"cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__				:   int     =						self.get_dummy_components()
						snake_case__				:   List[Any]     =						StableDiffusionInstructPixaPixPipeline(**__A )
						snake_case__				:   int     =						sd_pipe.to(__A )
						sd_pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   Tuple     =						self.get_dummy_inputs(__A )
						snake_case__				:   List[str]     =						sd_pipe(**__A ).images
						snake_case__				:   Dict     =						image[0, -3:, -3:, -1]
						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__				:   int     =						np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )

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



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[Any]     =						"cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__				:   Optional[Any]     =						self.get_dummy_components()
						snake_case__				:   Dict     =						StableDiffusionInstructPixaPixPipeline(**__A )
						snake_case__				:   Optional[int]     =						sd_pipe.to(__A )
						sd_pipe.set_progress_bar_config(disable=__A )

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

						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__				:   Any     =						np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )

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



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

						snake_case__				:   str     =						self.get_dummy_inputs(__A )
						snake_case__				:   Optional[Any]     =						[inputs["prompt"]] * 2

						snake_case__				:   List[Any]     =						np.array(inputs["image"] ).astype(np.floataa ) / 2_5_5.0
						snake_case__				:   Tuple     =						torch.from_numpy(__A ).unsqueeze(0 ).to(__A )
						snake_case__				:   List[str]     =						image / 2 + 0.5
						snake_case__				:   List[Any]     =						image.permute(0							,	3							,	1							,	2 )
						snake_case__				:   str     =						image.repeat(2							,	1							,	1							,	1 )

						snake_case__				:   str     =						sd_pipe(**__A ).images
						snake_case__				:   List[Any]     =						image[-1, -3:, -3:, -1]

						assert image.shape == (2, 3_2, 3_2, 3)
						snake_case__				:   Union[str, Any]     =						np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )

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



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   List[str]     =						"cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__				:   Any     =						self.get_dummy_components()
						snake_case__				:   Optional[Any]     =						EulerAncestralDiscreteScheduler(
						    beta_start=0.0_0_0_8_5							,	beta_end=0.0_1_2							,	beta_schedule="scaled_linear" )
						snake_case__				:   Optional[int]     =						StableDiffusionInstructPixaPixPipeline(**__A )
						snake_case__				:   Tuple     =						sd_pipe.to(__A )
						sd_pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   int     =						self.get_dummy_inputs(__A )
						snake_case__				:   Dict     =						sd_pipe(**__A ).images
						snake_case__				:   int     =						image[0, -3:, -3:, -1]

						snake_case__				:   Tuple     =						[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)
						snake_case__				:   Union[str, Any]     =						np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )

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



					def  _lowercase  (      self  :						Any ):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






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

						snake_case__				:   Tuple     =						pipe(**self.get_dummy_inputs_by_type(__A							,	input_image_type="pt" ) )[0]

						snake_case__				:   List[str]     =						components["vae"]
						snake_case__				:   List[str]     =						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():
								snake_case__				:   int     =						vae.encode(inputs[image_param] ).latent_dist.mode()

						snake_case__				:   List[str]     =						pipe(**__A )[0]

						snake_case__				:   Optional[Any]     =						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 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Optional[Any] ):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



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

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__				:   Union[str, Any]     =						np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )

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



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   List[str]     =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,	safety_checker=__A )
						snake_case__				:   Union[str, Any]     =						LMSDiscreteScheduler.from_config(pipe.scheduler.config )
						pipe.to(__A )
						pipe.set_progress_bar_config(disable=__A )
						pipe.enable_attention_slicing()

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__				:   Union[str, Any]     =						np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )

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



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   Dict     =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,	safety_checker=__A )
						snake_case__				:   Dict     =						DDIMScheduler.from_config(pipe.scheduler.config )
						pipe.to(__A )
						pipe.set_progress_bar_config(disable=__A )
						pipe.enable_attention_slicing()

						snake_case__				:   List[str]     =						self.get_inputs()
						snake_case__				:   List[str]     =						pipe(**__A ).images
						snake_case__				:   Optional[Any]     =						image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__				:   Optional[int]     =						np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )

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



					def  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   Optional[Any]     =						0

						def callback_fn(__A  :						int							,	__A  :						int							,	__A  :						torch.FloatTensor ) -> None:
							snake_case__				:   Any     =						True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
								snake_case__				:   str     =						latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 6_4, 6_4)
								snake_case__				:   List[str]     =						latents[0, -3:, -3:, -1]
								snake_case__				:   int     =						np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )

								assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
							elif step == 2:
								snake_case__				:   Tuple     =						latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 6_4, 6_4)
								snake_case__				:   List[str]     =						latents[0, -3:, -3:, -1]
								snake_case__				:   List[str]     =						np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )

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

						snake_case__				:   Union[str, Any]     =						False

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

						snake_case__				:   Optional[int]     =						self.get_inputs()
						pipe(**__A							,	callback=__A							,	callback_steps=1 )
						assert callback_fn.has_been_called
						assert number_of_steps == 3



					def  _lowercase  (      self  :						Tuple ):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

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

						snake_case__				:   Optional[Any]     =						self.get_inputs()
						snake_case__				:   Dict     =						pipe(**__A )

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






					def  _lowercase  (      self  :						int ):
						snake_case__				:   int     =						self.get_inputs()
						# resize to resolution that is divisible by 8 but not 16 or 32
						snake_case__				:   Optional[Any]     =						inputs["image"].resize((5_0_4, 5_0_4) )

						snake_case__				:   List[str]     =						"timbrooks/instruct-pix2pix"
						snake_case__				:   Union[str, Any]     =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    __A							,	safety_checker=__A							,	)
						pipe.to(__A )
						pipe.set_progress_bar_config(disable=__A )
						pipe.enable_attention_slicing()

						snake_case__				:   Union[str, Any]     =						pipe(**__A )
						snake_case__				:   Union[str, Any]     =						output.images[0]

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

						assert image.shape == (5_0_4, 5_0_4, 3)
						snake_case__				:   Optional[int]     =						np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )

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











# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__lowerCamelCase      :    Union[str, Any]   		=   """2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
	raise ImportWarning(
	    """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
	)

if version.parse(pyarrow.__version__).major < 8:
	raise ImportWarning(
	    """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
	    """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
	)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__lowerCamelCase      :    List[Any]   		=   concatenate_datasets
__lowerCamelCase      :    List[str]   		=   DownloadConfig
__lowerCamelCase      :    Union[str, Any]   		=   DownloadManager
__lowerCamelCase      :    str   		=   DownloadMode
__lowerCamelCase      :    Union[str, Any]   		=   DownloadConfig
__lowerCamelCase      :    List[str]   		=   DownloadMode
__lowerCamelCase      :    Dict   		=   DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager



