




import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class  __lowerCamelCase       (    UpperCamelCase__	,				unittest.TestCase    ):




    """simple docstring"""

    snake_case__					=      RealmTokenizer
    snake_case__					=      RealmTokenizerFast
    snake_case__					=      True
    snake_case__					=      True
    snake_case__					=      filter_non_english


    def    a      (       self     :     int   )      ->       str:
         super().setUp()

         lowerCAmelCase__		= [
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "[PAD]",
             "[MASK]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         lowerCAmelCase__		= os.path.join(self.tmpdirname   ,	VOCAB_FILES_NAMES["vocab_file"]   )
         with open(self.vocab_file   ,	"w"   ,	encoding="utf-8"   ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )


    def    a      (       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     List[str]   )      ->       int:
         lowerCAmelCase__		= "UNwant\u00E9d,running"
         lowerCAmelCase__		= "unwanted, running"
         return input_text, output_text


    def    a      (       self     :     Dict   )      ->       int:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= tokenizer.tokenize("UNwant\u00E9d,running"   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	["un", "##want", "##ed", ",", "runn", "##ing"]   )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )   ,	[9, 6, 7, 12, 10, 11]   )


    def    a      (       self     :     Any   )      ->       Any:
         if not self.test_rust_tokenizer:
              return

         lowerCAmelCase__		= self.get_tokenizer()
         lowerCAmelCase__		= self.get_rust_tokenizer()

         lowerCAmelCase__		= "UNwant\u00E9d,running"

         lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= tokenizer.encode(SCREAMING_SNAKE_CASE__   ,	add_special_tokens=SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= rust_tokenizer.encode(SCREAMING_SNAKE_CASE__   ,	add_special_tokens=SCREAMING_SNAKE_CASE__   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= self.get_rust_tokenizer()
         lowerCAmelCase__		= tokenizer.encode(SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= rust_tokenizer.encode(SCREAMING_SNAKE_CASE__   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )

         # With lower casing
         lowerCAmelCase__		= self.get_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= self.get_rust_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= "UNwant\u00E9d,running"

         lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= tokenizer.encode(SCREAMING_SNAKE_CASE__   ,	add_special_tokens=SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= rust_tokenizer.encode(SCREAMING_SNAKE_CASE__   ,	add_special_tokens=SCREAMING_SNAKE_CASE__   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= self.get_rust_tokenizer()
         lowerCAmelCase__		= tokenizer.encode(SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= rust_tokenizer.encode(SCREAMING_SNAKE_CASE__   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     Tuple   )      ->       List[str]:
         lowerCAmelCase__		= BasicTokenizer()

         self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"   )   ,	["ah", "\u535A", "\u63A8", "zz"]   )


    def    a      (       self     :     int   )      ->       List[str]:
         lowerCAmelCase__		= BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__   )

         self.assertListEqual(
             tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "   )   ,	["hello", "!", "how", "are", "you", "?"]   )
         self.assertListEqual(tokenizer.tokenize("H\u00E9llo"   )   ,	["hello"]   )


    def    a      (       self     :     Dict   )      ->       Optional[int]:
         lowerCAmelCase__		= BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__   ,	strip_accents=SCREAMING_SNAKE_CASE__   )

         self.assertListEqual(
             tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "   )   ,	["hällo", "!", "how", "are", "you", "?"]   )
         self.assertListEqual(tokenizer.tokenize("H\u00E9llo"   )   ,	["h\u00E9llo"]   )


    def    a      (       self     :     Dict   )      ->       List[Any]:
         lowerCAmelCase__		= BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__   ,	strip_accents=SCREAMING_SNAKE_CASE__   )

         self.assertListEqual(
             tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "   )   ,	["hallo", "!", "how", "are", "you", "?"]   )
         self.assertListEqual(tokenizer.tokenize("H\u00E9llo"   )   ,	["hello"]   )


    def    a      (       self     :     Dict   )      ->       Optional[int]:
         lowerCAmelCase__		= BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__   )

         self.assertListEqual(
             tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "   )   ,	["hallo", "!", "how", "are", "you", "?"]   )
         self.assertListEqual(tokenizer.tokenize("H\u00E9llo"   )   ,	["hello"]   )


    def    a      (       self     :     int   )      ->       List[str]:
         lowerCAmelCase__		= BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__   )

         self.assertListEqual(
             tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "   )   ,	["HeLLo", "!", "how", "Are", "yoU", "?"]   )


    def    a      (       self     :     List[Any]   )      ->       Optional[Any]:
         lowerCAmelCase__		= BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__   ,	strip_accents=SCREAMING_SNAKE_CASE__   )

         self.assertListEqual(
             tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "   )   ,	["HäLLo", "!", "how", "Are", "yoU", "?"]   )


    def    a      (       self     :     List[str]   )      ->       Optional[Any]:
         lowerCAmelCase__		= BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__   ,	strip_accents=SCREAMING_SNAKE_CASE__   )

         self.assertListEqual(
             tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "   )   ,	["HaLLo", "!", "how", "Are", "yoU", "?"]   )


    def    a      (       self     :     List[str]   )      ->       List[Any]:
         lowerCAmelCase__		= BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__   ,	never_split=["[UNK]"]   )

         self.assertListEqual(
             tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"   )   ,	["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]   )


    def    a      (       self     :     int   )      ->       Any:
         lowerCAmelCase__		= ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

         lowerCAmelCase__		= {}
         for i, token in enumerate(SCREAMING_SNAKE_CASE__   ):
              lowerCAmelCase__		= i
         lowerCAmelCase__		= WordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE__   ,	unk_token="[UNK]"   )

         self.assertListEqual(tokenizer.tokenize(""   )   ,	[]   )

         self.assertListEqual(tokenizer.tokenize("unwanted running"   )   ,	["un", "##want", "##ed", "runn", "##ing"]   )

         self.assertListEqual(tokenizer.tokenize("unwantedX running"   )   ,	["[UNK]", "runn", "##ing"]   )


    def    a      (       self     :     Dict   )      ->       int:
         self.assertTrue(_is_whitespace(" "   )   )
         self.assertTrue(_is_whitespace("\t"   )   )
         self.assertTrue(_is_whitespace("\r"   )   )
         self.assertTrue(_is_whitespace("\n"   )   )
         self.assertTrue(_is_whitespace("\u00A0"   )   )

         self.assertFalse(_is_whitespace("A"   )   )
         self.assertFalse(_is_whitespace("-"   )   )


    def    a      (       self     :     Any   )      ->       Dict:
         self.assertTrue(_is_control("\u0005"   )   )

         self.assertFalse(_is_control("A"   )   )
         self.assertFalse(_is_control(" "   )   )
         self.assertFalse(_is_control("\t"   )   )
         self.assertFalse(_is_control("\r"   )   )


    def    a      (       self     :     int   )      ->       List[str]:
         self.assertTrue(_is_punctuation("-"   )   )
         self.assertTrue(_is_punctuation("$"   )   )
         self.assertTrue(_is_punctuation("`"   )   )
         self.assertTrue(_is_punctuation("."   )   )

         self.assertFalse(_is_punctuation("A"   )   )
         self.assertFalse(_is_punctuation(" "   )   )


    def    a      (       self     :     str   )      ->       str:
         lowerCAmelCase__		= self.get_tokenizer()

         # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
         self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE__   ) for t in ["Test", "\xad", "test"]]   ,	[["[UNK]"], [], ["[UNK]"]]   )

         if self.test_rust_tokenizer:
              lowerCAmelCase__		= self.get_rust_tokenizer()
              self.assertListEqual(
                  [rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__   ) for t in ["Test", "\xad", "test"]]   ,	[["[UNK]"], [], ["[UNK]"]]   )


    @slow
    def    a      (       self     :     Optional[Any]   )      ->       Optional[int]:
         lowerCAmelCase__		= self.tokenizer_class.from_pretrained("bert-base-uncased"   )

         lowerCAmelCase__		= tokenizer.encode("sequence builders"   ,	add_special_tokens=SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= tokenizer.encode("multi-sequence build"   ,	add_special_tokens=SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )

         assert encoded_sentence == [101] + text + [102]
         assert encoded_pair == [101] + text + [102] + text_a + [102]


    def    a      (       self     :     Any   )      ->       Dict:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
                   lowerCAmelCase__		= self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,	**SCREAMING_SNAKE_CASE__   )

