



import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()
def 	UpperCamelCase			(     snake_case__ :					int ,  snake_case__ :					List[str] ,  snake_case__ :					Optional[int]			)			->  Dict:
   UpperCamelCase					:						List[Any]					=			FNetConfig.from_json_file(__a			)
   print(F"""Building PyTorch model from configuration: {config}"""			)
   UpperCamelCase					:						Union[str, Any]					=			FNetForPreTraining(__a			)

   UpperCamelCase					:						int					=			restore_checkpoint(__a ,  __a			)
   UpperCamelCase					:						Any					=			checkpoint_dict['''target''']

   # Embeddings
   # Position IDs
   UpperCamelCase					:						str					=			fnet_pretraining_model.state_dict()

   UpperCamelCase					:						List[str]					=			state_dict['''fnet.embeddings.position_ids''']
   UpperCamelCase					:						int					=			{'''fnet.embeddings.position_ids''': position_ids}
   # Embedding Layers
   UpperCamelCase					:						List[Any]					=			torch.tensor(
       pretrained_model_params['encoder']['embedder']['word']['embedding']			)
   UpperCamelCase					:						Optional[int]					=			torch.tensor(
       pretrained_model_params['encoder']['embedder']['position']['embedding'][0]			)
   UpperCamelCase					:						str					=			torch.tensor(
       pretrained_model_params['encoder']['embedder']['type']['embedding']			)
   UpperCamelCase					:						List[str]					=			torch.tensor(
       pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']			).T
   UpperCamelCase					:						int					=			torch.tensor(
       pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']			)
   UpperCamelCase					:						Any					=			torch.tensor(
       pretrained_model_params['encoder']['embedder']['layer_norm']['scale']			)
   UpperCamelCase					:						Union[str, Any]					=			torch.tensor(
       pretrained_model_params['encoder']['embedder']['layer_norm']['bias']			)

   # Encoder Layers
   for layer in range(config.num_hidden_layers			):
      UpperCamelCase					:						int					=			torch.tensor(
          pretrained_model_params['encoder'][F"""encoder_{layer}"""]['mixing_layer_norm']['scale']			)
      UpperCamelCase					:						Union[str, Any]					=			torch.tensor(
          pretrained_model_params['encoder'][F"""encoder_{layer}"""]['mixing_layer_norm']['bias']			)

      UpperCamelCase					:						int					=			torch.tensor(
          pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['intermediate']['kernel']			).T
      UpperCamelCase					:						List[str]					=			torch.tensor(
          pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['intermediate']['bias']			)

      UpperCamelCase					:						Tuple					=			torch.tensor(
          pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['output']['kernel']			).T
      UpperCamelCase					:						Optional[Any]					=			torch.tensor(
          pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['output']['bias']			)

      UpperCamelCase					:						List[Any]					=			torch.tensor(
          pretrained_model_params['encoder'][F"""encoder_{layer}"""]['output_layer_norm']['scale']			)
      UpperCamelCase					:						Optional[int]					=			torch.tensor(
          pretrained_model_params['encoder'][F"""encoder_{layer}"""]['output_layer_norm']['bias']			)

   # Pooler Layers
   UpperCamelCase					:						Optional[int]					=			torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']			).T
   UpperCamelCase					:						List[Any]					=			torch.tensor(pretrained_model_params['encoder']['pooler']['bias']			)

   # Masked LM Layers
   UpperCamelCase					:						Optional[int]					=			torch.tensor(
       pretrained_model_params['predictions_dense']['kernel']			).T
   UpperCamelCase					:						Union[str, Any]					=			torch.tensor(
       pretrained_model_params['predictions_dense']['bias']			)
   UpperCamelCase					:						str					=			torch.tensor(
       pretrained_model_params['predictions_layer_norm']['scale']			)
   UpperCamelCase					:						int					=			torch.tensor(
       pretrained_model_params['predictions_layer_norm']['bias']			)
   UpperCamelCase					:						str					=			torch.tensor(
       pretrained_model_params['encoder']['embedder']['word']['embedding']			)
   UpperCamelCase					:						Union[str, Any]					=			torch.tensor(
       pretrained_model_params['predictions_output']['output_bias']			)
   UpperCamelCase					:						List[Any]					=			torch.tensor(pretrained_model_params['predictions_output']['output_bias']			)

   # Seq Relationship Layers
   UpperCamelCase					:						Union[str, Any]					=			torch.tensor(
       pretrained_model_params['classification']['output_kernel']			)
   UpperCamelCase					:						int					=			torch.tensor(
       pretrained_model_params['classification']['output_bias']			)

   # Load State Dict
   fnet_pretraining_model.load_state_dict(__a			)

   # Save PreTrained
   print(F"""Saving pretrained model to {save_path}"""			)
   fnet_pretraining_model.save_pretrained(__a			)


if __name__ == "__main__":
 __UpperCAmelCase      = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--fnet_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained FNet model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 __UpperCAmelCase      = parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)








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

from ...utils import logging


__UpperCAmelCase      = logging.get_logger(__name__)
def 	UpperCamelCase			(     snake_case__ :					List[str] ,  snake_case__ :					str			)			->  Dict:
   UpperCamelCase					:						int					=			nn.functional.normalize(snake_case__			)
   UpperCamelCase					:						Union[str, Any]					=			nn.functional.normalize(snake_case__			)
   return torch.mm(snake_case__ ,  normalized_text_embeds.t()			)



class        lowerCAmelCase_	(						a__							):
       UpperCAmelCase__       :      int       =						CLIPConfig

