





import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
_A				:							Optional[int]							= """stas/tiny-wmt19-en-ru"""



class 				__snake_case  (     __SCREAMING_SNAKE_CASE ,    unittest.TestCase						):


    '''simple docstring'''







    lowerCamelCase__						:		Any							=  FSMTTokenizer
    lowerCamelCase__						:		int							=  False



    def    lowercase_				(					self    ):





        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE__       							= [
            '''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>''',
            '''<unk>''',
        ]
        SCREAMING_SNAKE_CASE__       							= dict(zip(A_      ,							range(len(A_    )    )    )    )
        SCREAMING_SNAKE_CASE__       							= ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        SCREAMING_SNAKE_CASE__       							= ['''en''', '''ru''']
        SCREAMING_SNAKE_CASE__       							= {
            '''langs''': self.langs,
            '''src_vocab_size''': 10,
            '''tgt_vocab_size''': 20,
        }

        SCREAMING_SNAKE_CASE__       							= os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''src_vocab_file''']    )
        SCREAMING_SNAKE_CASE__       							= os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''tgt_vocab_file''']    )
        SCREAMING_SNAKE_CASE__       							= os.path.join(self.tmpdirname      ,							'''tokenizer_config.json'''    )
        SCREAMING_SNAKE_CASE__       							= os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''merges_file''']    )
        with open(self.src_vocab_file      ,							'''w'''    ) as fp:
            fp.write(json.dumps(A_    )    )
        with open(self.tgt_vocab_file      ,							'''w'''    ) as fp:
            fp.write(json.dumps(A_    )    )
        with open(self.merges_file      ,							'''w'''    ) as fp:
            fp.write('''\n'''.join(A_    )    )
        with open(A_      ,							'''w'''    ) as fp:
            fp.write(json.dumps(A_    )    )



    @cached_property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''    )



    @cached_property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= FSMTTokenizer.from_pretrained(A_    )
        self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]      ,							['''en''', '''ru''']    )
        self.assertEqual(tokenizer.src_vocab_size      ,							21    )
        self.assertEqual(tokenizer.tgt_vocab_size      ,							21    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= FSMTTokenizer(self.langs      ,							self.src_vocab_file      ,							self.tgt_vocab_file      ,							self.merges_file    )

        SCREAMING_SNAKE_CASE__       							= '''lower'''
        SCREAMING_SNAKE_CASE__       							= ['''low''', '''er</w>''']
        SCREAMING_SNAKE_CASE__       							= tokenizer.tokenize(A_    )
        self.assertListEqual(A_      ,							A_    )

        SCREAMING_SNAKE_CASE__       							= tokens + ['''<unk>''']
        SCREAMING_SNAKE_CASE__       							= [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )      ,							A_    )



    @slow
    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.tokenizer_ru_en

        SCREAMING_SNAKE_CASE__       							= tokenizer.encode('''sequence builders'''      ,							add_special_tokens=A_    )
        SCREAMING_SNAKE_CASE__       							= tokenizer.encode('''multi-sequence build'''      ,							add_special_tokens=A_    )

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

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



    @slow
    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.tokenizer_en_ru
        SCREAMING_SNAKE_CASE__       							= self.tokenizer_ru_en

        SCREAMING_SNAKE_CASE__       							= [
            [
                '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
                [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
            ],
            ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
        ]

        # if data needs to be recreated or added, run:
        # import torch
        # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
        # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

        for src_text, tgt_input_ids in targets:
            SCREAMING_SNAKE_CASE__       							= tokenizer_enc.encode(A_      ,							return_tensors=A_    )
            self.assertListEqual(A_      ,							A_    )

            # and decode backward, using the reversed languages model
            SCREAMING_SNAKE_CASE__       							= tokenizer_dec.decode(A_      ,							skip_special_tokens=A_    )
            self.assertEqual(A_      ,							A_    )



    @slow
    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''      ,							do_lower_case=A_    )
        SCREAMING_SNAKE_CASE__       							= tokenizer.tokenize('''USA is United States of America'''    )
        SCREAMING_SNAKE_CASE__       							= ['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
        self.assertListEqual(A_      ,							A_    )



    @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''    )
    def    lowercase_				(					self    ):





        '''simple docstring'''

        pass




    @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''    )
    def    lowercase_				(					self    ):





        '''simple docstring'''

        pass







import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


_A				:							Any							= get_logger(__name__)


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    if text.startswith(lowerCAmelCase_  ):
        return text[len(lowerCAmelCase_  ) :]
    return text  # or whatever


def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    return {remove_prefix(lowerCAmelCase_   ,       '''model.'''  ): v for k, v in sd.items()}


def        __snake_case       (						lowerCAmelCase_  )							->	Optional[int]:
    SCREAMING_SNAKE_CASE__       							= {}
    for k in state_dicts[0].keys():
        SCREAMING_SNAKE_CASE__       							= [sd[k] for sd in state_dicts]
        SCREAMING_SNAKE_CASE__       							= sum(lowerCAmelCase_  ) / len(lowerCAmelCase_  )
        assert isinstance(lowerCAmelCase_   ,       torch.Tensor  )
        SCREAMING_SNAKE_CASE__       							= new_t
    return new_sd


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	None:
    SCREAMING_SNAKE_CASE__       							= AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_  )
    if os.path.isfile(lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= [pl_ckpt_path]
    else:
        assert os.path.isdir(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= list(Path(lowerCAmelCase_  ).glob('''*.ckpt'''  )  )
        assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

    if len(lowerCAmelCase_  ) > 1:
        logger.info(f'''averaging the weights of {ckpt_files}'''  )

    SCREAMING_SNAKE_CASE__       							= [sanitize(torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']  ) for x in ckpt_files]
    SCREAMING_SNAKE_CASE__       							= average_state_dicts(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= hf_model.load_state_dict(lowerCAmelCase_   ,       strict=lowerCAmelCase_  )
    assert not missing, f'''missing keys: {missing}'''
    hf_model.save_pretrained(lowerCAmelCase_  )
    try:
        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(lowerCAmelCase_  )
        tok.save_pretrained(lowerCAmelCase_  )
    except Exception:
        pass
        # dont copy tokenizer if cant


if __name__ == "__main__":
  fire.Fire(convert_pl_to_hf)
