




"""simple docstring"""




from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


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





class 		A__    (       __SCREAMING_SNAKE_CASE			):
	lowerCamelCase__		:			Tuple       =["safety_checker", "feature_extractor"]






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = True    ,					)				->       Tuple:




						"""simple docstring"""






						super().__init__()
						if hasattr(scheduler.config    ,					'''steps_offset'''						) and scheduler.config.steps_offset != 1:
											__magic_name__ :							Dict		       =							(
											    F'''The configuration file of this scheduler: {scheduler} is outdated.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'''    ,					lowerCamelCase    ,					standard_warn=lowerCamelCase						)
											__magic_name__ :							Optional[int]		       =							dict(scheduler.config						)
											__magic_name__ :							Any		       =							1
											__magic_name__ :							Any		       =							FrozenDict(lowerCamelCase						)

						self.register_modules(
						    vae=lowerCamelCase    ,					text_encoder=lowerCamelCase    ,					tokenizer=lowerCamelCase    ,					unet=lowerCamelCase    ,					scheduler=lowerCamelCase    ,					safety_checker=lowerCamelCase    ,					feature_extractor=lowerCamelCase    ,					)
						self.register_to_config(requires_safety_checker=lowerCamelCase						)






	@property
	def 			lowercase				(     self						)				->       Dict[str, Any]:




						"""simple docstring"""






						return {k: getattr(self    ,					lowerCamelCase						) for k in self.config.keys() if not k.startswith('''_'''						)}






	def 			lowercase				(     self    ,					lowerCamelCase = "auto"						)				->       Union[str, Any]:




						"""simple docstring"""






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






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						self.enable_attention_slicing(lowerCamelCase						)






	@torch.no_grad()
	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = 0.8    ,					lowerCamelCase = 50    ,					lowerCamelCase = 7.5    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					lowerCamelCase = 0.0    ,					lowerCamelCase = None    ,					lowerCamelCase = "pil"    ,					lowerCamelCase = True    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					)				->       Any:




						"""simple docstring"""






						return StableDiffusionInpaintPipelineLegacy(**self.components						)(
						    prompt=lowerCamelCase    ,					image=lowerCamelCase    ,					mask_image=lowerCamelCase    ,					strength=lowerCamelCase    ,					num_inference_steps=lowerCamelCase    ,					guidance_scale=lowerCamelCase    ,					negative_prompt=lowerCamelCase    ,					num_images_per_prompt=lowerCamelCase    ,					eta=lowerCamelCase    ,					generator=lowerCamelCase    ,					output_type=lowerCamelCase    ,					return_dict=lowerCamelCase    ,					callback=lowerCamelCase    ,					)






	@torch.no_grad()
	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = 0.8    ,					lowerCamelCase = 50    ,					lowerCamelCase = 7.5    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					lowerCamelCase = 0.0    ,					lowerCamelCase = None    ,					lowerCamelCase = "pil"    ,					lowerCamelCase = True    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					**lowerCamelCase    ,					)				->       Dict:




						"""simple docstring"""






						return StableDiffusionImgaImgPipeline(**self.components						)(
						    prompt=lowerCamelCase    ,					image=lowerCamelCase    ,					strength=lowerCamelCase    ,					num_inference_steps=lowerCamelCase    ,					guidance_scale=lowerCamelCase    ,					negative_prompt=lowerCamelCase    ,					num_images_per_prompt=lowerCamelCase    ,					eta=lowerCamelCase    ,					generator=lowerCamelCase    ,					output_type=lowerCamelCase    ,					return_dict=lowerCamelCase    ,					callback=lowerCamelCase    ,					callback_steps=lowerCamelCase    ,					)






	@torch.no_grad()
	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase = 512    ,					lowerCamelCase = 512    ,					lowerCamelCase = 50    ,					lowerCamelCase = 7.5    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					lowerCamelCase = 0.0    ,					lowerCamelCase = None    ,					lowerCamelCase = None    ,					lowerCamelCase = "pil"    ,					lowerCamelCase = True    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					)				->       Optional[int]:




						"""simple docstring"""






						return StableDiffusionPipeline(**self.components						)(
						    prompt=lowerCamelCase    ,					height=lowerCamelCase    ,					width=lowerCamelCase    ,					num_inference_steps=lowerCamelCase    ,					guidance_scale=lowerCamelCase    ,					negative_prompt=lowerCamelCase    ,					num_images_per_prompt=lowerCamelCase    ,					eta=lowerCamelCase    ,					generator=lowerCamelCase    ,					latents=lowerCamelCase    ,					output_type=lowerCamelCase    ,					return_dict=lowerCamelCase    ,					callback=lowerCamelCase    ,					callback_steps=lowerCamelCase    ,					)









import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel





@require_torch
@require_sentencepiece
@require_tokenizers
class 		A__    (       unittest.TestCase			):






	@slow
	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							CamembertModel.from_pretrained('''camembert-base'''						)
						model.to(lowerCamelCase						)

						__magic_name__ :							Dict		       =							torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]    ,					device=lowerCamelCase    ,					dtype=torch.long    ,					)  # J'aime le camembert !
						with torch.no_grad():
											__magic_name__ :							Tuple		       =							model(lowerCamelCase						)['''last_hidden_state''']
						__magic_name__ :							int		       =							torch.Size((1, 10, 768)						)
						self.assertEqual(output.shape    ,					lowerCamelCase						)
						# compare the actual values for a slice.
						__magic_name__ :							Tuple		       =							torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]    ,					device=lowerCamelCase    ,					dtype=torch.float    ,					)
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]    ,					lowerCamelCase    ,					atol=1e-4						)						)