                   lowerCAmelCase__		= f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
                   lowerCAmelCase__		= tokenizer_r.encode_plus(
                       SCREAMING_SNAKE_CASE__   ,	return_attention_mask=SCREAMING_SNAKE_CASE__   ,	return_token_type_ids=SCREAMING_SNAKE_CASE__   ,	return_offsets_mapping=SCREAMING_SNAKE_CASE__   ,	add_special_tokens=SCREAMING_SNAKE_CASE__   ,	)

                   lowerCAmelCase__		= tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE__   ,	"do_lower_case"   ) else False
                   lowerCAmelCase__		= (
                       [
                           ((0, 0), tokenizer_r.cls_token),
                           ((0, 1), "A"),
                           ((1, 2), ","),
                           ((3, 5), "na"),
                           ((5, 6), "##ï"),
                           ((6, 8), "##ve"),
                           ((9, 15), tokenizer_r.mask_token),
                           ((16, 21), "Allen"),
                           ((21, 23), "##NL"),
                           ((23, 24), "##P"),
                           ((25, 33), "sentence"),
                           ((33, 34), "."),
                           ((0, 0), tokenizer_r.sep_token),
                       ]
                       if not do_lower_case
                       else [
                           ((0, 0), tokenizer_r.cls_token),
                           ((0, 1), "a"),
                           ((1, 2), ","),
                           ((3, 8), "naive"),
                           ((9, 15), tokenizer_r.mask_token),
                           ((16, 21), "allen"),
                           ((21, 23), "##nl"),
                           ((23, 24), "##p"),
                           ((25, 33), "sentence"),
                           ((33, 34), "."),
                           ((0, 0), tokenizer_r.sep_token),
                       ]
                   )

                   self.assertEqual(
                       [e[1] for e in expected_results]   ,	tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]   )   )
                   self.assertEqual([e[0] for e in expected_results]   ,	tokens["offset_mapping"]   )


    @slow
    def    a      (       self     :     List[str]   )      ->       int:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
                   lowerCAmelCase__		= self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,	**SCREAMING_SNAKE_CASE__   )
                   lowerCAmelCase__		= self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,	**SCREAMING_SNAKE_CASE__   )
                   lowerCAmelCase__		= [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]

                   lowerCAmelCase__		= tokenizer_r.batch_encode_candidates(SCREAMING_SNAKE_CASE__   ,	max_length=10   ,	return_tensors="np"   )
                   lowerCAmelCase__		= tokenizer_p.batch_encode_candidates(SCREAMING_SNAKE_CASE__   ,	max_length=10   ,	return_tensors="np"   )

                   lowerCAmelCase__		= (2, 2, 10)

                   self.assertEqual(encoded_sentence_r["input_ids"].shape   ,	SCREAMING_SNAKE_CASE__   )
                   self.assertEqual(encoded_sentence_r["attention_mask"].shape   ,	SCREAMING_SNAKE_CASE__   )
                   self.assertEqual(encoded_sentence_r["token_type_ids"].shape   ,	SCREAMING_SNAKE_CASE__   )

                   self.assertEqual(encoded_sentence_p["input_ids"].shape   ,	SCREAMING_SNAKE_CASE__   )
                   self.assertEqual(encoded_sentence_p["attention_mask"].shape   ,	SCREAMING_SNAKE_CASE__   )
                   self.assertEqual(encoded_sentence_p["token_type_ids"].shape   ,	SCREAMING_SNAKE_CASE__   )






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 							_A   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					List[str]   ,				lowerCAmelCase_     :					int			):
     """simple docstring"""

     lowerCAmelCase__		= FNetConfig.from_json_file(lowerCAmelCase_			)
     print(F'Building PyTorch model from configuration: {config}'			)
     lowerCAmelCase__		= FNetForPreTraining(lowerCAmelCase_			)

     lowerCAmelCase__		= restore_checkpoint(lowerCAmelCase_   ,				lowerCAmelCase_			)
     lowerCAmelCase__		= checkpoint_dict["target"]

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

     lowerCAmelCase__		= state_dict["fnet.embeddings.position_ids"]
     lowerCAmelCase__		= {"fnet.embeddings.position_ids": position_ids}
     # Embedding Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]			)

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

          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["intermediate"]["kernel"]			).T
          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["intermediate"]["bias"]			)

          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["output"]["kernel"]			).T
          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["output"]["bias"]			)

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

     # Pooler Layers
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]			)

     # Masked LM Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]			)
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]			)

     # Seq Relationship Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]			)
     lowerCAmelCase__		= 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								=     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)
