import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar







class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      logging.get_logger()

     # the current default level is logging.WARNING
     SCREAMING_SNAKE_CASE__										=      logging.get_verbosity()

     logging.set_verbosity_error()
     self.assertEqual(logger.getEffectiveLevel()       ,	logging.get_verbosity()      )

     logging.set_verbosity_warning()
     self.assertEqual(logger.getEffectiveLevel()       ,	logging.get_verbosity()      )

     logging.set_verbosity_info()
     self.assertEqual(logger.getEffectiveLevel()       ,	logging.get_verbosity()      )

     logging.set_verbosity_debug()
     self.assertEqual(logger.getEffectiveLevel()       ,	logging.get_verbosity()      )

     # restore to the original level
     logging.set_verbosity(_lowercase      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      logging.get_verbosity()

     SCREAMING_SNAKE_CASE__										=      logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
     SCREAMING_SNAKE_CASE__										=      """Testing 1, 2, 3"""

     # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
     if level_origin <= logging.WARNING:
       with CaptureLogger(_lowercase      ) as cl:
         logger.warning(_lowercase      )
       self.assertEqual(cl.out       ,	msg + """\n"""      )

     # this is setting the level for all of `transformers.*` loggers
     logging.set_verbosity_error()

     # should not be able to log warnings
     with CaptureLogger(_lowercase      ) as cl:
       logger.warning(_lowercase      )
     self.assertEqual(cl.out       ,	""""""      )

     # should be able to log warnings again
     logging.set_verbosity_warning()
     with CaptureLogger(_lowercase      ) as cl:
       logger.warning(_lowercase      )
     self.assertEqual(cl.out       ,	msg + """\n"""      )

     # restore to the original level
     logging.set_verbosity(_lowercase      )




   @mockenv(TRANSFORMERS_VERBOSITY="""error"""      )
   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     transformers.utils.logging._reset_library_root_logger()
     # this action activates the env var
     SCREAMING_SNAKE_CASE__										=      logging.get_logger("""transformers.models.bart.tokenization_bart"""      )

     SCREAMING_SNAKE_CASE__										=      os.getenv("""TRANSFORMERS_VERBOSITY"""       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      logging.log_levels[env_level_str]

     SCREAMING_SNAKE_CASE__										=      logging.get_verbosity()
     self.assertEqual(
         _lowercase       ,	_lowercase       ,	f"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"""       ,	)

     # restore to the original level
     SCREAMING_SNAKE_CASE__										=      """"""
     transformers.utils.logging._reset_library_root_logger()




   @mockenv(TRANSFORMERS_VERBOSITY="""super-error"""      )
   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     transformers.utils.logging._reset_library_root_logger()
     SCREAMING_SNAKE_CASE__										=      logging.logging.getLogger()
     with CaptureLogger(_lowercase      ) as cl:
       # this action activates the env var
       logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
     self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""       ,	cl.out      )

     # no need to restore as nothing was changed




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     transformers.utils.logging._reset_library_root_logger()

     SCREAMING_SNAKE_CASE__										=      logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
     SCREAMING_SNAKE_CASE__										=      """Testing 1, 2, 3"""

     with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""      ):
       # nothing should be logged as env var disables this method
       with CaptureLogger(_lowercase      ) as cl:
         logger.warning_advice(_lowercase      )
       self.assertEqual(cl.out       ,	""""""      )

     with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""      ):
       # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
       with CaptureLogger(_lowercase      ) as cl:
         logger.warning_advice(_lowercase      )
       self.assertEqual(cl.out       ,	msg + """\n"""      )





def 	__SCREAMING_SNAKE_CASE  (					)							->					Optional[int]:







  """simple docstring"""





  disable_progress_bar()
  assert are_progress_bars_disabled()

  enable_progress_bar()
  assert not are_progress_bars_disabled()

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







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				Dict       ,	_lowercase			:				CLIPSegForImageSegmentation       ,	_lowercase			:				CLIPSegProcessor       ,	_lowercase			:				AutoencoderKL       ,	_lowercase			:				CLIPTextModel       ,	_lowercase			:				CLIPTokenizer       ,	_lowercase			:				UNetaDConditionModel       ,	_lowercase			:				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,	_lowercase			:				StableDiffusionSafetyChecker       ,	_lowercase			:				CLIPImageProcessor       ,	):



     """simple docstring"""






     super().__init__()

     if hasattr(scheduler.config       ,	"""steps_offset"""      ) and scheduler.config.steps_offset != 1:
       SCREAMING_SNAKE_CASE__										=      (
           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      )
       SCREAMING_SNAKE_CASE__										=      dict(scheduler.config      )
       SCREAMING_SNAKE_CASE__										=      1
       SCREAMING_SNAKE_CASE__										=      FrozenDict(_lowercase      )

     if hasattr(scheduler.config       ,	"""skip_prk_steps"""      ) and scheduler.config.skip_prk_steps is False:
       SCREAMING_SNAKE_CASE__										=      (
           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      )
       SCREAMING_SNAKE_CASE__										=      dict(scheduler.config      )
       SCREAMING_SNAKE_CASE__										=      True
       SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				List[Any]       ,	_lowercase			:				Optional[Union[str, int]] = "auto"      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     self.enable_attention_slicing(_lowercase      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				Optional[int]      ):



     """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(_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			:				str       ,	):



     """simple docstring"""






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

     # Run inpainting pipeline with the generated mask
     SCREAMING_SNAKE_CASE__										=      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       ,	)
