





"""simple docstring"""
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      __lowerCamelCase   (							a_     :		Optional[Any] ,   a_     :		Any ,   a_     :		List[str]=1e-12							)			->      List[Any]:
      __SCREAMING_SNAKE_CASE   :Any						=  jnp.divide(emb_a.T ,   jnp.clip(jnp.linalg.norm(a_ ,   axis=1							) ,   a_min=a_							)							).T
      __SCREAMING_SNAKE_CASE   :Tuple						=  jnp.divide(emb_a.T ,   jnp.clip(jnp.linalg.norm(a_ ,   axis=1							) ,   a_min=a_							)							).T
      return jnp.matmul(a_ ,   norm_emb_a.T							)






class 					_SCREAMING_SNAKE_CASE(   nn.Module  ):
       SCREAMING_SNAKE_CASE_   :      CLIPConfig
       SCREAMING_SNAKE_CASE_   :      jnp.dtype							=  jnp.floataa




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[Any]						=  FlaxCLIPVisionModule(self.config.vision_config							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  nn.Dense(self.config.projection_dim		,use_bias=SCREAMING_SNAKE_CASE__		,dtype=self.dtype							)

             __SCREAMING_SNAKE_CASE   :List[Any]						=  self.param('''concept_embeds'''		,jax.nn.initializers.ones		,(17, self.config.projection_dim)							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  self.param(
                 '''special_care_embeds'''		,jax.nn.initializers.ones		,(3, self.config.projection_dim)							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.param('''concept_embeds_weights'''		,jax.nn.initializers.ones		,(17,)							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.param('''special_care_embeds_weights'''		,jax.nn.initializers.ones		,(3,)							)




       def __call__(						self		,SCREAMING_SNAKE_CASE__							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.vision_model(SCREAMING_SNAKE_CASE__							)[1]
             __SCREAMING_SNAKE_CASE   :str						=  self.visual_projection(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  jax_cosine_distance(SCREAMING_SNAKE_CASE__		,self.special_care_embeds							)
             __SCREAMING_SNAKE_CASE   :str						=  jax_cosine_distance(SCREAMING_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
             __SCREAMING_SNAKE_CASE   :List[Any]						=  0.0

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  jnp.round(SCREAMING_SNAKE_CASE__		,3							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  jnp.any(special_scores > 0		,axis=1		,keepdims=SCREAMING_SNAKE_CASE__							)
             # Use a lower threshold if an image has any special care concept
             __SCREAMING_SNAKE_CASE   :Any						=  is_special_care * 0.0_1

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

             return has_nsfw_concepts






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Union[str, Any]							=  CLIPConfig
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  '''clip_input'''
       SCREAMING_SNAKE_CASE_   :      Any							=  FlaxStableDiffusionSafetyCheckerModule




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = jnp.floataa		,SCREAMING_SNAKE_CASE__ = True		,**SCREAMING_SNAKE_CASE__		,)			->      str:





             """simple docstring"""






             if input_shape is None:
                   __SCREAMING_SNAKE_CASE   :int						=  (1, 2_24, 2_24, 3)
             __SCREAMING_SNAKE_CASE   :str						=  self.module_class(config=SCREAMING_SNAKE_CASE__		,dtype=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)
             super().__init__(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,input_shape=SCREAMING_SNAKE_CASE__		,seed=SCREAMING_SNAKE_CASE__		,dtype=SCREAMING_SNAKE_CASE__		,_do_init=_do_init							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      FrozenDict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  jax.random.normal(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Optional[Any]						=  jax.random.split(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :str						=  {'''params''': params_rng, '''dropout''': dropout_rng}

             __SCREAMING_SNAKE_CASE   :List[Any]						=  self.module.init(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)['''params''']

             return random_params




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  jnp.transpose(SCREAMING_SNAKE_CASE__		,(0, 2, 3, 1)							)

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






"""simple docstring"""
import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def      __lowerCamelCase   (							a_     :		str ,   a_     :		Dict ,   a_     :		Any ,   a_     :		str							)			->      str:
      __SCREAMING_SNAKE_CASE   :int						=  s.rsplit(a_ ,   a_							)
      return new.join(a_							)



def      __lowerCamelCase   (							a_     :		List[str]							)			->      Dict:
      # encoder.embeddings are double copied in original FLAVA
      return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()							)



def      __lowerCamelCase   (							a_     :		Optional[int]							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  {}

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
      for key, value in state_dict.items():
            for group_key in group_keys:
                  if group_key in key:
                        __SCREAMING_SNAKE_CASE   :Optional[Any]						=  key.replace(f'''{group_key}.''' ,   f'''{group_key}.group.'''							)

            if "res_path" in key:
                  __SCREAMING_SNAKE_CASE   :str						=  key.replace('''res_path.''' ,   '''res_path.path.'''							)

            if key.endswith('''.w'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.w''' ,   '''.weight''' ,   1							)
            if key.endswith('''.b'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.b''' ,   '''.bias''' ,   1							)

            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  value.float()

      return upgrade



@torch.no_grad()
def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Optional[int] ,   a_     :		Optional[int]=None ,   a_     :		Dict=True							)			->      Union[str, Any]:
      from dall_e import Encoder

      __SCREAMING_SNAKE_CASE   :int						=  Encoder()
      if os.path.exists(a_							):
            __SCREAMING_SNAKE_CASE   :Dict						=  torch.load(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :List[str]						=  torch.hub.load_state_dict_from_url(a_							)

      if isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  ckpt.state_dict()
      encoder.load_state_dict(a_							)

      if config_path is not None:
            __SCREAMING_SNAKE_CASE   :Any						=  FlavaImageCodebookConfig.from_pretrained(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  FlavaImageCodebookConfig()

      __SCREAMING_SNAKE_CASE   :Tuple						=  FlavaImageCodebook(a_							).eval()
      __SCREAMING_SNAKE_CASE   :List[str]						=  encoder.state_dict()

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  upgrade_state_dict(a_							)
      hf_model.load_state_dict(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  hf_model.state_dict()
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  count_parameters(a_							)
      __SCREAMING_SNAKE_CASE   :Any						=  count_parameters(a_							)

      assert torch.allclose(a_ ,   a_ ,   atol=1e-3							)

      if save_checkpoint:
            hf_model.save_pretrained(a_							)
      else:
            return hf_state_dict


if __name__ == "__main__":
  lowerCamelCase_								=		argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  lowerCamelCase_								=		parser.parse_args()

  convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)