






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


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


class 							__UpperCamelCase     (  _lowerCAmelCase ):







	def __init__(	self   :				Union[str, Any]							,					_lowerCAmelCase   :				CLIPSegForImageSegmentation							,					_lowerCAmelCase   :				CLIPSegProcessor							,					_lowerCAmelCase   :				AutoencoderKL							,					_lowerCAmelCase   :				CLIPTextModel							,					_lowerCAmelCase   :				CLIPTokenizer							,					_lowerCAmelCase   :				UNetaDConditionModel							,					_lowerCAmelCase   :				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,					_lowerCAmelCase   :				StableDiffusionSafetyChecker							,					_lowerCAmelCase   :				CLIPImageProcessor							,					)		->			Any:
		"""simple docstring"""







		super().__init__()

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			    F'The configuration file of this scheduler: {scheduler} is outdated.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

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

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







	def 			_a (	self   :				Tuple							,					_lowerCAmelCase   :				Optional[Union[str, int]] = "auto"      )		->			Any:
		"""simple docstring"""







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







	def 			_a (	self   :				Optional[int]      )		->			Optional[Any]:
		"""simple docstring"""







		self.enable_attention_slicing(_lowerCAmelCase      )







	def 			_a (	self   :				Tuple      )		->			Union[str, Any]:
		"""simple docstring"""







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

		__lowercase   							=	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(_lowerCAmelCase							,					_lowerCAmelCase      )







	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 			_a (	self   :				Tuple      )		->			Optional[Any]:
		"""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(_lowerCAmelCase							,					"""_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   :				Union[str, Any]							,					_lowerCAmelCase   :				Union[str, List[str]]							,					_lowerCAmelCase   :				Union[torch.FloatTensor, PIL.Image.Image]							,					_lowerCAmelCase   :				str							,					_lowerCAmelCase   :				int = 512							,					_lowerCAmelCase   :				int = 512							,					_lowerCAmelCase   :				int = 50							,					_lowerCAmelCase   :				float = 7.5							,					_lowerCAmelCase   :				Optional[Union[str, List[str]]] = None							,					_lowerCAmelCase   :				Optional[int] = 1							,					_lowerCAmelCase   :				float = 0.0							,					_lowerCAmelCase   :				Optional[torch.Generator] = None							,					_lowerCAmelCase   :				Optional[torch.FloatTensor] = None							,					_lowerCAmelCase   :				Optional[str] = "pil"							,					_lowerCAmelCase   :				bool = True							,					_lowerCAmelCase   :				Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,					_lowerCAmelCase   :				int = 1							,					**_lowerCAmelCase   :				Optional[int]							,					)		->			Optional[int]:
		"""simple docstring"""







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

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



"""simple docstring"""




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_A     :			Tuple  						=			logging.get_logger(__name__)

_A     :			Dict  						=			{"""vocab_file""": """vocab.txt"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

_A     :			Optional[Any]  						=			{
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}


def     __magic_name__      ( __snake_case							:			Tuple    )					->							str:
       with open(__snake_case	,					"r"    ) as f:
              lowercase       :							Dict									=				f.read().splitlines()
              return [l.strip() for l in lines]

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   def __init__(     self      ,       _a      ,       _a="<unk>"      ,       _a="<cls>"      ,       _a="<pad>"      ,       _a="<mask>"      ,       _a="<eos>"      ,       **_a      ,       ):
          super().__init__(**_a							)
          lowercase       :							Union[str, Any]									=				load_vocab_file(_a							)
          lowercase       :							Union[str, Any]									=				dict(enumerate(self.all_tokens							)							)
          lowercase       :							Any									=				{tok: ind for ind, tok in enumerate(self.all_tokens							)}
          lowercase       :							Dict									=				unk_token
          lowercase       :							Optional[int]									=				cls_token
          lowercase       :							Optional[int]									=				pad_token
          lowercase       :							Any									=				mask_token
          lowercase       :							List[str]									=				eos_token
          lowercase       :							Tuple									=				self.all_tokens
          self._create_trie(self.unique_no_split_tokens							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a      ,       **_a							):
          return text.split()
   def      __magic_name__     (     self      ,       _a=False							):
          return len(self._id_to_token							)
   def      __magic_name__     (     self							):
          return {token: i for i, token in enumerate(self.all_tokens							)}
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[int]									=				[self.cls_token_id]
          lowercase       :							Dict									=				[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
                 if self.eos_token_id is None:
                        return cls + token_ids_a
                 else:
                        return cls + token_ids_a + sep
          elif self.eos_token_id is None:
                 raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"							)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = False							):
          if already_has_special_tokens:
                 if token_ids_a is not None:
                        raise ValueError(
                            "You should not supply a second sequence if the provided sequence of "
                            "ids is already formatted with special tokens for the model."							)

                 return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          lowercase       :							Union[str, Any]									=				[1] + ([0] * len(_a							)) + [1]
          if token_ids_a is not None:
                 mask += [0] * len(_a							) + [1]
          return mask
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Optional[Any]									=				os.path.join(_a      ,       (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"							)
          with open(_a      ,       "w"							) as f:
                 f.write("\n".join(self.all_tokens							)							)
          return (vocab_file,)
   @property
   def      __magic_name__     (     self							):
          return self.get_vocab_size(with_added_tokens=_a							)

   def      __magic_name__     (     self      ,       _a      ,       _a = False							):
          return super()._add_tokens(_a      ,       special_tokens=_a							)


