'''simple docstring'''

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
_lowerCamelCase :				Optional[Any]														= "stas/tiny-wmt19-en-ru"




class    lowerCamelCase__  (   _SCREAMING_SNAKE_CASE       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						FSMTTokenizer
   __UpperCAmelCase           =						False



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        _UpperCamelCase  :int       =[
            """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>""",
        ]
        _UpperCamelCase  :str       =dict(zip(lowerCAmelCase__					,      range(len(lowerCAmelCase__     )     )     )     )
        _UpperCamelCase  :List[Any]       =["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

        _UpperCamelCase  :List[str]       =["""en""", """ru"""]
        _UpperCamelCase  :List[str]       ={
            """langs""": self.langs,
            """src_vocab_size""": 10,
            """tgt_vocab_size""": 20,
        }

        _UpperCamelCase  :Dict       =os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["""src_vocab_file"""]     )
        _UpperCamelCase  :List[str]       =os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["""tgt_vocab_file"""]     )
        _UpperCamelCase  :List[str]       =os.path.join(self.tmpdirname					,      """tokenizer_config.json"""     )
        _UpperCamelCase  :int       =os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["""merges_file"""]     )
        with open(self.src_vocab_file					,      """w"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     )     )
        with open(self.tgt_vocab_file					,      """w"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     )     )
        with open(self.merges_file					,      """w"""     ) as fp:
             fp.write("""\n""".join(lowerCAmelCase__     )     )
        with open(lowerCAmelCase__					,      """w"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     )     )



   @cached_property
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





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



   @cached_property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





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



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =FSMTTokenizer.from_pretrained(lowerCAmelCase__     )
        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  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =FSMTTokenizer(self.langs					,      self.src_vocab_file					,      self.tgt_vocab_file					,      self.merges_file     )

        _UpperCamelCase  :Union[str, Any]       ="""lower"""
        _UpperCamelCase  :Any       =["""low""", """er</w>"""]
        _UpperCamelCase  :Dict       =tokenizer.tokenize(lowerCAmelCase__     )
        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )

        _UpperCamelCase  :str       =tokens + ["""<unk>"""]
        _UpperCamelCase  :Dict       =[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__     )					,      lowerCAmelCase__     )



   @slow
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =self.tokenizer_ru_en

        _UpperCamelCase  :Optional[int]       =tokenizer.encode("""sequence builders"""					,      add_special_tokens=lowerCAmelCase__     )
        _UpperCamelCase  :Any       =tokenizer.encode("""multi-sequence build"""					,      add_special_tokens=lowerCAmelCase__     )

        _UpperCamelCase  :int       =tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__     )
        _UpperCamelCase  :List[Any]       =tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__					,      lowerCAmelCase__     )

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



   @slow
   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :str       =self.tokenizer_en_ru
        _UpperCamelCase  :Any       =self.tokenizer_ru_en

        _UpperCamelCase  :List[Any]       =[
            [
                """Here\'s a little song I wrote. Don\'t worry, be happy.""",
                [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
            ],
            ["""This is it. No more. I\'m done!""", [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 427, 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:
             _UpperCamelCase  :Any       =tokenizer_enc.encode(lowerCAmelCase__					,      return_tensors=lowerCAmelCase__     )
             self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )

             # and decode backward, using the reversed languages model
             _UpperCamelCase  :List[Any]       =tokenizer_dec.decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )
             self.assertEqual(lowerCAmelCase__					,      lowerCAmelCase__     )



   @slow
   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Any       =FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""					,      do_lower_case=lowerCAmelCase__     )
        _UpperCamelCase  :List[Any]       =tokenizer.tokenize("""USA is United States of America"""     )
        _UpperCamelCase  :Optional[Any]       =["""us""", """a</w>""", """is</w>""", """un""", """i""", """ted</w>""", """st""", """ates</w>""", """of</w>""", """am""", """er""", """ica</w>"""]
        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )



   @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""     )
   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        pass




   @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""     )
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        pass



'''simple docstring'''






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline

