





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


SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						logging.get_logger(__name__)  # pylint: disable=invalid-name







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   def __init__( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			)				->  List[Any]:
      """simple docstring"""





      super().__init__()

      if hasattr(scheduler.config					,			"steps_offset"      ) and scheduler.config.steps_offset != 1:
         a__	:						Optional[int]      		=  (
             F"""The configuration file of this scheduler: {scheduler} is outdated.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"					,			snake_case					,			standard_warn=snake_case      )
         a__	:						Optional[Any]      		=  dict(scheduler.config      )
         a__	:						Any      		=  1
         a__	:						int      		=  FrozenDict(snake_case      )

      if hasattr(scheduler.config					,			"skip_prk_steps"      ) and scheduler.config.skip_prk_steps is False:
         a__	:						List[Any]      		=  (
             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"					,			snake_case					,			standard_warn=snake_case      )
         a__	:						Any      		=  dict(scheduler.config      )
         a__	:						int      		=  True
         a__	:						int      		=  FrozenDict(snake_case      )

      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=snake_case					,			segmentation_processor=snake_case					,			vae=snake_case					,			text_encoder=snake_case					,			tokenizer=snake_case					,			unet=snake_case					,			scheduler=snake_case					,			safety_checker=snake_case					,			feature_extractor=snake_case					,			)





   def 	_snake_case ( self					,			snake_case = "auto"      )				->  str:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      self.enable_attention_slicing(snake_case      )





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      if is_accelerate_available():
         from accelerate import cpu_offload
      else:
         raise ImportError("Please install accelerate via `pip install accelerate`"      )

      a__	:						Optional[int]      		=  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(snake_case					,			snake_case      )





   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      if self.device != torch.device("meta"      ) or not hasattr(self.unet					,			"_hf_hook"      ):
         return self.device
      for module in self.unet.modules():
         if (
             hasattr(snake_case					,			"_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					,			snake_case					,			snake_case					,			snake_case					,			snake_case = 512					,			snake_case = 512					,			snake_case = 50					,			snake_case = 7.5					,			snake_case = None					,			snake_case = 1					,			snake_case = 0.0					,			snake_case = None					,			snake_case = None					,			snake_case = "pil"					,			snake_case = True					,			snake_case = None					,			snake_case = 1					,			**snake_case					,			)				->  Optional[Any]:
      """simple docstring"""





      a__	:						Dict      		=  self.segmentation_processor(
          text=[text]					,			images=[image]					,			padding="max_length"					,			return_tensors="pt"      ).to(self.device      )
      a__	:						Union[str, Any]      		=  self.segmentation_model(**snake_case      )
      a__	:						Optional[Any]      		=  torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
      a__	:						List[Any]      		=  self.numpy_to_pil(snake_case      )[0].resize(image.size      )

      # Run inpainting pipeline with the generated mask
      a__	:						Optional[Any]      		=  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=snake_case					,			image=snake_case					,			mask_image=snake_case					,			height=snake_case					,			width=snake_case					,			num_inference_steps=snake_case					,			guidance_scale=snake_case					,			negative_prompt=snake_case					,			num_images_per_prompt=snake_case					,			eta=snake_case					,			generator=snake_case					,			latents=snake_case					,			output_type=snake_case					,			return_dict=snake_case					,			callback=snake_case					,			callback_steps=snake_case					,			)








import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						"""sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__       :			Tuple			      =						"""patrickvonplaten/t5-tiny-random"""







@require_torch
class   __lowerCAmelCase			(					unittest.TestCase  ):





   @cached_property
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      return AutoConfig.from_pretrained(snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.num_hidden_layers					,			1      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__		,     *a__	:						Any      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			self.teacher_config.encoder_layers      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__		,     *a__	:						List[str]      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			1      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      with self.assertRaises(snake_case      ):
         create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=snake_case					,			d=snake_case      )

