"""simple docstring"""




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


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





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

													if hasattr(scheduler.config						,'steps_offset'   ) and scheduler.config.steps_offset != 1:
																				A__		   =							(
																				    f'''The configuration file of this scheduler: {scheduler} is outdated.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'						,__UpperCAmelCase						,standard_warn=__UpperCAmelCase   )
																				A__		   =							dict(scheduler.config   )
																				A__		   =							1
																				A__		   =							FrozenDict(__UpperCAmelCase   )

													if hasattr(scheduler.config						,'skip_prk_steps'   ) and scheduler.config.skip_prk_steps is False:
																				A__		   =							(
																				    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'						,__UpperCAmelCase						,standard_warn=__UpperCAmelCase   )
																				A__		   =							dict(scheduler.config   )
																				A__		   =							True
																				A__		   =							FrozenDict(__UpperCAmelCase   )

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





						def  snake_case__		(				self						,__UpperCAmelCase = "auto"   )    -> List[Any]:
													if slice_size == "auto":
																				# half the attention head size is usually a good trade-off between
																				# speed and memory
																				A__		   =							self.unet.config.attention_head_dim // 2
													self.unet.set_attention_slice(__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													self.enable_attention_slicing(__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> int:
													if is_accelerate_available():
																				from accelerate import cpu_offload
													else:
																				raise ImportError('Please install accelerate via `pip install accelerate`'   )

													A__		   =							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(__UpperCAmelCase						,__UpperCAmelCase   )





						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def  snake_case__		(				self   )    -> List[str]:
													if self.device != torch.device('meta'   ) or not hasattr(self.unet						,'_hf_hook'   ):
																				return self.device
													for module in self.unet.modules():
																				if (
																				    hasattr(__UpperCAmelCase						,'_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						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 5_12						,__UpperCAmelCase = 5_12						,__UpperCAmelCase = 50						,__UpperCAmelCase = 7.5						,__UpperCAmelCase = None						,__UpperCAmelCase = 1						,__UpperCAmelCase = 0.0						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = "pil"						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = 1						,**__UpperCAmelCase						,)    -> Union[str, Any]:
													A__		   =							self.segmentation_processor(
													    text=[text]						,images=[image]						,padding='max_length'						,return_tensors='pt'   ).to(self.device   )
													A__		   =							self.segmentation_model(**__UpperCAmelCase   )
													A__		   =							torch.sigmoid(outputs.logits   ).cpu().detach().unsqueeze(-1   ).numpy()
													A__		   =							self.numpy_to_pil(__UpperCAmelCase   )[0].resize(image.size   )

													# Run inpainting pipeline with the generated mask
													A__		   =							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=__UpperCAmelCase						,image=__UpperCAmelCase						,mask_image=__UpperCAmelCase						,height=__UpperCAmelCase						,width=__UpperCAmelCase						,num_inference_steps=__UpperCAmelCase						,guidance_scale=__UpperCAmelCase						,negative_prompt=__UpperCAmelCase						,num_images_per_prompt=__UpperCAmelCase						,eta=__UpperCAmelCase						,generator=__UpperCAmelCase						,latents=__UpperCAmelCase						,output_type=__UpperCAmelCase						,return_dict=__UpperCAmelCase						,callback=__UpperCAmelCase						,callback_steps=__UpperCAmelCase						,)






"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Union[str, Any]  							=						'transfo-xl'
						lowerCAmelCase__     :  Any  							=						['mems']
						lowerCAmelCase__     :  Tuple  							=						{
						    'n_token': 'vocab_size',
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}





						def __init__(				self						,__UpperCAmelCase=26_77_35						,__UpperCAmelCase=[2_00_00, 4_00_00, 20_00_00]						,__UpperCAmelCase=10_24						,__UpperCAmelCase=10_24						,__UpperCAmelCase=16						,__UpperCAmelCase=64						,__UpperCAmelCase=40_96						,__UpperCAmelCase=4						,__UpperCAmelCase=False						,__UpperCAmelCase=18						,__UpperCAmelCase=16_00						,__UpperCAmelCase=10_00						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=0						,__UpperCAmelCase=-1						,__UpperCAmelCase=True						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.0						,__UpperCAmelCase=True						,__UpperCAmelCase="normal"						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=1e-5						,__UpperCAmelCase=0						,**__UpperCAmelCase						,)    -> Tuple:
													A__		   =							vocab_size
													A__		   =							[]
													self.cutoffs.extend(__UpperCAmelCase   )
													if proj_share_all_but_first:
																				A__		   =							[False] + [True] * len(self.cutoffs   )
													else:
																				A__		   =							[False] + [False] * len(self.cutoffs   )
													A__		   =							d_model
													A__		   =							d_embed
													A__		   =							d_head
													A__		   =							d_inner
													A__		   =							div_val
													A__		   =							pre_lnorm
													A__		   =							n_layer
													A__		   =							n_head
													A__		   =							mem_len
													A__		   =							same_length
													A__		   =							attn_type
													A__		   =							clamp_len
													A__		   =							sample_softmax
													A__		   =							adaptive
													A__		   =							dropout
													A__		   =							dropatt
													A__		   =							untie_r
													A__		   =							init
													A__		   =							init_range
													A__		   =							proj_init_std
													A__		   =							init_std
													A__		   =							layer_norm_epsilon
													super().__init__(eos_token_id=__UpperCAmelCase						,**__UpperCAmelCase   )





						@property
						def  snake_case__		(				self   )    -> Optional[Any]:
													# Message copied from Transformer-XL documentation
													logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
													return -1





						@max_position_embeddings.setter
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> int:
													# Message copied from Transformer-XL documentation
													raise NotImplementedError(
													    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )





