





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


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



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





        '''simple docstring'''




        super().__init__()

        if hasattr(scheduler.config      ,   "steps_offset"	) and scheduler.config.steps_offset != 1:
           A_       :					List[str]        =      (
               f"The configuration file of this scheduler: {scheduler} is outdated.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"      ,   snake_case      ,   standard_warn=snake_case	)
           A_       :					List[str]        =      dict(scheduler.config	)
           A_       :					List[str]        =      1
           A_       :					Any        =      FrozenDict(snake_case	)

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



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							: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
           A_       :					List[str]        =      self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        self.enable_attention_slicing(snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




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

        A_       :					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(snake_case      ,   snake_case	)



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





        '''simple docstring'''




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

        # Run inpainting pipeline with the generated mask
        A_       :					Union[str, Any]        =      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=snake_case      ,   image=snake_case      ,   mask_image=snake_case      ,   height=snake_case      ,   width=snake_case      ,   num_inference_steps=snake_case      ,   guidance_scale=snake_case      ,   negative_prompt=snake_case      ,   num_images_per_prompt=snake_case      ,   eta=snake_case      ,   generator=snake_case      ,   latents=snake_case      ,   output_type=snake_case      ,   return_dict=snake_case      ,   callback=snake_case      ,   callback_steps=snake_case      ,   )







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowerCAmelCase				:   Optional[int]						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   List[str]						       =						{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''umt5'''
     __UpperCamelCase            =  ['''past_key_values''']



     def __init__(				self							:int      ,   snake_case							:Optional[Any]=250_112      ,   snake_case							:Optional[int]=512      ,   snake_case							:Any=64      ,   snake_case							:Union[str, Any]=1_024      ,   snake_case							:Tuple=8      ,   snake_case							:Optional[int]=None      ,   snake_case							:Union[str, Any]=6      ,   snake_case							:List[Any]=32      ,   snake_case							:Dict=128      ,   snake_case							:List[str]=0.1      ,   snake_case							:List[Any]=1e-6      ,   snake_case							:Dict=1.0      ,   snake_case							:Union[str, Any]="gated-gelu"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Any=True      ,   snake_case							:List[str]="T5Tokenizer"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Union[str, Any]=0      ,   snake_case							:List[Any]=1      ,   snake_case							:List[Any]=0      ,   **snake_case							:Any      ,   ):





        '''simple docstring'''




        super().__init__(
            is_encoder_decoder=snake_case      ,   tokenizer_class=snake_case      ,   tie_word_embeddings=snake_case      ,   pad_token_id=snake_case      ,   eos_token_id=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )
        A_       :					Union[str, Any]        =      vocab_size
        A_       :					Tuple        =      d_model
        A_       :					List[str]        =      d_kv
        A_       :					Union[str, Any]        =      d_ff
        A_       :					Any        =      num_layers
        A_       :					Optional[Any]        =      (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A_       :					List[Any]        =      num_heads
        A_       :					List[str]        =      relative_attention_num_buckets
        A_       :					Dict        =      relative_attention_max_distance
        A_       :					Optional[Any]        =      dropout_rate
        A_       :					Any        =      layer_norm_epsilon
        A_       :					List[Any]        =      initializer_factor
        A_       :					Any        =      feed_forward_proj
        A_       :					Optional[Any]        =      use_cache

        A_       :					int        =      self.feed_forward_proj.split("-"	)
        A_       :					Any        =      act_info[-1]
        A_       :					Tuple        =      act_info[0] == "gated"

        if len(snake_case	) > 1 and act_info[0] != "gated" or len(snake_case	) > 2:
           raise ValueError(
               f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'"	)

        if feed_forward_proj == "gated-gelu":
           A_       :					Optional[Any]        =      "gelu_new"



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.d_model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.num_heads





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        return self.num_layers




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					List[Any]        =      {
            "input_ids": {0: "batch", 1: "encoder_sequence"},
            "attention_mask": {0: "batch", 1: "encoder_sequence"},
        }
        if self.use_past:
           A_       :					Any        =      "past_encoder_sequence + sequence"
           A_       :					Union[str, Any]        =      {0: "batch"}
           A_       :					Optional[int]        =      {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
           A_       :					Dict        =      {0: "batch", 1: "decoder_sequence"}
           A_       :					str        =      {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
           self.fill_with_past_key_values_(snake_case      ,   direction="inputs"	)

        return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return 13





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        return 5e-4
