



"""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


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







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:Optional[Any]       ,  lowercase_			:CLIPSegForImageSegmentation       ,  lowercase_			:CLIPSegProcessor       ,  lowercase_			:AutoencoderKL       ,  lowercase_			:CLIPTextModel       ,  lowercase_			:CLIPTokenizer       ,  lowercase_			:UNetaDConditionModel       ,  lowercase_			:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,  lowercase_			:StableDiffusionSafetyChecker       ,  lowercase_			:CLIPImageProcessor       ,  )    ->      List[str]:
												super().__init__()

												if hasattr(scheduler.config       ,  'steps_offset' ) and scheduler.config.steps_offset != 1:
																		UpperCAmelCase					=			(
																		    f"""The configuration file of this scheduler: {scheduler} is outdated.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_ )
																		UpperCAmelCase					=			dict(scheduler.config )
																		UpperCAmelCase					=			1
																		UpperCAmelCase					=			FrozenDict(lowercase_ )

												if hasattr(scheduler.config       ,  'skip_prk_steps' ) and scheduler.config.skip_prk_steps is False:
																		UpperCAmelCase					=			(
																		    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_ )
																		UpperCAmelCase					=			dict(scheduler.config )
																		UpperCAmelCase					=			True
																		UpperCAmelCase					=			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      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Optional[Union[str, int]] = "auto" )    ->      int:
												if slice_size == "auto":
																		# half the attention head size is usually a good trade-off between
																		# speed and memory
																		UpperCAmelCase					=			self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												self.enable_attention_slicing(lowercase_ )






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

												UpperCAmelCase					=			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      UpperCAmelCase__					( self			:Optional[Any] )    ->      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(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			:Optional[Any]       ,  lowercase_			:Union[str, List[str]]       ,  lowercase_			:Union[torch.FloatTensor, PIL.Image.Image]       ,  lowercase_			:str       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 50       ,  lowercase_			:float = 7.5       ,  lowercase_			:Optional[Union[str, List[str]]] = None       ,  lowercase_			:Optional[int] = 1       ,  lowercase_			:float = 0.0       ,  lowercase_			:Optional[torch.Generator] = None       ,  lowercase_			:Optional[torch.FloatTensor] = None       ,  lowercase_			:Optional[str] = "pil"       ,  lowercase_			:bool = True       ,  lowercase_			:Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,  lowercase_			:int = 1       ,  **lowercase_			:int       ,  )    ->      int:
												UpperCAmelCase					=			self.segmentation_processor(
												    text=[text]       ,  images=[image]       ,  padding='max_length'       ,  return_tensors='pt' ).to(self.device )
												UpperCAmelCase					=			self.segmentation_model(**lowercase_ )
												UpperCAmelCase					=			torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
												UpperCAmelCase					=			self.numpy_to_pil(lowercase_ )[0].resize(image.size )

												# Run inpainting pipeline with the generated mask
												UpperCAmelCase					=			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_       ,  )




"""simple docstring"""


# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_    					=	{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """CpmAntForCausalLM""",
				    """CpmAntModel""",
				    """CpmAntPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
				from .tokenization_cpmant import CpmAntTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_cpmant import (
								    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CpmAntForCausalLM,
								    CpmAntModel,
								    CpmAntPreTrainedModel,
								)


else:
				import sys

				snake_case_    					=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





