





import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin







class     lowerCamelCase(	__snake_case ,   unittest.TestCase  ):
     '''simple docstring'''


     __magic_name__        = SpeechaTextaTokenizer
     __magic_name__        = False




     def 					lowerCAmelCase__		(						self       ):
         super().setUp()

         _A    =       '<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '       )
         _A    =       ['he re</w> 123', 'here a 1456']
         _A    =       dict(zip(snake_case_							,    range(len(snake_case_       )       )       )       )

         _A    =       {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

         _A    =       tempfile.mkdtemp()
         _A    =       os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES['vocab_file']       )
         _A    =       os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES['merges_file']       )

         with open(self.vocab_file							,    'w'							,    encoding='utf-8'       ) as fp:
             fp.write(json.dumps(snake_case_       ) + '\n'       )

         with open(self.merges_file							,    'w'       ) as fp:
             fp.write('\n'.join(snake_case_       )       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       list(self.get_tokenizer().get_vocab().keys()       )

         self.assertEqual(vocab_keys[0]							,    '<s>'       )
         self.assertEqual(vocab_keys[1]							,    '<pad>'       )
         self.assertEqual(vocab_keys[-1]							,    'vocab'       )
         self.assertEqual(len(snake_case_       )							,    14       )




     def 					lowerCAmelCase__		(						self       ):
         self.assertEqual(self.get_tokenizer().vocab_size							,    14       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       SpeechaTextaTokenizer.from_pretrained(self.tmpdirname       )

         # make sure @@ is correctly concatenated
         _A    =       [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
         _A    =       tokenizer.decode(snake_case_       )

         self.assertTrue(output_string == 'herecouple words ofthe'       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       SpeechaTextaTokenizer.from_pretrained(self.tmpdirname       )

         with tempfile.TemporaryDirectory() as tmp_dirname:
             tokenizer.save_pretrained(snake_case_       )
             os.remove(os.path.join(snake_case_							,    'merges.txt'       )       )

             # load tokenizer without merges file should not throw an error
             _A    =       SpeechaTextaTokenizer.from_pretrained(snake_case_       )

         with tempfile.TemporaryDirectory() as tmp_dirname:
             # save tokenizer and load again
             tokenizer.save_pretrained(snake_case_       )
             _A    =       SpeechaTextaTokenizer.from_pretrained(snake_case_       )

         self.assertIsNotNone(snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         if not self.test_slow_tokenizer:
             return

         _A    =       inspect.signature(self.tokenizer_class.__init__       )
         _A    =       self.get_tokenizer()

         for parameter_name, parameter in signature.parameters.items():
             if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                 self.assertIn(snake_case_							,    tokenizer.init_kwargs       )












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 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[str]:




    """simple docstring"""




    _A    =       FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE							)
    print(F"Building PyTorch model from configuration: {config}"							)
    _A    =       FNetForPreTraining(_SCREAMING_SNAKE_CASE							)

    _A    =       restore_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    _A    =       checkpoint_dict['target']

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

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

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

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

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

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

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

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

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

    # Load State Dict
    fnet_pretraining_model.load_state_dict(_SCREAMING_SNAKE_CASE							)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"							)
    fnet_pretraining_model.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
 __A       : str						    = 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.")
 __A       : Any						    = parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





