




'''simple docstring'''



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 (_lowerCamelCase							:     int   ,	_lowerCamelCase							:     Tuple   ,	_lowerCamelCase							:     Any	)->   Union[str, Any]:




     '''simple docstring'''





     __snake_case    					=  FNetConfig.from_json_file(_lowerCamelCase	)
     print(f'''Building PyTorch model from configuration: {config}'''	)
     __snake_case    					=  FNetForPreTraining(_lowerCamelCase	)

     __snake_case    					=  restore_checkpoint(_lowerCamelCase   ,	_lowerCamelCase	)
     __snake_case    					=  checkpoint_dict['''target''']

     # Embeddings
     # Position IDs
     __snake_case    					=  fnet_pretraining_model.state_dict()

     __snake_case    					=  state_dict['''fnet.embeddings.position_ids''']
     __snake_case    					=  {'''fnet.embeddings.position_ids''': position_ids}
     # Embedding Layers
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']	)
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]	)
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']	)
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']	).T
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']	)
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']	)
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']	)

     # Encoder Layers
     for layer in range(config.num_hidden_layers	):
          __snake_case    					=  torch.tensor(
              pretrained_model_params['''encoder'''][f'''encoder_{layer}''']['''mixing_layer_norm''']['''scale''']	)
          __snake_case    					=  torch.tensor(
              pretrained_model_params['''encoder'''][f'''encoder_{layer}''']['''mixing_layer_norm''']['''bias''']	)

          __snake_case    					=  torch.tensor(
              pretrained_model_params['''encoder'''][f'''feed_forward_{layer}''']['''intermediate''']['''kernel''']	).T
          __snake_case    					=  torch.tensor(
              pretrained_model_params['''encoder'''][f'''feed_forward_{layer}''']['''intermediate''']['''bias''']	)

          __snake_case    					=  torch.tensor(
              pretrained_model_params['''encoder'''][f'''feed_forward_{layer}''']['''output''']['''kernel''']	).T
          __snake_case    					=  torch.tensor(
              pretrained_model_params['''encoder'''][f'''feed_forward_{layer}''']['''output''']['''bias''']	)

          __snake_case    					=  torch.tensor(
              pretrained_model_params['''encoder'''][f'''encoder_{layer}''']['''output_layer_norm''']['''scale''']	)
          __snake_case    					=  torch.tensor(
              pretrained_model_params['''encoder'''][f'''encoder_{layer}''']['''output_layer_norm''']['''bias''']	)

     # Pooler Layers
     __snake_case    					=  torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']	).T
     __snake_case    					=  torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']	)

     # Masked LM Layers
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''predictions_dense''']['''kernel''']	).T
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''predictions_dense''']['''bias''']	)
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''predictions_layer_norm''']['''scale''']	)
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''predictions_layer_norm''']['''bias''']	)
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']	)
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''predictions_output''']['''output_bias''']	)
     __snake_case    					=  torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']	)

     # Seq Relationship Layers
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''classification''']['''output_kernel''']	)
     __snake_case    					=  torch.tensor(
         pretrained_model_params['''classification''']['''output_bias''']	)

     # Load State Dict
     fnet_pretraining_model.load_state_dict(_lowerCamelCase	)

     # Save PreTrained
     print(f'''Saving pretrained model to {save_path}'''	)
     fnet_pretraining_model.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
     UpperCAmelCase_       :  Optional[Any]      =		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_       :  Dict      =		parser.parse_args()
     convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)












'''simple docstring'''



from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    '''cross_attention''',
    '''0.20.0''',
    '''Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.''',
    standard_warn=False,
)


UpperCAmelCase_       :  Optional[int]      =		AttentionProcessor





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Any:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Tuple:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[Any]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Tuple:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  int:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  List[str]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )






