
from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule


def 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Any=1E-12    ):
       snake_case       :   str							=      jnp.divide(emb_a.T				,					jnp.clip(jnp.linalg.norm(__lowerCamelCase				,					axis=1    )				,					a_min=__lowerCamelCase    )    ).T
       snake_case       :   str							=      jnp.divide(emb_a.T				,					jnp.clip(jnp.linalg.norm(__lowerCamelCase				,					axis=1    )				,					a_min=__lowerCamelCase    )    ).T
       return jnp.matmul(__lowerCamelCase				,					norm_emb_a.T    )






class   UpperCAmelCase		(						nn.Module ):
   A__					:		CLIPConfig
   A__					:		jnp.dtype						     =     jnp.floataa



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      FlaxCLIPVisionModule(self.config.vision_config )
          snake_case       :   List[Any]							=      nn.Dense(self.config.projection_dim    ,						use_bias=snake_case__    ,						dtype=self.dtype )

          snake_case       :   Tuple							=      self.param("concept_embeds"    ,						jax.nn.initializers.ones    ,						(17, self.config.projection_dim) )
          snake_case       :   Optional[int]							=      self.param(
              "special_care_embeds"    ,						jax.nn.initializers.ones    ,						(3, self.config.projection_dim) )

          snake_case       :   Any							=      self.param("concept_embeds_weights"    ,						jax.nn.initializers.ones    ,						(17,) )
          snake_case       :   Any							=      self.param("special_care_embeds_weights"    ,						jax.nn.initializers.ones    ,						(3,) )



   def __call__(self		:					Tuple    ,						snake_case__		:					Tuple )		->	Any:


          '''simple docstring'''


          snake_case       :   str							=      self.vision_model(snake_case__ )[1]
          snake_case       :   int							=      self.visual_projection(snake_case__ )

          snake_case       :   str							=      jax_cosine_distance(snake_case__    ,						self.special_care_embeds )
          snake_case       :   Dict							=      jax_cosine_distance(snake_case__    ,						self.concept_embeds )

          # increase this value to create a stronger `nfsw` filter
          # at the cost of increasing the possibility of filtering benign image inputs
          snake_case       :   List[str]							=      0.0

          snake_case       :   Optional[int]							=      special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
          snake_case       :   int							=      jnp.round(snake_case__    ,						3 )
          snake_case       :   Optional[Any]							=      jnp.any(special_scores > 0    ,						axis=1    ,						keepdims=snake_case__ )
          # Use a lower threshold if an image has any special care concept
          snake_case       :   str							=      is_special_care * 0.01

          snake_case       :   Any							=      cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
          snake_case       :   Optional[Any]							=      jnp.round(snake_case__    ,						3 )
          snake_case       :   int							=      jnp.any(concept_scores > 0    ,						axis=1 )

          return has_nsfw_concepts






class   UpperCAmelCase		(						A_ ):
   A__					:		str						     =     CLIPConfig
   A__					:		int						     =     "clip_input"
   A__					:		List[str]						     =     FlaxStableDiffusionSafetyCheckerModule



   def __init__(self		:					str    ,						snake_case__		:					CLIPConfig    ,						snake_case__		:					Optional[Tuple] = None    ,						snake_case__		:					int = 0    ,						snake_case__		:					jnp.dtype = jnp.floataa    ,						snake_case__		:					bool = True    ,						**snake_case__		:					Dict    ,						)		->	Dict:


          '''simple docstring'''


          if input_shape is None:
                 snake_case       :   int							=      (1, 2_24, 2_24, 3)
          snake_case       :   Union[str, Any]							=      self.module_class(config=snake_case__    ,						dtype=snake_case__    ,						**snake_case__ )
          super().__init__(snake_case__    ,						snake_case__    ,						input_shape=snake_case__    ,						seed=snake_case__    ,						dtype=snake_case__    ,						_do_init=_do_init )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					jax.random.KeyArray    ,						snake_case__		:					Tuple    ,						snake_case__		:					FrozenDict = None )		->	FrozenDict:


