


'''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


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





class      snake_case    (    lowerCAmelCase__			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__()

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        f'''The configuration file of this scheduler: {scheduler} is outdated.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

   if hasattr(scheduler.config			,  "skip_prk_steps"     ) and scheduler.config.skip_prk_steps is False:
    lowerCamelCase_      =  (
        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"			,  __a			,  standard_warn=__a     )
    lowerCamelCase_      =  dict(scheduler.config     )
    lowerCamelCase_      =  True
    lowerCamelCase_      =  FrozenDict(__a     )

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






  def   snake_case		(    self			,  UpperCamelCase = "auto"     ):







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






  def   snake_case		(    self     ):







   """simple docstring"""
   self.enable_attention_slicing(__a     )






  def   snake_case		(    self     ):







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

   lowerCamelCase_      =  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(__a			,  __a     )






  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def   snake_case		(    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(__a			,  "_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 = 512			,  UpperCamelCase = 512			,  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			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.segmentation_processor(
       text=[text]			,  images=[image]			,  padding="max_length"			,  return_tensors="pt"     ).to(self.device     )
   lowerCamelCase_      =  self.segmentation_model(**__a     )
   lowerCamelCase_      =  torch.sigmoid(outputs.logits     ).cpu().detach().unsqueeze(-1     ).numpy()
   lowerCamelCase_      =  self.numpy_to_pil(__a     )[0].resize(image.size     )

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







"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :   Dict			    =			logging.getLogger(__name__)
@dataclass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=0.0       ,metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Whether to SortishSamler or not."""}		)
							__UpperCAmelCase						:    bool				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """whether to use adafactor"""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Attention dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[str]				      =field(
							    default="""linear"""       ,metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'''}       ,)




