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



class    _snake_case (						UpperCAmelCase_				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__()

   if hasattr(scheduler.config       ,	"""steps_offset""") and scheduler.config.steps_offset != 1:
     lowercase__	: Any     		=   (
         f'The configuration file of this scheduler: {scheduler} is outdated.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"""       ,	SCREAMING_SNAKE_CASE_       ,	standard_warn=SCREAMING_SNAKE_CASE_)
     lowercase__	: List[Any]     		=   dict(scheduler.config)
     lowercase__	: Optional[int]     		=   1
     lowercase__	: Tuple     		=   FrozenDict(SCREAMING_SNAKE_CASE_)

   if hasattr(scheduler.config       ,	"""skip_prk_steps""") and scheduler.config.skip_prk_steps is False:
     lowercase__	: 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"""       ,	SCREAMING_SNAKE_CASE_       ,	standard_warn=SCREAMING_SNAKE_CASE_)
     lowercase__	: Tuple     		=   dict(scheduler.config)
     lowercase__	: int     		=   True
     lowercase__	: List[Any]     		=   FrozenDict(SCREAMING_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=SCREAMING_SNAKE_CASE_       ,	segmentation_processor=SCREAMING_SNAKE_CASE_       ,	vae=SCREAMING_SNAKE_CASE_       ,	text_encoder=SCREAMING_SNAKE_CASE_       ,	tokenizer=SCREAMING_SNAKE_CASE_       ,	unet=SCREAMING_SNAKE_CASE_       ,	scheduler=SCREAMING_SNAKE_CASE_       ,	safety_checker=SCREAMING_SNAKE_CASE_       ,	feature_extractor=SCREAMING_SNAKE_CASE_       ,	)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_ = "auto"):


   '''simple docstring'''




   if slice_size == "auto":
     # half the attention head size is usually a good trade-off between
     # speed and memory
     lowercase__	: Union[str, Any]     		=   self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.enable_attention_slicing(SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: Union[str, Any]     		=   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(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def   lowercase__  (       self):


   '''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(SCREAMING_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       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = 5_12       ,	SCREAMING_SNAKE_CASE_ = 5_12       ,	SCREAMING_SNAKE_CASE_ = 50       ,	SCREAMING_SNAKE_CASE_ = 7.5       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = 1       ,	SCREAMING_SNAKE_CASE_ = 0.0       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = "pil"       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = 1       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




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

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








# Copyright 2021 The HuggingFace 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.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowerCamelCase__     :		Optional[int]        = [
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]

def 		UpperCamelCase						(   lowercase_			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   True
  while ask_again:
    lowercase__	: Tuple     		=   input(lowercase_ )
    try:
      if default is not None and len(lowercase_ ) == 0:
        return default
      return convert_value(lowercase_ ) if convert_value is not None else result
    except Exception:
      if error_message is not None:
        print(lowercase_ )

def 		UpperCamelCase						(   lowercase_			,			lowercase_=[]			,			lowercase_=None			,			lowercase_=0 )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   BulletMenu(lowercase_			,			lowercase_ )
  lowercase__	: Any     		=   menu.run(default_choice=lowercase_ )
  return convert_value(lowercase_ ) if convert_value is not None else result

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   int(lowercase_ )
  return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: List[str]     		=   int(lowercase_ )
  return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: str     		=   int(lowercase_ )
  return DynamoBackend(DYNAMO_BACKENDS[value] ).value

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   int(lowercase_ )
  return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   int(lowercase_ )
  return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  return {"yes": True, "no": False}[value.lower()]



class    _snake_case (						argparse.RawDescriptionHelpFormatter				):


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: int     		=   super()._format_usage(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   usage.replace("""<command> [<args>] """       ,	"""""")
   return usage






