





"""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						(      __A ,__A ,__A    )		->				int:
   # Initialise PyTorch model
   lowercase    :			int  =       FNetConfig.from_json_file(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )
   lowercase    :			Optional[int]  =       FNetForPreTraining(__A    )

   lowercase    :			List[str]  =       restore_checkpoint(__A ,__A    )
   lowercase    :			str  =       checkpoint_dict["""target"""]

   # Embeddings
   # Position IDs
   lowercase    :			Optional[Any]  =       fnet_pretraining_model.state_dict()

   lowercase    :			Optional[int]  =       state_dict["""fnet.embeddings.position_ids"""]
   lowercase    :			Any  =       {"""fnet.embeddings.position_ids""": position_ids}
   # Embedding Layers
   lowercase    :			Union[str, Any]  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]    )
   lowercase    :			Any  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]    )
   lowercase    :			Union[str, Any]  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]    )
   lowercase    :			Dict  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]    ).T
   lowercase    :			Tuple  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]    )
   lowercase    :			Union[str, Any]  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]    )
   lowercase    :			Optional[int]  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]    )

   # Encoder Layers
   for layer in range(config.num_hidden_layers    ):
      lowercase    :			List[Any]  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]    )
      lowercase    :			Tuple  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]    )

      lowercase    :			Dict  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]    ).T
      lowercase    :			Union[str, Any]  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]    )

      lowercase    :			List[Any]  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""kernel"""]    ).T
      lowercase    :			str  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""bias"""]    )

      lowercase    :			Tuple  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]    )
      lowercase    :			Any  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]    )

   # Pooler Layers
   lowercase    :			Optional[Any]  =       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]    ).T
   lowercase    :			Optional[int]  =       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]    )

   # Masked LM Layers
   lowercase    :			List[Any]  =       torch.tensor(
       pretrained_model_params["""predictions_dense"""]["""kernel"""]    ).T
   lowercase    :			str  =       torch.tensor(
       pretrained_model_params["""predictions_dense"""]["""bias"""]    )
   lowercase    :			str  =       torch.tensor(
       pretrained_model_params["""predictions_layer_norm"""]["""scale"""]    )
   lowercase    :			Any  =       torch.tensor(
       pretrained_model_params["""predictions_layer_norm"""]["""bias"""]    )
   lowercase    :			Dict  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]    )
   lowercase    :			List[str]  =       torch.tensor(
       pretrained_model_params["""predictions_output"""]["""output_bias"""]    )
   lowercase    :			Optional[Any]  =       torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]    )

   # Seq Relationship Layers
   lowercase    :			int  =       torch.tensor(
       pretrained_model_params["""classification"""]["""output_kernel"""]    )
   lowercase    :			List[Any]  =       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__":
   lowerCAmelCase:	int							      =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.")
   lowerCAmelCase:	List[str]							      =parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)







"""simple docstring"""
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""MarkupLMFeatureExtractor"""
   __UpperCAmelCase        =					("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
   __UpperCAmelCase        =					True



   def __call__(       self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case = True      ,   snake_case = False      ,   snake_case = None      ,   snake_case = None      ,   snake_case = 0      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = True      ,   snake_case = None      ,   **snake_case      ,   ) ->    BatchEncoding:







      """simple docstring"""
      # first, create nodes and xpaths
      if self.parse_html:
         if html_strings is None:
            raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""	)

         if nodes is not None or xpaths is not None or node_labels is not None:
            raise ValueError(
                """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""	)

         lowercase    :			List[Any]  =       self.feature_extractor(snake_case	)
         lowercase    :			Union[str, Any]  =       features["""nodes"""]
         lowercase    :			List[Any]  =       features["""xpaths"""]
      else:
         if html_strings is not None:
            raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""	)
         if nodes is None or xpaths is None:
            raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""	)

        # # second, apply the tokenizer
      if questions is not None and self.parse_html:
         if isinstance(snake_case      ,   snake_case	):
            lowercase    :			Dict  =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

      lowercase    :			Dict  =       self.tokenizer(
          text=questions if questions is not None else nodes      ,   text_pair=nodes if questions is not None else None      ,   xpaths=snake_case      ,   node_labels=snake_case      ,   add_special_tokens=snake_case      ,   padding=snake_case      ,   truncation=snake_case      ,   max_length=snake_case      ,   stride=snake_case      ,   pad_to_multiple_of=snake_case      ,   return_token_type_ids=snake_case      ,   return_attention_mask=snake_case      ,   return_overflowing_tokens=snake_case      ,   return_special_tokens_mask=snake_case      ,   return_offsets_mapping=snake_case      ,   return_length=snake_case      ,   verbose=snake_case      ,   return_tensors=snake_case      ,   **snake_case      ,   )

      return encoded_inputs



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Optional[Any]:







      """simple docstring"""
      return self.tokenizer.batch_decode(*snake_case      ,   **snake_case	)



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Tuple:







      """simple docstring"""
      return self.tokenizer.decode(*snake_case      ,   **snake_case	)





   @property
   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      lowercase    :			Any  =       self.tokenizer.model_input_names
      return tokenizer_input_names


