





import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					__magic_name__  (							lowerCamelCase__    ,				unittest.TestCase							):





     """simple docstring"""



     __UpperCamelCase					    =     HerbertTokenizer
     __UpperCamelCase					    =     HerbertTokenizerFast
     __UpperCamelCase					    =     True



     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        super().setUp()

        # Use a simpler test file without japanese/chinese characters
        with open(f"{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"			,      encoding="utf-8"  ) as f_data:
           A_					:				List[Any]          =			f_data.read().replace("\n\n"			,      "\n"  ).strip()

        A_					:				Tuple          =			[
            "<s>",
            "</s>",
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "w</w>",
            "r</w>",
            "t</w>",
            "lo",
            "low",
            "er</w>",
            "low</w>",
            "lowest</w>",
            "newer</w>",
            "wider</w>",
            ",</w>",
            "<unk>",
        ]
        A_					:				Tuple          =			dict(zip(snake_case			,      range(len(snake_case  )  )  )  )
        A_					:				List[Any]          =			["l o 123", "lo w 1456", "e r</w> 1789", ""]

        A_					:				Optional[int]          =			os.path.join(self.tmpdirname			,      VOCAB_FILES_NAMES["vocab_file"]  )
        A_					:				Optional[int]          =			os.path.join(self.tmpdirname			,      VOCAB_FILES_NAMES["merges_file"]  )
        with open(self.vocab_file			,      "w"  ) as fp:
           fp.write(json.dumps(snake_case  )  )
        with open(self.merges_file			,      "w"  ) as fp:
           fp.write("\n".join(snake_case  )  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      snake_case       :List[Any]  ):





        '''simple docstring'''




        A_					:				List[str]          =			"lower newer"
        A_					:				List[Any]          =			"lower newer"
        return input_text, output_text



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			self.tokenizer_class(vocab_file=self.vocab_file			,      merges_file=self.merges_file  )

        A_					:				List[str]          =			"lower"
        A_					:				Tuple          =			["low", "er</w>"]
        A_					:				Any          =			tokenizer.tokenize(snake_case  )
        self.assertListEqual(snake_case			,      snake_case  )

        A_					:				Any          =			tokens + ["<unk>"]
        A_					:				Union[str, Any]          =			[16, 17, 23]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case  )			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :int  ):





        '''simple docstring'''




        if not self.test_rust_tokenizer:
           return

        A_					:				List[str]          =			self.get_tokenizer()
        A_					:				List[str]          =			self.get_rust_tokenizer()

        A_					:				int          =			"lower,newer"

        A_					:				Optional[int]          =			tokenizer.tokenize(snake_case  )
        A_					:				Optional[int]          =			rust_tokenizer.tokenize(snake_case  )
        self.assertListEqual(snake_case			,      snake_case  )

        A_					:				List[Any]          =			tokenizer.encode(snake_case			,      add_special_tokens=snake_case  )
        A_					:				List[str]          =			rust_tokenizer.encode(snake_case			,      add_special_tokens=snake_case  )
        self.assertListEqual(snake_case			,      snake_case  )

        A_					:				Tuple          =			self.get_rust_tokenizer()
        A_					:				Dict          =			tokenizer.encode(snake_case  )
        A_					:				int          =			rust_tokenizer.encode(snake_case  )
        self.assertListEqual(snake_case			,      snake_case  )





     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			self.tokenizer_class.from_pretrained("allegro/herbert-base-cased"  )

        A_					:				List[str]          =			tokenizer.encode("konstruowanie sekwencji"			,      add_special_tokens=snake_case  )
        A_					:				Optional[Any]          =			tokenizer.encode("konstruowanie wielu sekwencji"			,      add_special_tokens=snake_case  )

        A_					:				Optional[Any]          =			tokenizer.build_inputs_with_special_tokens(snake_case  )
        A_					:				Any          =			tokenizer.build_inputs_with_special_tokens(snake_case			,      snake_case  )

        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + text_a + [2]







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						(					_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : List[str]				,				_lowerCAmelCase : List[Any]	) ->						Any:
   # Initialise PyTorch model
   A_					:				Tuple          =			FNetConfig.from_json_file(_lowerCAmelCase	)
   print(f"Building PyTorch model from configuration: {config}"	)
   A_					:				Tuple          =			FNetForPreTraining(_lowerCAmelCase	)

   A_					:				List[str]          =			restore_checkpoint(_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Any          =			checkpoint_dict["target"]

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

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

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

      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"]	).T
      A_					:				List[Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"]	)

      A_					:				Optional[int]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["kernel"]	).T
      A_					:				int          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["bias"]	)

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

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

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

   # Seq Relationship Layers
   A_					:				str          =			torch.tensor(
       pretrained_model_params["classification"]["output_kernel"]	)
   A_					:				str          =			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__":
 _lowerCAmelCase							:      Tuple						=	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							:      Optional[int]						=	parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
