



'''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_							: List[Any]						 =	logging.get_logger(__name__)  # pylint: disable=invalid-name



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_  ,)		->   Any:
        super().__init__()

        if hasattr(scheduler.config  ,"""steps_offset"""							) and scheduler.config.steps_offset != 1:
               _UpperCAmelCase  :						Optional[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                   f'''The configuration file of this scheduler: {scheduler} is outdated.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"""  ,a_  ,standard_warn=a_							)
               _UpperCAmelCase  :						Optional[int]			     = dict(scheduler.config							)
               _UpperCAmelCase  :						str			     = 1
               _UpperCAmelCase  :						Dict			     = FrozenDict(a_							)

        if hasattr(scheduler.config  ,"""skip_prk_steps"""							) and scheduler.config.skip_prk_steps is False:
               _UpperCAmelCase  :						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"""  ,a_  ,standard_warn=a_							)
               _UpperCAmelCase  :						Tuple			     = dict(scheduler.config							)
               _UpperCAmelCase  :						List[Any]			     = True
               _UpperCAmelCase  :						List[Any]			     = 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  ,a_ = "auto"							)		->   List[Any]:
        if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               _UpperCAmelCase  :						List[str]			     = self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(a_							)







 def _snake_case (							self							)		->   Any:
        self.enable_attention_slicing(a_							)







 def _snake_case (							self							)		->   Tuple:
        if is_accelerate_available():
               from accelerate import cpu_offload
        else:
               raise ImportError("""Please install accelerate via `pip install accelerate`"""							)

        _UpperCAmelCase  :						Dict			     = 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							)		->   str:
        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  ,a_  ,a_  ,a_  ,a_ = 512  ,a_ = 512  ,a_ = 50  ,a_ = 7.5  ,a_ = None  ,a_ = 1  ,a_ = 0.0  ,a_ = None  ,a_ = None  ,a_ = "pil"  ,a_ = True  ,a_ = None  ,a_ = 1  ,**a_  ,)		->   Any:
        _UpperCAmelCase  :						Dict			     = self.segmentation_processor(
            text=[text]  ,images=[image]  ,padding="""max_length"""  ,return_tensors="""pt"""							).to(self.device							)
        _UpperCAmelCase  :						str			     = self.segmentation_model(**a_							)
        _UpperCAmelCase  :						Optional[Any]			     = torch.sigmoid(outputs.logits							).cpu().detach().unsqueeze(-1							).numpy()
        _UpperCAmelCase  :						Any			     = self.numpy_to_pil(a_							)[0].resize(image.size							)

        # Run inpainting pipeline with the generated mask
        _UpperCAmelCase  :						Optional[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=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'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
   from ...onnx.config import PatchingSpec
   from ...tokenization_utils_base import PreTrainedTokenizerBase


A_							: Any						 =	logging.get_logger(__name__)

A_							: Optional[int]						 =	{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """longformer"""







 def __init__(							self  ,a_ = 512  ,a_ = 2  ,a_ = 1  ,a_ = 0  ,a_ = 2  ,a_ = 30_522  ,a_ = 768  ,a_ = 12  ,a_ = 12  ,a_ = 3_072  ,a_ = "gelu"  ,a_ = 0.1  ,a_ = 0.1  ,a_ = 512  ,a_ = 2  ,a_ = 0.02  ,a_ = 1E-1_2  ,a_ = False  ,**a_  ,)		->   List[Any]:
        super().__init__(pad_token_id=a_  ,**a_							)

        _UpperCAmelCase  :						List[Any]			     = attention_window
        _UpperCAmelCase  :						Any			     = sep_token_id
        _UpperCAmelCase  :						Dict			     = bos_token_id
        _UpperCAmelCase  :						Tuple			     = eos_token_id
        _UpperCAmelCase  :						Tuple			     = vocab_size
        _UpperCAmelCase  :						Optional[Any]			     = hidden_size
        _UpperCAmelCase  :						Optional[int]			     = num_hidden_layers
        _UpperCAmelCase  :						Union[str, Any]			     = num_attention_heads
        _UpperCAmelCase  :						Optional[int]			     = hidden_act
        _UpperCAmelCase  :						str			     = intermediate_size
        _UpperCAmelCase  :						List[Any]			     = hidden_dropout_prob
        _UpperCAmelCase  :						Union[str, Any]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						List[str]			     = max_position_embeddings
        _UpperCAmelCase  :						Optional[int]			     = type_vocab_size
        _UpperCAmelCase  :						Any			     = initializer_range
        _UpperCAmelCase  :						Optional[int]			     = layer_norm_eps
        _UpperCAmelCase  :						Union[str, Any]			     = onnx_export







class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_ = "default"  ,a_ = None							)		->   int:
        super().__init__(a_  ,a_  ,a_							)
        _UpperCAmelCase  :						Tuple			     = True







 @property
 def _snake_case (							self							)		->   Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
               _UpperCAmelCase  :						Optional[int]			     = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
               _UpperCAmelCase  :						Tuple			     = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""global_attention_mask""", dynamic_axis),
            ]							)







 @property
 def _snake_case (							self							)		->   Mapping[str, Mapping[int, str]]:
        _UpperCAmelCase  :						str			     = super().outputs
        if self.task == "default":
               _UpperCAmelCase  :						int			     = {0: """batch"""}
        return outputs







 @property
 def _snake_case (							self							)		->   float:
        return 1E-4







 @property
 def _snake_case (							self							)		->   int:
        # needs to be >= 14 to support tril operator
        return max(super().default_onnx_opset  ,14							)







 def _snake_case (							self  ,a_  ,a_ = -1  ,a_ = -1  ,a_ = False  ,a_ = None  ,)		->   Mapping[str, Any]:
        _UpperCAmelCase  :						List[str]			     = super().generate_dummy_inputs(
            preprocessor=a_  ,batch_size=a_  ,seq_length=a_  ,is_pair=a_  ,framework=a_							)
        import torch

        # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
        # makes the export fail randomly
        _UpperCAmelCase  :						int			     = torch.zeros_like(inputs["""input_ids"""]							)
        # make every second token global
        _UpperCAmelCase  :						List[str]			     = 1

        return inputs