          '''simple docstring'''


          snake_case       :   str							=      jax.random.normal(snake_case__    ,						snake_case__ )

          snake_case				,					snake_case       :   int							=      jax.random.split(snake_case__ )
          snake_case       :   Optional[int]							=      {"params": params_rng, "dropout": dropout_rng}

          snake_case       :   Optional[int]							=      self.module.init(snake_case__    ,						snake_case__ )["params"]

          return random_params



   def __call__(self		:					Union[str, Any]    ,						snake_case__		:					str    ,						snake_case__		:					dict = None    ,						)		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   int							=      jnp.transpose(snake_case__    ,						(0, 2, 3, 1) )

          return self.module.apply(
              {"params": params or self.params}    ,						jnp.array(snake_case__    ,						dtype=jnp.floataa )    ,						rngs={}    ,						)







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


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    """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   UpperCAmelCase		:



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]=None    ,						**snake_case__		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future." )
          snake_case       :   Optional[Any]							=      model
          snake_case       :   Dict							=      kwargs.get("model_save_dir"    ,						snake_case__ )
          snake_case       :   int							=      kwargs.get("latest_model_name"    ,						snake_case__ )



   def __call__(self		:					Tuple    ,						**snake_case__		:					str )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      {k: np.array(snake_case__ ) for k, v in kwargs.items()}
          return self.model.run(snake_case__    ,						snake_case__ )



   @staticmethod
   def 				_SCREAMING_SNAKE_CASE   (snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[int]=None    ,						snake_case__		:					Optional[int]=None )		->	Any:


          '''simple docstring'''


          if provider is None:
                 logger.info("No onnxruntime provider specified, using CPUExecutionProvider" )
                 snake_case       :   Optional[int]							=      "CPUExecutionProvider"

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[str] = None    ,						**snake_case__		:					Any )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Tuple							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME

          snake_case       :   Any							=      self.model_save_dir.joinpath(self.latest_model_name )
          snake_case       :   str							=      Path(snake_case__ ).joinpath(snake_case__ )
          try:
                 shutil.copyfile(snake_case__    ,						snake_case__ )
          except shutil.SameFileError:
                 pass

          # copy external weights (for models >2GB)
          snake_case       :   List[str]							=      self.model_save_dir.joinpath(snake_case__ )
          if src_path.exists():
                 snake_case       :   Tuple							=      Path(snake_case__ ).joinpath(snake_case__ )
                 try:
                        shutil.copyfile(snake_case__    ,						snake_case__ )
                 except shutil.SameFileError:
                        pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, os.PathLike]    ,						**snake_case__		:					Optional[int]    ,						)		->	str:


          '''simple docstring'''


          if os.path.isfile(snake_case__ ):
                 logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
                 return

          os.makedirs(snake_case__    ,						exist_ok=snake_case__ )

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



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Tuple    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[Union[bool, str, None]] = None    ,						snake_case__		:					Optional[Union[str, None]] = None    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional["ort.SessionOptions"] = None    ,						**snake_case__		:					Tuple    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME
          # load model from local directory
          if os.path.isdir(snake_case__ ):
                 snake_case       :   Any							=      OnnxRuntimeModel.load_model(
                     os.path.join(snake_case__    ,						snake_case__ )    ,						provider=snake_case__    ,						sess_options=snake_case__ )
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ )
          # load model from hub
          else:
                 # download model
                 snake_case       :   Dict							=      hf_hub_download(
                     repo_id=snake_case__    ,						filename=snake_case__    ,						use_auth_token=snake_case__    ,						revision=snake_case__    ,						cache_dir=snake_case__    ,						force_download=snake_case__    ,						)
                 snake_case       :   List[Any]							=      Path(snake_case__ ).parent
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ ).name
                 snake_case       :   Dict							=      OnnxRuntimeModel.load_model(snake_case__    ,						provider=snake_case__    ,						sess_options=snake_case__ )
          return cls(model=snake_case__    ,						**snake_case__ )



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Optional[Any]    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						**snake_case__		:					Dict    ,						)		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      None
          if len(str(snake_case__ ).split("@" ) ) == 2:
                 snake_case				,					snake_case       :   int							=      model_id.split("@" )

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





