
import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
							import onnxruntime as ort


lowercase__	:List[Any]										=					logging.get_logger(__name__)

lowercase__	:Dict										=					{
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}







class        lowercase	:




		def __init__(       self     ,A__=None     ,**A__):
							logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.''')
							lowercase					=		model
							lowercase					=		kwargs.get('''model_save_dir'''     ,A__)
							lowercase					=		kwargs.get('''latest_model_name'''     ,A__)




		def __call__(       self     ,**A__):
							lowercase					=		{k: np.array(A__) for k, v in kwargs.items()}
							return self.model.run(A__     ,A__)




		@staticmethod
		def 				A__   (       A__     ,A__=None     ,A__=None):
							if provider is None:
												logger.info('''No onnxruntime provider specified, using CPUExecutionProvider''')
												lowercase					=		'''CPUExecutionProvider'''

							return ort.InferenceSession(A__     ,providers=[provider]     ,sess_options=A__)




		def 				A__   (       self     ,A__     ,A__ = None     ,**A__):
							lowercase					=		file_name if file_name is not None else ONNX_WEIGHTS_NAME

							lowercase					=		self.model_save_dir.joinpath(self.latest_model_name)
							lowercase					=		Path(A__).joinpath(A__)
							try:
												shutil.copyfile(A__     ,A__)
							except shutil.SameFileError:
												pass

							# copy external weights (for models >2GB)
							lowercase					=		self.model_save_dir.joinpath(A__)
							if src_path.exists():
												lowercase					=		Path(A__).joinpath(A__)
												try:
																	shutil.copyfile(A__     ,A__)
												except shutil.SameFileError:
																	pass




		def 				A__   (       self     ,A__     ,**A__     ,):
							if os.path.isfile(A__):
												logger.error(f'Provided path ({save_directory}) should be a directory, not a file')
												return

							os.makedirs(A__     ,exist_ok=A__)

							# saving model weights/files
							self._save_pretrained(A__     ,**A__)




		@classmethod
		def 				A__   (       cls     ,A__     ,A__ = None     ,A__ = None     ,A__ = False     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,**A__     ,):
							lowercase					=		file_name if file_name is not None else ONNX_WEIGHTS_NAME
							# load model from local directory
							if os.path.isdir(A__):
												lowercase					=		OnnxRuntimeModel.load_model(
												    os.path.join(A__     ,A__)     ,provider=A__     ,sess_options=A__)
												lowercase					=		Path(A__)
							# load model from hub
							else:
												# download model
												lowercase					=		hf_hub_download(
												    repo_id=A__     ,filename=A__     ,use_auth_token=A__     ,revision=A__     ,cache_dir=A__     ,force_download=A__     ,)
												lowercase					=		Path(A__).parent
												lowercase					=		Path(A__).name
												lowercase					=		OnnxRuntimeModel.load_model(A__     ,provider=A__     ,sess_options=A__)
							return cls(model=A__     ,**A__)





		@classmethod
		def 				A__   (       cls     ,A__     ,A__ = True     ,A__ = None     ,A__ = None     ,**A__     ,):
							lowercase					=		None
							if len(str(A__).split('''@''')) == 2:
												lowercase			,		lowercase					=		model_id.split('''@''')

							return cls._from_pretrained(
							    model_id=A__     ,revision=A__     ,cache_dir=A__     ,force_download=A__     ,use_auth_token=A__     ,**A__     ,)






import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


lowercase__	:Union[str, Any]										=					logging.get_logger(__name__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Union[str, Any]       =CLIPConfig

							lowercase_ : str       =['''CLIPEncoderLayer''']




							def __init__(       self     ,A__):
												super().__init__(A__)

												lowercase					=		CLIPVisionModelWithProjection(config.vision_config)

												lowercase					=		nn.Linear(config.vision_config.projection_dim     ,1)
												lowercase					=		nn.Linear(config.vision_config.projection_dim     ,1)





							@torch.no_grad()
							def 				A__   (       self     ,A__     ,A__     ,A__=0.5     ,A__=0.5):
												lowercase					=		self.vision_model(A__)[0]

												lowercase					=		self.p_head(A__)
												lowercase					=		nsfw_detected.flatten()
												lowercase					=		nsfw_detected > p_threshold
												lowercase					=		nsfw_detected.tolist()

												if any(A__):
																	logger.warning(
																	    '''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
																	    ''' Try again with a different prompt and/or seed.''')

												for idx, nsfw_detected_ in enumerate(A__):
																	if nsfw_detected_:
																						lowercase					=		np.zeros(images[idx].shape)

												lowercase					=		self.w_head(A__)
												lowercase					=		watermark_detected.flatten()
												lowercase					=		watermark_detected > w_threshold
												lowercase					=		watermark_detected.tolist()

												if any(A__):
																	logger.warning(
																	    '''Potential watermarked content was detected in one or more images. A black image will be returned instead.'''
																	    ''' Try again with a different prompt and/or seed.''')

												for idx, watermark_detected_ in enumerate(A__):
																	if watermark_detected_:
																						lowercase					=		np.zeros(images[idx].shape)

												return images, nsfw_detected, watermark_detected




