
import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset





@pytest.mark.parametrize("dataset_size"	, [None, 4_0_0 * 2**2_0, 6_0_0 * 2**2_0]    )
@pytest.mark.parametrize("input_in_memory_max_size"	, ["default", 0, 1_0_0 * 2**2_0, 9_0_0 * 2**2_0]    )
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				if input_in_memory_max_size != "default":
								monkeypatch.setattr(datasets.config	, "IN_MEMORY_MAX_SIZE"	, lowerCamelCase__    )
				lowerCamelCase_						=			datasets.config.IN_MEMORY_MAX_SIZE
				if input_in_memory_max_size == "default":
								assert in_memory_max_size == 0
				else:
								assert in_memory_max_size == input_in_memory_max_size
				if dataset_size and in_memory_max_size:
								lowerCamelCase_						=			dataset_size < in_memory_max_size
				else:
								lowerCamelCase_						=			False
				lowerCamelCase_						=			is_small_dataset(lowerCamelCase__    )
				assert result == expected




from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


__A				=logging.get_logger(__name__)  # pylint: disable=invalid-name




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[Any]:
					super().__init__()

					if hasattr(scheduler.config						,  "steps_offset" ) and scheduler.config.steps_offset != 1:
									lowerCamelCase_						=			(
									    f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
									    f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
									    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
									    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
									    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
									    " file"
									)
									deprecate("steps_offset!=1"						,  "1.0.0"						,  lowercase						,  standard_warn=lowercase )
									lowerCamelCase_						=			dict(scheduler.config )
									lowerCamelCase_						=			1
									lowerCamelCase_						=			FrozenDict(lowercase )

					if hasattr(scheduler.config						,  "skip_prk_steps" ) and scheduler.config.skip_prk_steps is False:
									lowerCamelCase_						=			(
									    f'The configuration file of this scheduler: {scheduler} has not set the configuration'
									    " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
									    " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
									    " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
									    " Hub, it would be very nice if you could open a Pull request for the"
									    " `scheduler/scheduler_config.json` file"
									)
									deprecate("skip_prk_steps not set"						,  "1.0.0"						,  lowercase						,  standard_warn=lowercase )
									lowerCamelCase_						=			dict(scheduler.config )
									lowerCamelCase_						=			True
									lowerCamelCase_						=			FrozenDict(lowercase )

					if safety_checker is None:
									logger.warning(
									    f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
									    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
									    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
									    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
									    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
									    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." )

					self.register_modules(
					    segmentation_model=lowercase						,  segmentation_processor=lowercase						,  vae=lowercase						,  text_encoder=lowercase						,  tokenizer=lowercase						,  unet=lowercase						,  scheduler=lowercase						,  safety_checker=lowercase						,  feature_extractor=lowercase						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase = "auto" )				->       Tuple:
					if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									lowerCamelCase_						=			self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					self.enable_attention_slicing(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError("Please install accelerate via `pip install accelerate`" )

					lowerCamelCase_						=			torch.device("cuda" )

					for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
									if cpu_offloaded_model is not None:
													cpu_offload(lowercase						,  lowercase )



	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					if self.device != torch.device("meta" ) or not hasattr(self.unet						,  "_hf_hook" ):
									return self.device
					for module in self.unet.modules():
									if (
									    hasattr(lowercase						,  "_hf_hook" )
									    and hasattr(module._hf_hook						,  "execution_device" )
									    and module._hf_hook.execution_device is not None
									):
													return torch.device(module._hf_hook.execution_device )
					return self.device



	@torch.no_grad()
	def __call__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase = 512						,  lowercase = 512						,  lowercase = 50						,  lowercase = 7.5						,  lowercase = None						,  lowercase = 1						,  lowercase = 0.0						,  lowercase = None						,  lowercase = None						,  lowercase = "pil"						,  lowercase = True						,  lowercase = None						,  lowercase = 1						,  **lowercase						,  )				->       int:
					lowerCamelCase_						=			self.segmentation_processor(
					    text=[text]						,  images=[image]						,  padding="max_length"						,  return_tensors="pt" ).to(self.device )
					lowerCamelCase_						=			self.segmentation_model(**lowercase )
					lowerCamelCase_						=			torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
					lowerCamelCase_						=			self.numpy_to_pil(lowercase )[0].resize(image.size )

					# Run inpainting pipeline with the generated mask
					lowerCamelCase_						=			StableDiffusionInpaintPipeline(
					    vae=self.vae						,  text_encoder=self.text_encoder						,  tokenizer=self.tokenizer						,  unet=self.unet						,  scheduler=self.scheduler						,  safety_checker=self.safety_checker						,  feature_extractor=self.feature_extractor						,  )
					return inpainting_pipeline(
					    prompt=lowercase						,  image=lowercase						,  mask_image=lowercase						,  height=lowercase						,  width=lowercase						,  num_inference_steps=lowercase						,  guidance_scale=lowercase						,  negative_prompt=lowercase						,  num_images_per_prompt=lowercase						,  eta=lowercase						,  generator=lowercase						,  latents=lowercase						,  output_type=lowercase						,  return_dict=lowercase						,  callback=lowercase						,  callback_steps=lowercase						,  )


