code: 
  
  
  """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 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				int      =		FNetConfig.from_json_file(__snake_case      )
     print(F'Building PyTorch model from configuration: {config}'      )
     _lowerCamelCase  :				Optional[Any]      =		FNetForPreTraining(__snake_case      )
  
     _lowerCamelCase  :				Tuple      =		restore_checkpoint(__snake_case	,   __snake_case      )
     _lowerCamelCase  :				List[Any]      =		checkpoint_dict["""target"""]
  
     # Embeddings
     # Position IDs
     _lowerCamelCase  :				Optional[int]      =		fnet_pretraining_model.state_dict()
  
     _lowerCamelCase  :				Dict      =		state_dict["""fnet.embeddings.position_ids"""]
     _lowerCamelCase  :				Dict      =		{"""fnet.embeddings.position_ids""": position_ids}
     # Embedding Layers
     _lowerCamelCase  :				Optional[Any]      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
     _lowerCamelCase  :				Optional[int]      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]      )
     _lowerCamelCase  :				str      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]      )
     _lowerCamelCase  :				Dict      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]      ).T
     _lowerCamelCase  :				List[str]      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]      )
     _lowerCamelCase  :				str      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]      )
     _lowerCamelCase  :				str      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]      )
  
     # Encoder Layers
     for layer in range(config.num_hidden_layers      ):
        _lowerCamelCase  :				int      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'encoder_{layer}']["""mixing_layer_norm"""]["""scale"""]      )
        _lowerCamelCase  :				Optional[Any]      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'encoder_{layer}']["""mixing_layer_norm"""]["""bias"""]      )
  
        _lowerCamelCase  :				Union[str, Any]      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""intermediate"""]["""kernel"""]      ).T
        _lowerCamelCase  :				Union[str, Any]      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""intermediate"""]["""bias"""]      )
  
        _lowerCamelCase  :				List[str]      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""output"""]["""kernel"""]      ).T
        _lowerCamelCase  :				List[str]      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""output"""]["""bias"""]      )
  
        _lowerCamelCase  :				str      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'encoder_{layer}']["""output_layer_norm"""]["""scale"""]      )
        _lowerCamelCase  :				Dict      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'encoder_{layer}']["""output_layer_norm"""]["""bias"""]      )
  
     # Pooler Layers
     _lowerCamelCase  :				Union[str, Any]      =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]      ).T
     _lowerCamelCase  :				str      =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]      )
  
     # Masked LM Layers
     _lowerCamelCase  :				Dict      =		torch.tensor(
         pretrained_model_params["""predictions_dense"""]["""kernel"""]      ).T
     _lowerCamelCase  :				List[str]      =		torch.tensor(
         pretrained_model_params["""predictions_dense"""]["""bias"""]      )
     _lowerCamelCase  :				Tuple      =		torch.tensor(
         pretrained_model_params["""predictions_layer_norm"""]["""scale"""]      )
     _lowerCamelCase  :				Optional[Any]      =		torch.tensor(
         pretrained_model_params["""predictions_layer_norm"""]["""bias"""]      )
     _lowerCamelCase  :				Optional[Any]      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
     _lowerCamelCase  :				Tuple      =		torch.tensor(
         pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
     _lowerCamelCase  :				int      =		torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
  
     # Seq Relationship Layers
     _lowerCamelCase  :				Tuple      =		torch.tensor(
         pretrained_model_params["""classification"""]["""output_kernel"""]      )
     _lowerCamelCase  :				int      =		torch.tensor(
         pretrained_model_params["""classification"""]["""output_bias"""]      )
  
     # Load State Dict
     fnet_pretraining_model.load_state_dict(__snake_case      )
  
     # Save PreTrained
     print(F'Saving pretrained model to {save_path}'      )
     fnet_pretraining_model.save_pretrained(__snake_case      )
  
  
  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)
  
  
style_context: 
  
  
  """simple docstring"""
  from typing import List, Optional, Tuple
  
  from tokenizers import pre_tokenizers
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_clip import CLIPTokenizer
  
  
  UpperCAmelCase   =	logging.get_logger(__name__)
  
  UpperCAmelCase   =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
  
  UpperCAmelCase   =	{
      """vocab_file""": {
          """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
      },
      """merges_file""": {
          """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
      },
      """tokenizer_file""": {
          """openai/clip-vit-base-patch32""": (
              """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
          ),
      },
  }
  
  UpperCAmelCase   =	{
      """openai/clip-vit-base-patch32""": 77,
  }
  
  
  
  
  class   lowercase__							(				A_							):
     __UpperCAmelCase       					=				VOCAB_FILES_NAMES
     __UpperCAmelCase       					=				PRETRAINED_VOCAB_FILES_MAP
     __UpperCAmelCase       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCAmelCase       					=				['''input_ids''', '''attention_mask''']
     __UpperCAmelCase       					=				CLIPTokenizer
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      SCREAMING_SNAKE_CASE="<|startoftext|>"			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      **SCREAMING_SNAKE_CASE			,      )     ->     List[Any]:
        super().__init__(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      tokenizer_file=SCREAMING_SNAKE_CASE			,      unk_token=SCREAMING_SNAKE_CASE			,      bos_token=SCREAMING_SNAKE_CASE			,      eos_token=SCREAMING_SNAKE_CASE			,      pad_token=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE			,      )
  
        if not isinstance(self.backend_tokenizer.pre_tokenizer			,      pre_tokenizers.Sequence):
           raise ValueError(
               """The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"""
               """ heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"""
               """ to be compatible with this version.The easiest way to do so is"""
               """ `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"""
               """ to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"""
               """ transformers.""")
  
        self._wrap_decode_method_backend_tokenizer()
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				str      =		self.backend_tokenizer.decode
  
        def new_decode_method(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Any      =		orig_decode_method(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Optional[int]      =		text.replace(self.backend_tokenizer.model.end_of_word_suffix			,      """ """).strip()
           return text
  
        _lowerCamelCase  :				List[str]      =		new_decode_method
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     List[int]:
        _lowerCamelCase  :				str      =		[self.bos_token_id]
        _lowerCamelCase  :				Union[str, Any]      =		[self.eos_token_id]
  
        if token_ids_a is None:
           return bos_token + token_ids_a + eos_token
        return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     List[int]:
        _lowerCamelCase  :				Any      =		[self.bos_token_id]
        _lowerCamelCase  :				int      =		[self.eos_token_id]
  
        if token_ids_a is None:
           return len(bos_token + token_ids_a + eos_token) * [0]
        return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token) * [0]
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     Tuple[str]:
        _lowerCamelCase  :				Dict      =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE			,      name=SCREAMING_SNAKE_CASE)
        return tuple(SCREAMING_SNAKE_CASE)
  
  