





import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     lowerCamelCase(	A__ ,   unittest.TestCase  ):
     '''simple docstring'''


     __magic_name__        = GPTNeoXJapaneseTokenizer
     __magic_name__        = False
     __magic_name__        = {'''do_clean_text''': False, '''add_prefix_space''': False}




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

         _A    =       [
             "こん",
             "こんに",
             "にちは",
             "ばんは",
             "世界,㔺界",
             "、",
             "。",
             "<BR>",
             "<SP>",
             "<TAB>",
             "<URL>",
             "<EMAIL>",
             "<TEL>",
             "<DATE>",
             "<PRICE>",
             "<BLOCK>",
             "<KIGOU>",
             "<U2000U2BFF>",
             "<|emoji1|>",
             "<unk>",
             "<|startoftext|>",
             "<|endoftext|>",
         ]
         _A    =       {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
         _A    =       {"unk_token": "<unk>"}

         _A    =       os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES['vocab_file']       )
         _A    =       os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES['emoji_file']       )
         with open(self.vocab_file							,    'w'							,    encoding='utf-8'       ) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )
         with open(self.emoji_file							,    'w'       ) as emoji_writer:
             emoji_writer.write(json.dumps(snake_case_       )       )




     def 					lowerCAmelCase__		(						self							,    **snake_case_       ):
         kwargs.update(self.special_tokens_map       )
         return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname							,    **snake_case_       )




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         _A    =       "こんにちは、世界。 \nこんばんは、㔺界。😀"
         _A    =       "こんにちは、世界。 \nこんばんは、世界。😀"
         return input_text, output_text




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         _A    =       self.get_input_output_texts(snake_case_       )
         _A    =       tokenizer.encode(snake_case_							,    add_special_tokens=snake_case_       )
         _A    =       tokenizer.decode(snake_case_							,    clean_up_tokenization_spaces=snake_case_       )
         return text, ids




     def 					lowerCAmelCase__		(						self       ):
         pass  # TODO add if relevant




     def 					lowerCAmelCase__		(						self       ):
         pass  # TODO add if relevant




     def 					lowerCAmelCase__		(						self       ):
         pass  # TODO add if relevant




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.get_tokenizer()

         # Testing tokenization
         _A    =       "こんにちは、世界。　こんばんは、㔺界。"
         _A    =       ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
         _A    =       tokenizer.tokenize(snake_case_       )
         self.assertListEqual(snake_case_							,    snake_case_       )

         # Testing conversion to ids without special tokens
         _A    =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
         _A    =       tokenizer.convert_tokens_to_ids(snake_case_       )
         self.assertListEqual(snake_case_							,    snake_case_       )

         # Testing conversion to ids with special tokens
         _A    =       tokens + [tokenizer.unk_token]
         _A    =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
         _A    =       tokenizer.convert_tokens_to_ids(snake_case_       )
         self.assertListEqual(snake_case_							,    snake_case_       )




     @slow
     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b'       )

         _A    =       tokenizer.encode('ありがとう。'							,    add_special_tokens=snake_case_       )
         _A    =       tokenizer.encode('どういたしまして。'							,    add_special_tokens=snake_case_       )

         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_       )
         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_							,    snake_case_       )

         assert encoded_sentence == ids_a
         assert encoded_pair == ids_a + ids_a




     def 					lowerCAmelCase__		(						self       ):
         pass




     def 					lowerCAmelCase__		(						self       ):
         pass












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)