       UpperCAmelCase__       :      Union[str, Any]       =						["CLIPEncoderLayer"]
       def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	List[str]:
          super().__init__(SCREAMING_SNAKE_CASE_ )

          UpperCamelCase					:						int					=			CLIPVisionModel(config.vision_config )
          UpperCamelCase					:						int					=			nn.Linear(config.vision_config.hidden_size,    config.projection_dim,    bias=SCREAMING_SNAKE_CASE_ )

          UpperCamelCase					:						List[Any]					=			nn.Parameter(torch.ones(17,    config.projection_dim ),    requires_grad=SCREAMING_SNAKE_CASE_ )
          UpperCamelCase					:						str					=			nn.Parameter(torch.ones(3,    config.projection_dim ),    requires_grad=SCREAMING_SNAKE_CASE_ )

          UpperCamelCase					:						Dict					=			nn.Parameter(torch.ones(17 ),    requires_grad=SCREAMING_SNAKE_CASE_ )
          UpperCamelCase					:						Optional[Any]					=			nn.Parameter(torch.ones(3 ),    requires_grad=SCREAMING_SNAKE_CASE_ )
       @torch.no_grad()
       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Union[str, Any]:
          UpperCamelCase					:						Optional[int]					=			self.vision_model(SCREAMING_SNAKE_CASE_ )[1]  # pooled_output
          UpperCamelCase					:						int					=			self.visual_projection(SCREAMING_SNAKE_CASE_ )

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          UpperCamelCase					:						Optional[Any]					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.special_care_embeds ).cpu().float().numpy()
          UpperCamelCase					:						Any					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.concept_embeds ).cpu().float().numpy()

          UpperCamelCase					:						str					=			[]
          UpperCamelCase					:						int					=			image_embeds.shape[0]
          for i in range(SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase					:						Optional[int]					=			{'special_scores': {}, 'special_care': [], 'concept_scores': {}, 'bad_concepts': []}

             # increase this value to create a stronger `nfsw` filter
             # at the cost of increasing the possibility of filtering benign images
             UpperCamelCase					:						Optional[Any]					=			0.0

             for concept_idx in range(len(special_cos_dist[0] ) ):
                UpperCamelCase					:						Union[str, Any]					=			special_cos_dist[i][concept_idx]
                UpperCamelCase					:						Optional[Any]					=			self.special_care_embeds_weights[concept_idx].item()
                UpperCamelCase					:						str					=			round(concept_cos - concept_threshold + adjustment,    3 )
                if result_img["special_scores"][concept_idx] > 0:
                   result_img["special_care"].append({concept_idx, result_img['special_scores'][concept_idx]} )
                   UpperCamelCase					:						Dict					=			0.01

             for concept_idx in range(len(cos_dist[0] ) ):
                UpperCamelCase					:						List[Any]					=			cos_dist[i][concept_idx]
                UpperCamelCase					:						Optional[int]					=			self.concept_embeds_weights[concept_idx].item()
                UpperCamelCase					:						Union[str, Any]					=			round(concept_cos - concept_threshold + adjustment,    3 )
                if result_img["concept_scores"][concept_idx] > 0:
                   result_img["bad_concepts"].append(SCREAMING_SNAKE_CASE_ )

             result.append(SCREAMING_SNAKE_CASE_ )

          UpperCamelCase					:						int					=			[len(res['bad_concepts'] ) > 0 for res in result]

          for idx, has_nsfw_concept in enumerate(SCREAMING_SNAKE_CASE_ ):
             if has_nsfw_concept:
                if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) or torch.is_tensor(images[0] ):
                   UpperCamelCase					:						List[str]					=			torch.zeros_like(images[idx] )  # black image
                else:
                   UpperCamelCase					:						int					=			np.zeros(images[idx].shape )  # black image

          if any(SCREAMING_SNAKE_CASE_ ):
             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.' )

          return images, has_nsfw_concepts






       @torch.no_grad()
       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Optional[int]:
          UpperCamelCase					:						Union[str, Any]					=			self.vision_model(SCREAMING_SNAKE_CASE_ )[1]  # pooled_output
          UpperCamelCase					:						str					=			self.visual_projection(SCREAMING_SNAKE_CASE_ )

          UpperCamelCase					:						str					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.special_care_embeds )
          UpperCamelCase					:						Any					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.concept_embeds )

          # increase this value to create a stronger `nsfw` filter
          # at the cost of increasing the possibility of filtering benign images
          UpperCamelCase					:						Dict					=			0.0

          UpperCamelCase					:						str					=			special_cos_dist - self.special_care_embeds_weights + adjustment
          # special_scores = special_scores.round(decimals=3)
          UpperCamelCase					:						List[str]					=			torch.any(special_scores > 0,    dim=1 )
          UpperCamelCase					:						List[Any]					=			special_care * 0.01
          UpperCamelCase					:						List[Any]					=			special_adjustment.unsqueeze(1 ).expand(-1,    cos_dist.shape[1] )

          UpperCamelCase					:						List[str]					=			(cos_dist - self.concept_embeds_weights) + special_adjustment
          # concept_scores = concept_scores.round(decimals=3)
          UpperCamelCase					:						Tuple					=			torch.any(concept_scores > 0,    dim=1 )

          UpperCamelCase					:						Any					=			0.0  # black image

          return images, has_nsfw_concepts



